BaseSpace User Guide
Supporting the NextSeq, Miseq, and HiSeq Sequencing Systems
FOR RESEARCH USE ONLY Introduction
How Do I Start
BaseSpace User Interface
How To Use BaseSpace
Workflow Reference
Data Reference
Technical Assistance
ILLUMINA PROPRIETARY
Part # 15050652 Rev. A
January 2014
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The instructions in this document must be strictly and explicitly followed by qualified and properly trained personnel in order
to ensure the proper and safe use of the product(s) described herein. All of the contents of this document must be fully read
and understood prior to using such product(s).
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© 2011-2014 Illumina, Inc. All rights reserved.
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BaseSpace is a genomics analysis platform that is directly integrated into the NextSeq,
MiSeq and HiSeq sequencing platforms. When setting up runs on your sequencing
instrument, you have the option to send the run to BaseSpace. This will send the basecall (*.bcl) files, as well as associated files, to your dedicated space on the cloud, as well
as associated files. On the HiSeq, you can also choose to do Run Monitoring Only, which
only sends the files needed for remote monitoring of the run to BaseSpace.
NOTE
This user guide supports data analysis for the NextSeq, Miseq, and HiSeq sequencing
systems, and contains information about the Prep tab, which is used to set up a NextSeq
sequencing run.
This user guide is specific for BaseSpace running in the cloud, and is not intended for the
on-premise implementation, BaseSpace OnSite.
The instrument seamlessly pushes the data to BaseSpace for automatic analysis and
storage, with the option of retaining data for local analysis and hosting. There is no need
for a manual and time-consuming data-transfer step: the data is already up in the cloud,
for you and your collaborators to access anywhere, anytime.
BaseSpace can automatically run analysis jobs using the Illumina MiSeq workflow apps.
BaseSpace also allows you to use the third-party apps to analyze your data. In addition,
BaseSpace provides a mechanism to share data with others and easily scale storage and
computing needs.
For more information about BaseSpace, see the BaseSpace Data Sheet.
Workflow Model
Prep Run on NextSeq
BaseSpace enables you to prep runs for NextSeq sequencing.
The prep workflow in BaseSpaceconsists of four steps:
} Biological Samples: Define the samples that are going to be sequenced.
} Libraries: Define the libraries, which consist of biological samples that are prepped
and contain adapters. Each library usually derives from a single biological sample,
though biological samples can be used in multiple libraries.
} Pools: Group libraries into pools that share analysis parameters. Pools can consist of
one or multiple libraries.
} Planned Runs: Define run parameters for pool, then send planned run to the
NextSeq.
You can now start the run from the instrument.
BaseSpace User Guide for NextSeq, Miseq, and HiSeq
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Introduction
Introduction
Figure 1 Prep Workflow
Data Processing
Processing a flow cell on a sequencing instrument produces a variety of files, collectively
referred to as a run. A run contains log files, instrument health data, run metrics, sample
sheet, and base call information (*.bcl files) that is demultiplexed in BaseSpace to create
the samples used in secondary analysis.
Samples are analyzed automatically using the Illumina workflow apps as specified in
the sample sheet, or by launching custom BaseSpace apps. BaseSpace apps, many of
which are written by third-party vendors, are processing software and routines that
interact with BaseSpace data through the API. User-level authentication and in-flight
data encryption are enforced for every app that requests access to BaseSpace data.
AppResults then store the files that are generated by Illumina workflow apps or
BaseSpace apps. For example, when a resequencing app executes alignment and variant
calling, an AppResult is created for each sample. An AppResult generally contains BAM
and VCF files, but it can also contain other file types. AppResults can also be used as
inputs to apps. App sessions are created to record every time an app is launched.
Finally, projects are simple containers that store samples and AppResults.
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Introduction
Figure 2 BaseSpace Data Model
BaseSpace Security Model
Data security is a key concern in making the decision to move to cloud-based genomic
storage and analysis. Illumina's BaseSpace is hosted on Amazon’s Web Services (AWS)
and provides a combination of Amazon’s comprehensive and well-tested approach to
platform security, overlaid with Illumina’s own security testing and procedures, which
includes reviews and tests by independent security professionals. This provides a cloud
genomics solution that meets or exceeds the security provided by many institutional IT
infrastructures.
Amazon’s Web Services
Illumina has chosen to work with AWS as the leader in cloud-based infrastructure,
hosting customer-facing services and critical operations for both private industry and
U.S. government departments including Treasury, DOE, and State. Amazon’s own
security processes and standards are publicly available for review. AWS standards and
accreditation include:
BaseSpace User Guide for NextSeq, Miseq, and HiSeq
5
} SOC 1/SSAE 16/ISAE 3402 (auditing)
} FISMA moderate (U.S. Federal Government; for reference, the NIH’s own data centers
are rated FISMA moderate)
} PCI DSS Level 1 (electronic payments)
} ISO 27001 (international security standard)
} FIPS 140-2 (encryption)
Additionally, AWS data centers are protected by security staff and controlled access
procedures. Staff with system access undergo background checks, and all hardware is
located behind firewalls which are configured by default to block all traffic. Operating
security patches are automatically applied to AWS servers, including BaseSpace servers.
AWS actively monitors its firewalls to check for vulnerabilities, a service beyond the
resources of most institutions. BaseSpace encrypts all data, something else that is rarely
done in the institutional IT setting.
BaseSpace Data Stream Software
Illumina sequencing instruments have on-board control and workflow software. This
includes a robust data-streaming component which acts as a software broker with the
BaseSpace API, allowing individual base-call (*.bcl) files to be sent over an encrypted
connection, verified, and assembled into samples for analysis in real time as the
sequencing run is conducted. Real-time monitoring of data generated by one sequencing
instrument or a federation of instruments is possible through the BaseSpace interface.
The instrument control software does not allow publicly addressable inbound
communications. All communication is made through standard https requests initiated
by the user at the instrument. Each data-upload transaction is linked to an authenticated
user account.
BaseSpace Apps
There are two different types of apps in BaseSpace:
} Sample Sheet Driven Workflow Apps (for MiSeq only): launching these Illumina
workflow apps is specified in the sample sheet, and they are started automatically
by BaseSpace. The sample sheet driven workflow apps primarily perform secondary
analysis or simple file manipulations. They consist of the following:
• Resequencing
• Amplicon Analysis
• Library QC
• SmallRNA
• Metagenomics
• De Novo Assembly
• Generate FASTQ
See the Workflow Reference on page 55 for descriptions of the workflow apps.
} Custom BaseSpace apps.: these apps need to be actively launched to analyze data.
In general, these apps perform tertiary analysis, visualization, or annotation of data.
Most of them are generated by third-party vendors, although Illumina has generated
a few too. There may be additional costs associated with running a BaseSpace app
from a third-party vendor. BaseSpace apps may require the AppResults from an
Illumina workflow app as input.
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Sequencing data has traditionally been stored in non-centralized locations, which offer
little uniformity of data management across locations. BaseSpace transforms data
management by creating an environment with large stores of sequencing data, which
can be easily accessed and analyzed online with a store of applications. Third-party
vendors can develop their own apps for BaseSpace. BaseSpace offers the following
benefits as a development platform:
} Easy access to data: sequencing data is automatically uploaded from instrument to
BaseSpace
} Consistent data retrieval: with a few lines of code, access data with the BaseSpace
API and SDKs
} Write once, execute often: once an app is written and published, all users can launch
it
} Flexible billing: apps can bill customers as little or as much as they wish
} Highly scalable: data storage and analysis scales since BaseSpace is built on
Amazon Web Services (AWS)
} Flexible app hosting: apps can be hosted on any website, desktop or mobile
application, or inside BaseSpace as a Native App.
} Easy sharing: users can easily share their data and results
Developers can create apps using the BaseSpace Application Programming Interface
(API) or the Software Development Kits (SDKs) available for Java, R, Ruby, and Python.
Both approaches offer safe and easy access to BaseSpace data for Apps to analyse,
visualize, monitor, etc.
Apps access data using the BaseSpace RESTful API. The API may be accessed via simple
HTTPS requests using any programming language and is organized to allow you to get
to the data you need quickly.
The SDKs are available for several programming languages and make it even easier for
developers to write applications or to integrate existing ones. The SDKs work by
exposing what the API has to offer natively without the developer needing to worry
about building their own HTTPS requests. This allows rapid development and
integration with BaseSpace data and a simple mechanism of discovering what the API
has to offer.
For more information, see the BaseSpace API documentation.
BaseSpace User Guide for NextSeq, Miseq, and HiSeq
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Introduction
BaseSpace API
How Do I Start
You can reach BaseSpace from https://basespace.illumina.com/home/index. In this
section we discuss the different ways to get started with BaseSpace.
Use your MyIllumina account to log on; the first time you visit you will be asked to
accept the BaseSpace agreement. After that, you are ready to run BaseSpace.
Exploring BaseSpace on page 8
Not Uploading Yet on page 8
Using BaseSpace with MiSeq on page 9
Using BaseSpace with HiSeq on page 10
Using BaseSpace with NextSeq on page 11
Getting Shared Data on page 12
Exploring BaseSpace
If you just want to explore BaseSpace, go to https://basespace.illumina.com/home/index
and click the Get Started link. You will need to register to set up a new account. Fill out
the form, and indicate whether you want access to product and support resources.
We will send you a confirmation email to the email account you entered. Open that
email, and click on the confirmation link. Now you are ready to start testing BaseSpace
with the test data we uploaded for you. For information about how to run certain tasks,
see How To Use BaseSpace on page 25
Once you feel comfortable with BaseSpace, and you have a BaseSpace equipped HiSeq or
MiSeq, you can start uploading data and run analyses from your sequencing instrument.
Raw data from the run is also stored on the instrument, or in the location of the output
folder that you specified in Run Options.
Not Uploading Yet
If you have a sequencing instrument but you are not uploading data yet, you can start by
exploring BaseSpace. Go to basespace.illumina.com and log on; there are two ways to do
that:
} Use your MyIllumina account to log on; the first time you visit you will be asked to
accept the BaseSpace agreement. After that, you are ready to run BaseSpace.
} Explore BaseSpace by taking a test drive; you must register to set up a new account.
See Exploring BaseSpace on page 8.
Now you are ready to start testing BaseSpace with the test data we uploaded for you. For
information about how to run tasks, see How To Use BaseSpace on page 25
Once you feel comfortable with BaseSpace, you can start uploading data and run
analyses from your sequencing instrument. Raw data from the run is also stored on the
instrument, or in the location of the output folder that you specified in Run Options.
Alternatively, you can elect to only upload health data to BaseSpace. Health data helps
Illumina improving the sequencing instruments and BaseSpace; for more information,
see Health Runs on page 87.
See the MiSeq System User Guide, NextSeq System User Guide, or HiSeq User Guide for
instructions for setting up your sequencing instrument.
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Using BaseSpace with MiSeq
BaseSpace is Illumina's analysis cloud environment. Using BaseSpace to store and
analyze your run data provides the following benefits:
} Eliminates the need for onsite storage and computing
} Enables web-based data management and analysis
} Provides tools for global collaboration and sharing
In this section we discuss the different ways to get started with BaseSpace when
uploading data and analysis from the MiSeq.
You can reach BaseSpace by going to basespace.illumina.com. Use your MyIllumina
account to log on; the first time you visit you will be asked to accept the BaseSpace
agreement. After that, you are ready to run BaseSpace.
When you set up the run on the MiSeq, you should select the option to log in to
BaseSpace. If you have a problem with the data upload between MiSeq and BaseSpace,
see MiSeq Connection on page 9
NOTE
Raw data from the run is also stored on the instrument, or in the location of the output
folder that you specified in Run Options.
BaseSpace automatically disconnects from the MiSeq at the end of the run or as soon as
all primary analysis files have finished uploading. If the internet connection is
interrupted, analysis files will continue uploading after the connection is restored from
the point when the interruption occurred.
As soon as the last base call file is uploaded to BaseSpace, secondary analysis of your
data begins. The same analysis workflows are supported on BaseSpace as with oninstrument analysis using MiSeq Reporter. For information about how to run tasks, see
How To Use BaseSpace on page 25
MiSeq Connection
If the MiSeq data is not uploaded to BaseSpace, check the following things.
1
Make sure you have an internet connection from the MiSeq.
2
When setting up runs on the MiSeq, you have the option to log in to BaseSpace,
and use BaseSpace for storage and analysis. Make sure that option is checked.
When you begin your sequencing run on the MiSeq, the BaseSpace icon changes to
indicate that the MiSeq is connected to BaseSpace and data files are being transferred to
your secure location.
Figure 3 Connected to BaseSpace Icon on the MiSeq
For more information, see the MiSeq System User Guide.
BaseSpace User Guide for NextSeq, Miseq, and HiSeq
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How Do I Start
Once your sequencing instrument is uploading data, go to Using BaseSpace with MiSeq on
page 9, Using BaseSpace with NextSeq on page 11, or Using BaseSpace with HiSeq on page
10 to get started with the analysis of your run.
Using BaseSpace with HiSeq
BaseSpace Connectivity—The HiSeq features an option to send instrument health and
sequencing data to BaseSpace in real time to streamline both instrument quality control
and analysis. Real-time monitoring of runs enables fast troubleshooting. BaseSpace
facilitates collaboration by enabling you to share results instantly with anyone anywhere
in the world. Free alignment and variant calling and the soon to be launched BaseSpace
app store provide many easy to use workflows that tailor analysis for diverse biological
applications.
BaseSpace is Illumina's analysis cloud environment. Using BaseSpace to store and
analyze your run data provides the following benefits:
} Eliminates the need for onsite storage and computing
} Enables web-based data management and analysis
} Provides tools for global collaboration and sharing
In this section we discuss the different ways to get started with BaseSpace when
uploading data and analysis from the HiSeq.
You can reach BaseSpace by going to basespace.illumina.com. Use your MyIllumina
account to log on; the first time you visit you will be asked to accept the BaseSpace
agreement. After that, you are ready to run BaseSpace.
When you set up the run on the HiSeq, you should select the option to log in to
BaseSpace. If you have a problem with the data upload between HiSeq and BaseSpace,
see HiSeq Connection on page 10
NOTE
Raw data from the run is also stored on the instrument, or in the location of the output
folder that you specified in the Storage screen.
BaseSpace automatically disconnects from the HiSeq at the end of the run or as soon as
all primary analysis files have finished uploading. If the internet connection is
interrupted, analysis files will continue uploading after the connection is restored from
the point when the interruption occurred.
As soon as the last base-call file is uploaded to BaseSpace, secondary analysis of your
data begins. For information about how to run tasks, see How To Use BaseSpace on page
25
HiSeq Connection
If the HiSeq data is not uploaded to BaseSpace, check the following things.
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1
Make sure you have a stable internet connection of at least 10 Mbps from the HiSeq.
2
The Storage screen during run configuration on the HiSeq enables you to define
where your run data will be output and stored. Select the options below:
• Connect to BaseSpace—When you select this option you will be prompted to
enter your MyIllumina account information. Zip BCL files (below) will be selected
by default. Illumina recommends that you also save files locally. To save files
locally, select Save to an output folder and enter a path, usually to a local
network folder.
• Storage and analysis—This option enables the HiSeq to send run data as well as
system health information to BaseSpace.
Part # 15050652 Rev. A
How Do I Start
Figure 4 Storage Screen
3
If BaseSpace is not available, open Windows Services and start or restart Illumina
BaseSpace Broker:
a Click the Windows Start button.
b Right-click on Computer, select Manage.
c On the left, under Services and Applications, choose Services.
d Scroll down the list to find Illumina Basespace Broker.
e Right click on Illumina Basespace Broker and do one of the following:
— Click Start if this option is not greyed out
— If the Start option is greyed out, click Restart
The service will start, or will close then restart.
f Close the Computer Management window.
NOTE
To use BaseSpace, you must load a sample sheet at the start of your run.
For more information, see the HiSeq User Guide
When you begin your sequencing run on the HiSeq, the BaseSpace icon changes to
indicate that the HiSeq is connected to BaseSpace and data files are being transferred to
your secure location.
Using BaseSpace with NextSeq
BaseSpace is Illumina's analysis cloud environment. BaseSpace facilitates your
experiments on the NextSeq sytem in two different ways:
} BaseSpace helps to organize your samples and experiments, and preps runs for
NextSeq.
} BaseSpace stores and analyzes your run data, providing the following benefits:
• Eliminates the need for onsite storage and computing
• Enables web-based data management and analysis
• Provides tools for global collaboration and sharing
If your NextSeq sequencing system and BaseSpace do not connect properly, check the
following:
} Make sure you have a stable internet connection of at least 10 Mbps from the
NextSeq.
} From the Manage Instrument screen, select System Configuration to access a series
of screens that configure the connection to BaseSpace.
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} Log in to BaseSpace when setting up the run on the NextSeq sequencing system.
Getting Shared Data
If you receive a link to shared data in BaseSpace, click on the link. Use your MyIllumina
account to log on; the first time you visit you will be asked to accept the BaseSpace
agreement. After that, you are ready to run BaseSpace. You may need to set up a new
account. Fill out the form, and indicate whether you want access to product and support
resources.
Alternatively, log on to your BaseSpace account. If someone shared data with you, you
should see a notification stating so.
The shared data will show up in your project list. Now you can use the BaseSpace tools
to look at and download the data. For information about how to run tasks, see How To
Use BaseSpace on page 25
NOTE
The sharing feature can at any time be disabled by the owner of the data.
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The BaseSpace user interface (UI) has four tabs that allow you to access and use your
data. In addition, there are a number of common interface elements that enable general
tasks. This section describes the various aspects of the BaseSpace UI.
Common Elements on page 13
Dashboard Tab on page 15
Prep Tab on page 17
Runs Tab on page 20
Projects Tab on page 21
Apps Tab on page 24
Public Data Tab on page 24
Common Elements
There are a number of common UI elements that are shared between all BaseSpace
pages:
} Toolbar
} Contact us button
} Bottom links
These elements enable general tasks, and are explained below.
Toolbar
The BaseSpace toolbar elements are listed in the table below.
Icon
Element
Description
Dashboard See Dashboard Tab on page 15
Tab
Runs Tab
See Runs Tab on page 20.
Projects
Tab
See Projects Tab on page 21.
Prep Tab
See Prep Tab on page 17. This tab is used to set up a NextSeq
run.
Apps Tab
See Apps Tab on page 24.
Public
Data Tab
See Public Data Tab on page 24.
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BaseSpace User Interface
BaseSpace User Interface
Icon
Element
Support
Page
Search
Account
Description
The BaseSpace Support page provides access to the BaseSpace
Knowledge Base, User Guide, and Illumina Technical Support.
The Search box allows you to find runs, projects, or samples.
For more information, see Search for Runs, Projects, and Samples
on page 53.
The Account dropdown list provides access to:
• iCredits. See Access Your Wallet on page 51.
• MyAccount. See MyAccount on page 15.
• MyIllumina Dashboard.
• FAQ: leads to a number of frequently asked questions and
illumina-provided answers.
• Terms: leads to the User Agreement.
• Blog: leads to the blog. Check this out for the latest news,
developments, and updates.
• Sign out.
Contact Us Button
The Contact us button opens a new screen that allows you to:
• Browse the knowledge base
• Provide feedback for Illumina, suggest ideas to the user community, or browse, read,
and vote for other people's ideas.
• Contact Support
Figure 5 Knowledge base, feedback, and contact screen.
Bottom Links
The bottom links provide access to more information:
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MyAccount
MyAccount provides access to the Settings, Wallet, Purchase History, Transfer History,
and Genomes pages.
Settings
On the Settings page you can edit your notifications settings, edit your profile, or update
your profile picture.
Wallet
The Wallet page allows you to manage iCredits and credit cards. See Access Your Wallet
on page 51 for more information.
Purchase History
The Purchase History page contains detailed information about purchases, adjustments,
and balance for your account. See View Purchase History on page 53 for more information.
Transfer History
The Transfer History page allows you to review projects or runs that have been
transferred. See Transfer Ownership on page 51 for more information.
Genomes
The Genomes page lists the genomes that are associated with your BaseSpace account.
Dashboard Tab
After login, the first tab you see is the dashboard. The dashboard provides
access to notifications, your latest runs, projects, and app results. The
dashboard is always accessible in BaseSpace from the top ribbon selector.
NOTE
If a run or project is not showing on BaseSpace, your data may not have been
sent to BaseSpace. You need to set the BaseSpace option on your sequencing
instrument; see the instrument's user guide.
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15
BaseSpace User Interface
} Help: online help.
} FAQ: leads to a number of frequently asked questions and illumina-provided
answers.
} Developers: leads to the developers portal, set up to help you generate custom apps.
} Terms: leads to the User Agreement.
} Blog: leads to the blog (blog.basespace.illumina.com). Check this out for the latest
news, updates, and developments, and subscribe to updates.
Notifications
Notifications are displayed here by most recent first. There are multiple types of
notifications:
} Runs
• Run in progress
• Run completed
• Run error
} Collaborators
• Collaborator joined a project/run of which you're a member
• Collaborator invited you to a project/run
• (optionally) collaborator may have included a personal message
• Collaborator recommended an App
• Collaborator accepted your offer to transfer ownership
• Collaborator offered to transfer ownership to you.
} Analyses by you
• Analysis in progress
• Analysis completed
• Analysis error
} Analyses by collaborators
• Analysis in progress
• Analysis completed
• Analysis error
} Uploads, additions, or deletions to/from a project of which you're a member
• By you
• By a collaborator
} Messages from Illumina
• New Demo data set
• Announcement of a new feature
Runs Pane
The bottom left pane of the BaseSpace dashboard shows the three most recent runs,
which is updated automatically.
Clicking on the Runs pane opens the Runs tab. Clicking on a run opens the Runs tab
with the run loaded. For more information, see Runs Tab on page 20.
Projects Pane
The bottom middle pane of the BaseSpace dashboard shows the three most recent
projects. The folder icon indicates the sharing status of the project: if it displays several
people
, the project is shared.
Clicking on the Projects pane opens the Projects tab. Clicking on a project opens the
Projects tab with the project loaded. For more information, see Projects Tab on page 21.
App Results Pane
The right bottom pane of the BaseSpace dashboard shows the most recent app results.
Clicking on an app result provides charts relevant for the app used in the Projects tab.
For more information, see App Results Page on page 23.
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The Prep tab enables you to set up a sequencing run on the NextSeq sequencing
system.
This tab is currently only available for NextSeq sequencing systems. Other
sequencing instruments use a sample sheet to provide sample information to BaseSpace.
The Prep Tab sets up a run in four steps:
} Biological Samples: Contains information about the samples that are going to be
sequenced. See Biological Samples on page 17
} Libraries: Consists of biological samples that are prepped and contain adapters.
Each library usually derives from a single biological sample, though biological
samples can be used in multiple libraries. See Libraries on page 18.
} Pools: Consists of groups of libraries that share analysis parameters. Pools can
consist of one or multiple libraries. See Pools on page 18.
} Planned Runs: Contains pools that run with the same analysis parameters, on the
same machine, at the same time. Planned runs can consist of one or multiple pools.
See Planned Runs on page 19.
Biological Samples
When you click on the Biological Samples tab you see the Biological Samples list, which
shows all available samples you have created on your account.
Figure 6 Biological Samples List
If you want information about the samples, you can perform the following:
} Sort the list by clicking on the column headers.
} Click on a sample to got to the sample page.
This page provides the following actions to prepare your analysis:
} Create a new sample.
} Import new samples.
} Select a sample and edit its properties.
} Select one or more samples and continue with Prep Libraries.
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BaseSpace User Interface
Prep Tab
NOTE
You can select multiple samples by using one of the following methods:
• Select multiple checkboxes.
• Click anywhere on a sample row while holding Ctrl button to add to a selection.
• Click anywhere on a sample row while holding Shift button to select all samples in
between.
• Click the checkbox next to Plate/Tube ID to select all samples on the current page.
The box next to the Biological Samples header keeps track of the total number of
samples, and how many are selected. Click X next to the selection count to clear the
current selection.
For more information about these actions, see Create New Biological Samples on page 36,
Import Biological Samples on page 37, and Use Existing Biological Samples on page 38.
Libraries
When you click on the Libraries tab you see the Libraries list, which shows all available
plates or tubes with libraries you have created on your account. You can sort the list by
clicking on the column headers, or click on a plate to see its properties and associated
libraries.
Figure 7 Libraries List
This page provides the following actions to prepare your analysis:
} Click on a plate, then click the Edit button to edit its properties or libraries.
} Select one or more plates or tubes and move to Pool Libraries.
NOTE
If you want to select multiple libraries:
• Select multiple checkboxes.
• Click anywhere on a library row while holding Ctrl button to add to a selection.
• Click anywhere on a library row while holding Shift button to select all libraries in
between.
• Click the checkbox next to Plate/Tube ID to select all samples on the current page.
The box next to the Libraries header keeps track of the total number of libraries, and how
many are selected. Click X next to the selection count to clear the current selection.
For more information about these actions, see Prep Libraries on page 38.
Pools
When you click on the Pools tab you see the Pools list, which shows all available pools
of libraries you have created on your account. You can sort the list by clicking on the
column headers, or click on a pool to see its properties and associated libraries.
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BaseSpace User Interface
Figure 8 Pools List
This page provides the following actions to prepare your analysis:
} Click on a pool, then click the Edit button to edit the notes.
} Select a pool and move to Plan Run.
NOTE
You can also merge pools the following way:
• Click the Save & Continue Later. This will take you to the Pools list, with the
recently created plate at the top of the list.
• Select the checkboxes in the Pools list.
• Click the Merge Pools button in the top navbar.
The box next to the Pools header keeps track of the total number of pools, and how many
are selected.
For more information about these actions, see Pool Libraries on page 40.
Planned Runs
When you click on the Planned Runs tab you see the Planned Runs list, which shows all
planned runs you have created on your account.
Figure 9 Planned Runs List
You can sort the list by clicking on the column headers, or click on a run to see or edit its
properties. For more information about these actions, see Plan Runs on page 41.
The runs can have the following states:
} Ready to Sequence: the run can be started from the NextSeq sequencing system.
} Planning, the run will not show up on the NextSeq sequencing system since it is still
in the planning stage.
NOTE
If you want to select multiple runs:
• Select multiple checkboxes.
• Click anywhere on a planned run row while holding Ctrl button to add to a selection.
• Click anywhere on a planned run row while holding Shift button to select all runs in
between.
• Click the checkbox next Experiment Name to select all planned runs on the current
page.
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19
The box next to the Planned Runs header keeps track of the total number of runs, and
how many are selected. Click X next to the selection count to clear the current selection.
When sequencing on a run starts, the run is removed automatically from the Planned
Runs list.
Runs Tab
The Runs button leads to the runs list, which allows you to sort your runs based
on experiment name, state, workflow, created date, machine, and owner.
The following run states are possible (blue boxes indicate final states.):
If you want to look at a run in detail, click on the name to view metrics in more detail.
For more information, see Run Overview Page on page 20.
When you click on the gear wheel next to the run name, you will see options for sharing,
transferring, and downloading a run. For more information, see Share Data on page 45,
Transfer Ownership on page 51, or Download Files on page 43.
Run Overview Page
The Run Overview page provides 5 panes:
} The Run Details pane gives a summary of the run with links to view files and
download and share options. For more information, see Share Data on page 45, View
Files and Results on page 25, or Download Files on page 43.
} The Samples pane gives a list of all the app results in the run, the associated
projects and the number of samples in that analysis. This pane provides access to
the following pages:
• Samples list, see Run Samples List on page 21
• Sample Details page, see Sample Overview Page on page 23
• App Results page, see App Results Page on page 23
• Project Overview page, see Project Overview Page on page 22
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Part # 15050652 Rev. A
In addition, there is a Side Navigation ribbon, which provides easy navigation in the
Run Details area. It contains links to the Overview, Run Samples List, Charts, Run
Summary, Indexing QC, Sample Sheet or Run Settings, and Files pages.
Run Samples List
The samples list allows you to sort the samples in your run based on sample ID, app,
date created, and project. If you want to look at a sample, app result, or project in detail,
click on the links to get to the following pages:
} Sample Overview Page on page 23.
} App Results Page on page 23.
} Project Overview Page on page 22.
In addition, there is a Side Navigation ribbon, which provides easy navigation in the
Run Details area. It contains links to the Overview, Run Samples List, Charts, Run
Summary, Indexing QC, Sample Sheet or Run Settings, and Files pages.
Projects Tab
The Projects button opens a list of your projects. You can sort the list by name, last
update, or owner. Clicking on a project provides access to the app results and
samples within that project.
You generate a new project by clicking New Project button on top of the list.
When you hover over a project that you own, you see the Settings wheel.
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21
BaseSpace User Interface
} The Charts pane displays an intensity by cycle chart. Clicking on the header will
take you to the Charts page, which contains five charts with run metrics. See Charts
on page 69.
} The Run Summary pane displays tables with basic data quality metrics. Clicking on
the header will take you to the Run Summary page. See Run Summary on page 66.
} The Indexing QC pane lists count information for indices used in the run. Clicking
on the header will take you to the Indexing QC page. See Indexing QC on page 68
The Settings wheel provides the following options for sharing a project and editing the
project details:
} Edit project: edit the name and description of the project. See also Edit Project Details
on page 49.
} Share: manage sharing a project with a particular collaborator. See also Share a Project
Using the Email Option on page 46.
} Get link: forward the sharing link to any number of collaborators. See also Share a
Project with Get Link on page 45.
} Transfer ownership: hand control of data over to a collaborator or customer. See also
Transfer Ownership on page 51.
NOTE
Runs and projects have separate permissions. If you share a project you do
not share the runs contained within the project.
Project Overview Page
The Project Overview page provides access to three panes with information about the
project:
} The About tab gives you summary information about the project: owner, shared
status, date created, and collaborators.
} The Analyses tab gives a list of all the App Sessions in the project, which can be
sorted based on analysis name, last modified date created, status, or application
used to generate the analysis. Clicking on the analysis links to the app results for
that sample, see App Results Page on page 23 for more information.
} The Samples tab gives a list of all the samples in the project. Clicking on a sample
links to the page for that sample, see Sample Overview Page on page 23 for more
information. Selecting the samples allows you to launch it in a app, copy to a
different project, or combine with another result.
NOTE
You can access these panes through the left navigation bar.
Project Toolbar
The Project Toolbar provides the following actions:
} Launch app: run custom apps on your sample. Clicking on the app name leads to a
page with more information about launching that app, including access permissions.
See also Analyze Samples Further on page 31.
Note that running custom apps may incur a charge.
} Share project: manage sharing a project with a particular collaborator. See also Share
a Project Using the Email Option on page 46
} Get link: forward the sharing link to any number of collaborators. See also Share a
Project with Get Link on page 45
} Edit project: edit the name and description of the project. See also Edit Project Details
on page 49
} Transfer owner: hand control of data over to a collaborator or customer. See also
Transfer Ownership on page 51
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Part # 15050652 Rev. A
If you have selected samples in the Samples pane, you can perform additional actions:
} Copy to...: copy samples from this project to another. See also Copy Samples on page
50
} Combine: combine samples. See also Combine Samples on page 50
NOTE
The app session states are defined as follows:
State
Running
Complete
Aborted
Needs
Attention
Description
The app is processing or uploading data.
Processing and file upload has finished and the data is now available
to use
This AppResult or Sample has been aborted and may not be resumed.
Processing cannot continue without user intervention
App Results Page
The App Results page provides details about the results for that app session. There is a
general information pane to the left, and up to four graphs:
}
}
}
}
Low Percentage Graph on page 61
High Percentage Graph on page 62
Clusters Graph on page 63
Mismatch Graph on page 65
For more information about the various apps, see the topics below:
}
}
}
}
}
}
}
}
Custom/PCR Amplicon on page 55
Resequencing on page 55
Library QC on page 56
Small RNA Analysis on page 56
Metagenomics Analysis on page 57
De Novo Assembly Samples Page on page 57
Generate FASTQ on page 58
Isaac on page 58
Sample Overview Page
The Sample Overview page provides 2 panes:
} The Sample Details pane gives a summary of the run with a links to launch a
custom BaseSpace app on your sample. Clicking on the app name leads to a page
with more information about that app, including access permissions.
Note that running custom apps may incur a charge.
} The Files pane gives a list of files associated with that sample. You can either look at
all FASTQ files, or look at files specific for an app session.
See also View Files and Results on page 25. You can also download selected files; see
Download Files with the BaseSpace Downloader on page 44.
BaseSpace User Guide for NextSeq, Miseq, and HiSeq
23
BaseSpace User Interface
Options that are not available for the particular analysis or sample are grayed out.
Apps Tab
The Apps button leads to the Apps page, which provides an overview of the
custom BaseSpace apps that you can run.
} Clicking on the app name leads to a page with more information about that app,
including a link to the developer and their app support contact details.
} Clicking the Launch button
leads you through the launch pages, which
allow you to set up the app session. Depending on the app, you may need to specify
the project, sample, or output folder used by the app, as well as accept access
permissions.
Note that running custom apps may incur a charge.
} You can search for apps using the Search Apps box, or filter by app category on the
right.
Public Data Tab
The Public Data page provides an overview of the publicly available data sets
that you can use. Clicking on a data set provides more information for that data,
and allows you to import the run or project. You can search for apps using the
Search Public Data box, or filter by the research areas and categories listed on the right.
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How To Use BaseSpace
How To Use BaseSpace
The following topics describe how to run different functions in BaseSpace.
View Files and Results on page 25
Analyze Samples Further on page 31
Prepare a NextSeq Run on page 36
Download Files on page 43
Share Data on page 45
Project and Sample Management on page 49
Purchasing on page 51
Search for Runs, Projects, and Samples on page 53
View Files and Results
The following topics describe how to view files and results in BaseSpace.
View Files from a Run
What is it
BaseSpace gives you an option to view your run files or download them individually.
When to use it
Use this if you want to view files such as bcl's or images, you can also download these
files locally.
Why to use it
Use this if you want to view files such as bcl files or images, you can also download
these files locally.
How to use it
1
Click the Runs icon.
2
Click the desired run.
3
From the Run Overview Page, select the Files icon from the sidebar.
4
Select the desired file to view.
View Indexing QC Page
What is it
The Indexing QC page lists count information for indices used in the run. Note that the
Indexing QC will only be available if the run is an index run.
For more information, see Indexing QC on page 68.
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25
When to use it
Use this when you want to access indexing QC results.
Why to use it
You may see unexpected results for a sample with a particular index, and need to
troubleshoot what happened. You may also use it to confirm all indexed samples were
represented properly.
How to use it
1
Click the Runs icon.
2
Click the desired run.
3
There are two methods to go to the Indexing QC page:
• From the Run Overview page click the Indexing QC link.
• From the Run Overview page click the Indexing QC icon from the sidebar
navigation menu.
You can select the displayed lane through the dropdown list.
The first table provides an overall summary of the indexing performance for that lane,
including:
Total Reads
The total number of reads for this lane.
PF Reads
The total number of passing filter reads for this lane.
% Reads Identified (PF)
The total fraction of passing filter reads assigned to an index.
CV
The coefficient of variation for the number of counts across
all indices.
Min
The lowest representation for any index.
Max
The highest representation for any index.
Further information is provided regarding the frequency of individual indices in both
table and graph form. The table contains several columns, including
Index Number
A unique number assigned to each index by BaseSpace for
display purposes.
Sample ID
The sample ID assigned to an index in the sample sheet.
Project
The project assigned to an index in the sample sheet.
Index 1 (I7)
The sequence for the first index read.
Index 2 (I5)
The sequence for the second index read.
% Reads Identified (PF)
The number of reads (only includes Passing Filter reads)
mapped to this index.
This information is also displayed in graphical form. In the graphical display, indices
are ordered according to the unique Index Number assigned by BaseSpace.
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What is it
The Charts page displays charts with run metrics.
When to use it
Use this when you want to view charts such as Flow Cell, Data By Cycle, Data By Lane,
Qscore Distribution, and Qscore Heatmap.
For more information, see Charts on page 69.
Why to use it
Use this if you want access to these various charts.
How to use it
1
Click the Runs icon.
2
Click the desired run.
3
There are two methods to go to the Charts page:
• From the Run Overview page click the Charts link.
• From the Run Overview page click the Charts icon from the sidebar navigation
menu.
View Run Samples List
What is it
The Run Samples List contains a list of all the samples in the run.
When to use it
} Use this when you want to see a list of all the samples in the run
} Use this when you want to navigate to details regarding a specific sample.
Why to use it
Use this if you want a quick way to view all the samples in a Run.
Use this when you want to see more detail regarding your samples such as genome
name, sample or FASTQ files.
How to use it
1
Click the Runs icon.
2
Click the desired run.
3
There are two methods to go to the Run Samples List:
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How To Use BaseSpace
View Run Charts
• From the Runs Overview page click the Samples link.
• From the Runs Overview page click the Samples icon from the sidebar
navigation menu.
You can now click on a sample to see the sample overview; for more information, see
Sample Overview Page on page 23.
View Run Summary
What is it
The Run Summary page has the overall statistics about the run.
When to use it
Use this when you want to view information about the run such as percent
alignment,cycles, densities and so on.
Why to use it
Use this if you want a quick breakdown of the statistics for a particular run.
How to use it
1
Click the Runs icon.
2
Click the desired run.
3
There are two methods to go to the Run Summary:
• From the Run Overview Page select Run Summary button.
• From the Run Overview Page select the Run Summary icon from the left side
bar.
The following metrics are displayed in the top table, split out by read and total:
28
Level
The level or read of the run.
Cycles
The number of cycles in the level.
Yield Total
The number of bases sequenced. This is updated as the run
progresses
Projected Total Yield
The projected number of bases expected to be sequenced at
the end of the run.
Yield Perfect
The number of bases in reads that align perfectly, as
determined by a spiked in PhiX control sample. If no PhiX
control sample is run in the lane, this chart is not available.
Yield <=3 errors
The number of bases in reads that align with 3 errors or less,
as determined by a spiked in PhiX control sample. If no PhiX
control sample is run in the lane, this chart is not available,
and will show a zero value.
Part # 15050652 Rev. A
The percentage of the sample that aligned to the PhiX
genome. This is determined for each level or read
independently.
% Perfect [Num Usable Cycles]
The percentage of bases in reads that align perfectly, as
determined by a spiked in PhiX control sample, at the cycle
indicated in the brackets. If no PhiX control sample is run in
the lane, this chart will display 0% but will still show the
number of cycles used.
% <=3 errors [Num Usable
Cycles]
The percentage of bases in reads that align with 3 errors or
less, as determined by a spiked in PhiX control sample, at the
indicated cycle. If no PhiX control sample is run in the lane,
this chart If no PhiX control sample is run in the lane, this
chart will display 0% but will still show the number of cycles
used.
Error Rate
The calculated error rate of the reads that aligned to PhiX.
Intensity Cycle 1
The average of the A channel intensity measured at the first
cycle averaged over filtered clusters.
% Intensity Cycle 20
The corresponding intensity statistic at cycle 20 as a
percentage of that at the first cycle. 100%x(Intensity at cycle
20)/(Intensity at cycle 1).
%Q>=30
The percentage of bases with a quality score of 30 or higher,
th
respectively. This chart is generated after the 25 cycle, and
the values represent the current cycle.
The following metrics are available in the Read tables, split out by lane:
Tiles
The number of tiles per lane.
Density
The density of clusters (in thousands per mm ) detected by
image analysis, +/- one standard deviation.
Clusters PF
The percentage of clusters passing filtering, +/- one standard
deviation.
Phas./Prephas.
The value used by RTA for the percentage of molecules in a
cluster for which sequencing falls behind (phasing) or jumps
ahead (prephasing) the current cycle within a read.
Reads
The number of clusters (in millions).
Reads PF
The number of clusters (in millions) passing filtering.
%Q>=30
The percentage of bases with a quality score of 30 or higher,
th
respectively. This chart is generated after the 25 cycle, and
the values represent the current cycle.
Yield
The number of bases sequenced which passed filter.
Cycles Err Rated
The number of cycles that have been error rated with respect
to PhiX starting at cycle 1.
Aligned
The percentage that aligned to the PhiX genome.
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How To Use BaseSpace
Aligned
Error Rate
The calculated error rate, as determined by the PhiX
alignment. Subsequent columns display the error rate for
cycles 1–35, 1–75, and 1–100.
Intensity Cycle 1
The average of the A channel intensity measured at the first
cycle averaged over filtered clusters.
%Intensity Cycle 20
The corresponding intensity statistic at cycle 20 as a
percentage of that at the first cycle. 100%x(Intensity at cycle
20)/(Intensity at cycle 1).
View Sample Sheet from a Run
What is it
This option allows you to view the sample sheet that is tied to this run.
When to use it
Use this when you want to view the associated sample sheet for this Run.
Why to use it
You want to check whether the sample sheet was set up properly.
How to use it
1
Click the Runs icon.
2
Click the desired run.
3
From the Run Overview Page, select the Sample Sheet icon from the sidebar.
View the Project Sample List
What is it
The Project Sample List contains the list of samples in a project.
When to use it
} Use this when you want to see a list of all the samples in the project
} Use this when you want to navigate to details regarding a specific sample.
Why to use it
This is an easy way to get to the details page of a sample.
How to use it
30
1
Click the Projects icon.
2
Click the desired project.
3
Click the Samples link from the sidebar navigation menu.
Part # 15050652 Rev. A
View the Analyses List
What is it
The Analyses List contains a list of app sessions in a project.
When to use it
Use this when you want to navigate to details regarding a specific app session.
Why to use it
This is an easy way to get to the details of a particular app session.
How to use it
1
Click the Projects icon.
2
Click the desired project.
You can now click on an Analysis to see the results; for more information, see App
Results Page on page 23.
Analyze Samples Further
The following topics describe how to further analyze samples in BaseSpace, starting with
FASTQ files (HiSeq and MiSeq) or the results from sample-sheet driven workflows
(MiSeq).
Launch the IGV App
What is it
The Integrative Genomics Viewer (IGV) of the Broad Institute is a fully featured genome
browser that allows you to visualize your sequence data in great detail. Illumina has
modified IGV to display alignment and variant data from BaseSpace (BAM and VCF
files).
When to use it
IGV enables you to perform variant analysis after launching Resequencing or Amplicon
workflows in BaseSpace. IGV is run on a project, which is the highest level directory and
contains one or more AppResults. IGV retains all of its native functions, including
loading data from your local computer.
Why to use it
To visualize your sequence data in greater detail.
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How To Use BaseSpace
You can now click on a sample to see the sample overview; for more information, see
Sample Overview Page on page 23.
How to use it
NOTE
The Java run-time environment has to be installed on computer in order for IGV to work
properly. Download Java here: java.com/en/.
Run the IGV App the following way:
1
Click the Projects icon.
2
Click desired project.
3
Click the Launch Apps button and select the IGV application from the dropdown
list.
4
Select the Accept button.
5
Depending on your browser, it will ask you to open or save the .jnlp file.
• For Internet Explorer, click the Open button.
• For Chrome, click the Keep button and then click on file to open.
• For Firefox, select the Open with Java(TM) Web Start Launcher (default) option.
The IGV App opens on your desktop with the requested project loaded.
BaseSpace Data in IGV
The BaseSpace file browser shows data in BaseSpace that is available for viewing in
IGV. The directory structure shown is according to how data is organized in BaseSpace.
A project is the highest level directory and it contains one or more AppResults. If an
AppResult was the result of analyzing a single sample, then the sample name is
appended to the AppResult name. Each AppResult contains zero or more files.
Only alignment (BAM) and variant (VCF) files are shown in the file browser. Double
click a BAM or VCF file to load it as an IGV track. First load VCF files before BAM files
since read tracks can take up an entire IGV screen, which requires scrolling to see
variants.
Additional Reference Genomes
IGV contains a number of installed reference genomes:
}
}
}
}
Homo sapiens: Human hg19
Mus musculus: Mouse mm9
Saccharomyces cerevisiae: S. cerevisiae (sacCer2)
Arabidopsis thaliana: A. thaliana (TAIR10)
In addition, you can download the following additional reference genomes from
Illumina:
} PhiX: ftp://igenome:[email protected]/PhiX/Illumina/RTA/PhiX_
Illumina_RTA.tar.gz
} Staphylococcus aureus (strain NCTC 8325): ftp://igenome:[email protected]/Staphylococcus_aureus_NCTC_8325/NCBI/2006-0213/Staphylococcus_aureus_NCTC_8325_NCBI_2006-02-13.tar.gz
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Launch Third-Party Apps
What is it
A method to launch apps built by third-party vendors. In general, these apps perform
tertiary analysis, visualization, or annotation of data.
When to use it
When you want to run a third-party app on your samples, because it provides an
additional level of analysis that is not provided by Illumina workflow apps. Running
third-party apps may incur a charge.
How to use it
1
Navigate to the project, sample, or app result that you want to run the app on.
2
Click the Launch Apps button and select the desired third-party application from the
dropdown list.
3
Read the End User License Agreement and permissions, and click Accept if you are
ok with them.
The third-party will now guide you through the start-up process.
NOTE
Third-party apps are generated by third-party vendors. For support, contact that vendor.
Launch the Isaac App
What is it
An app that runs the Isaac Aligner and Isaac Variant Caller on your sample.
For more information, see Isaac on page 58
When to use it
When you want to run a fast aligner, and call SNPs and small indels. Isaac should only
be used for whole human genome analysis. Do not use it for other species or targeted
sequencing.
How to use it
1
Click the Projects icon.
2
Click the desired project.
3
From the Samples list, select the sample to run the Isaac workflow on.
4
Click the Launch Apps button and select the Isaac workflow from the dropdown list.
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How To Use BaseSpace
} E. coli (strain DH10B): ftp://igenome:[email protected]/Escherichia_
coli_K_12_DH10B/NCBI/2008-03-17/Escherichia_coli_K_12_DH10B_NCBI_2008-0317.tar.gz
For E. coli, rename the first line in genome.fa from >chr to >ecoli.
5
From the main Isaac Workflow page click the Start button.
The Isaac App will now run on your requested project.
Run Sample Sheet Driven Workflow Apps
Sample sheet driven workflow apps are kicked-off automatically, based on the workflow
that is specified in the sample sheet. You can resubmit the sample sheet and re-queue the
run with new analysis parameters once.
Fix Sample Sheet / Re-Run Workflow
What is it
The Fix Sample Sheet page lets you correct errors in your sample sheet, or set up a new
analysis to re-queue.
When to use it
} To fix errors in the sample sheet.
} To change analysis parameters.
} To change indexing details.
Why to use it
} Errors in the sample sheet may prevent BaseSpace from processing a run. This
allows BaseSpace to finish the analysis.
} The first analysis may have been sub-optimal. You can resubmit the sample sheet
and re-queue the run with new analysis parameters once.
} The index settings for samples may have been wrong. You need to correct this.
NOTE
You can only submit a corrected sample sheet and re-queue the run one time.
How to use it
1
You can reach the Fix Sample Sheet page two ways:
• A run may have a Needs Attention state. Open the run, and click on the Fix
Sample Sheet link.
• Go to a run, select the More drop-down list, and then select Fix Sample Sheet.
The Fix Sample Sheet page opens. If BaseSpace has detected an error, it displays the
issue above the black sample sheet editor.
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Depending on the complexity of the change, you have two options:
• Easy fix: edit the sample sheet in the sample sheet editor. BaseSpace will keep
validating the sample sheet as you edit; any remaning issues are displayed
above the sample sheet editor.
• More complex change: use Illumina Experiment Manager (IEM).
a
If you have not installed IEM yet, click on the Illumina Experiment Manager
(IEM) link, and install IEM.
b
Open IEM.
c
Import the original sample sheet from your system in IEM and edit it, or
generate a new sample sheet. See the Illumina Experiment Manager User
Guide for instructions.
d
Copy and paste the sample sheet into the Sample Sheet Editor in BaseSpace.
BaseSpace will validate the sample sheet; any issues are displayed above the
sample sheet editor.
3
Once you are done editing and the sample sheet is valid, click the Queue Analysis
button, and BaseSpace will start analyzing the run using the new sample sheet. You
can only resubmit a sample sheet and re-queue the run one time.
NOTE
If your edits result in an invalid sample sheet, the Queue Analysis button is not available.
You can return to the original using the Load Original button.
Common Sample Sheet Fixes
If a sample sheet is invalid, it could be because the genome path is not set up correctly.
This situation would be indicated by the Genome Path Unknown Genome warning (as in
the example above). The paths of the standard BaseSpace genomes should conform to
the following relative paths:
Arabidopsis_thaliana\NCBI\build9.1\Sequence\WholeGenomeFASTA
Bos_taurus\Ensembl\UMD3.1\Sequence\WholeGenomeFASTA
Escherichia_coli_K_12_DH10B\NCBI\2008-0317\Sequence\WholeGenomeFASTA
Homo_sapiens\UCSC\hg19\Sequence\WholeGenomeFASTA
Mus_musculus\UCSC\mm9\Sequence\WholeGenomeFASTA
PhiX\Illumina\RTA\Sequence\WholeGenomeFASTA
Rattus_norvegicus\UCSC\rn4\Sequence\WholeGenomeFASTA
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2
Saccharomyces_cerevisiae\UCSC\sacCer2\Sequence\WholeGenomeFASTA
Staphylococcus_aureus_NCTC_8325\NCBI\2006-0213\Sequence\WholeGenomeFASTA
Prepare a NextSeq Run
What is it
You can prepare NextSeq runs through the BaseSpace Prep tab, which organizes
samples, libraries, pools, and run in a single environment.
When to use it
Use this if you want to prepare a sequencing run on a NextSeq instrument, and have the
data stream seamlessly to BaseSpace.
Do not use it to prepare sequencing runs for other instruments. If you do have a NextSeq
sequencing system but don't want to use BaseSpace, you can also start a run straight on
the instrument.
Why to use it
Preparing a run in the Prep tab moves the data and analysis seamlessly to BaseSpace.
Using the Prep tab means BaseSpace is a your single-stop solution for sequencing
management, storage and analysis.
How to use it
1
Log in to BaseSpace. If it is your first time logging in, accept the user agreement.
2
Click the Prep icon
3
Set up a NextSeq run on the Prep Tab in four consecutive steps:
a Biological Samples: Contains information about the samples that are going to be
sequenced. You can create new samples, import samples, or use existing
samples; for instructions, see one of the following topics:
— Create New Biological Samples on page 36
— Import Biological Samples on page 37
— Use Existing Biological Samples on page 38
b Libraries: Consists of biological samples that are prepped and contain adapters.
Each library usually derives from a single biological sample, though biological
samples can be used in multiple libraries. See Libraries on page 18.
c Pools: Consists of groups of libraries that share analysis parameters. Pools can
consist of one or multiple libraries. See Pools on page 18.
d Planned Runs: Contains pools that run with the same analysis parameters, on
the same machine, at the same time. Planned runs can consist of one or multiple
pools. See Planned Runs on page 19
.
Create New Biological Samples
If you want to create a new biological sample, do the following:
36
Part # 15050652 Rev. A
1
Click the Prep icon
.
2
Click Biological Samples.
3
Click the + Create button.
4
Fill out the required fields Sample ID, Name, and Nucleic Acid type.
NOTE
Sample ID and sample name can only exist of alphanumeric characters, dash, or
underscore. Sample ID should be unique and short; sample name can be more descriptive
to provide a human-readable identifier.
5
Optional: Fill out the Organism (species) field
6
Optional: Fill out the Project fields. You can also generate a new project. A project is
optional, but if you don't specify it here, you will have to set it later, because the
output data needs to be stored to the project.
7
When finished, do one of the following:
• If you only want to select the newly created sample, click the Next: Prep
Libraries button. Continue with Prep Libraries on page 38.
• If you want to select multiple samples, click the Save & Continue Later. This
will take you back to the Biological Samples list, with the recently created sample
at the top of the list. Continue with Use Existing Biological Samples on page 38.
Import Biological Samples
If you want to import new biological samples, do the following:
1
Click the Prep icon
.
2
Click Biological Samples.
3
Click the Import button.
4
If you have not generated an import file yet, click on the template link, fill out the
samples, and be aware of the following when filling out the template:
• User Sample ID and sample name can only exist of alphanumeric characters,
dash, or underscore. Sample ID should be unique and short; sample name can
be more descriptive to provide a human-readable identifier.
• The Organism (species) field is optional.
• The Project field is optional at this step, but if you don't specify it here, you will
have to set it later, because the output data needs to be stored to the project.
• Fill out the Nucleic Acid column with DNA or RNA.
BaseSpace User Guide for NextSeq, Miseq, and HiSeq
37
How To Use BaseSpace
NOTE
Use the import function to create several new samples, see Import Biological Samples on
page 37.
Figure 10 Import Sample Template
5
Click the Choose File button.
6
Browse to the import file and click Open.
7
Click Import.
8
When finished, do one of the following:
• If you only want to select the newly created samples, click the Next: Prep
Libraries button. Continue with Prep Libraries on page 38.
• If you want to select multiple samples, click the Save & Continue Later. This
will take you back to the Biological Samples list, with the recently created sample
at the top of the list. Continue with Use Existing Biological Samples on page 38.
Use Existing Biological Samples
The Biological Samples list shows all available samples you have created on your
account.
9
To select existing sample(s), do one of the following in the Biological Samples list:
} Select the checkboxes.
} Click the sample. If you want to select multiple samples, hold the Ctrl button.
} Select all samples by selecting the checkbox next to the SampleID header.
10 Click the Prep Libraries button in the top navbar.
Prep Libraries
On the Prep Libraries page, you assign indices to biological samples, based on the
indices available in the library preparation chosen. Every used well or tube contains a
separate library. Best practice is to set up the libraries in BaseSpace first, export a file of
your library settings, and use that to pipet the biological samples into the proper wells or
tubes.
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Part # 15050652 Rev. A
1
Select the library prep type. BaseSpace now automatically assigns indices to wells or
tubes, depending on the format of the library prep type.
Figure 11 Tube Set Up for Single Index Library Preparation Kit
Figure 12 Plate Set Up for Dual Index Library Preparation Kit
2
Enter the plate ID. The ID needs to be unique.
3
Click the Auto Prep button to fill the plate or tubes automatically with all samples
listed.
NOTE
You can also manually drag the samples to wells or tubes:
1. Select one or more samples. To multiselect, hold Shift. To multiselect on Firefox or
Internet Explorer 9, you need to click the well twice.
2. Drag selected samples to a position.
3. Check whether the indices have been assigned to the proper samples. Hovering over a
position will reveal the sample that is assigned to that position. You can drag samples
from position to position.
4
Save a file of your library settings by clicking the Download CSV button. Use this
file in the lab to indicate which biological samples get pipetted into specific wells.
5
When finished, do one of the following:
BaseSpace User Guide for NextSeq, Miseq, and HiSeq
39
How To Use BaseSpace
NOTE
If you do not want to use index sequencing, you still need to assign your biological sample
to an index. Only when you set up your sequencing run, you will specify that you do not
sequence the index.
} If you want to select the new plate or tubes, click the Pool Libraries button. Continue
with Pool Libraries on page 40.
} If you want to select multiple library preps or plates, do the following:
a Click the Save & Continue Later. This will take you to the Libraries list, with the
recently created set up at the top of the list.
b Select the checkboxes in the Libraries list.
c Click the Pool Libraries button in the top navbar.
NOTE
If one of your samples is not assigned to a project, you cannot continue. Select the
sample, click the Set Project button, and assign it to a project. You can also
generate a new project.
Nextera Rapid Capture Considerations
If you are performing Nextera Rapid Capture, do the following:
} Choose Nextera Enrichment as library prep.
} Put biological samples belonging to the same enrichment on a row next to each
other.
} Change the index in the drowdown menu to the left of the rows to the proper index,
probably the same indices for the different rows (enrichments).
} Name your plate in such a way that makes clear multiple enrichments are on the
plate, or add a note to that effect in the Note field.
Pool Libraries
The Pool Libraries page allows you to pool samples and sequence them in the same run,
using the same analysis parameters.
40
6
Fill out the first pool ID. Pool ID needs to be unique.
7
If needed, you can create additional pools on the right by clicking the + Add Pool
button and filling out the pool IDs.
• Colors of the wells will correspond to the colors of the pools.
• You can hover over the wells to see the library IDs.
8
Drag and drop individual samples from their well on the plate to a pool.
You can multiselect by holding Shift. To multiselect on Firefox or Internet Explorer 9,
you need to click the well twice.
Part # 15050652 Rev. A
How To Use BaseSpace
9
If you want to pool libraries from multiple plates, use the Plate dropdown menu to
specify the plate.
NOTE
You can also merge pools the following way:
• Click the Save & Continue Later. This will take you to the Pools list, with the
recently created plate at the top of the list.
• Select the checkboxes in the Pools list.
• Click the Merge Pools button in the top navbar.
10 Click the Plan Run button.
Nextera Rapid Capture Considerations
If you are performing Nextera Rapid Capture, make sure to assign only samples from the
same enrichment to one pool, and note this in the pool name.
Plan Runs
In the Planned Runs page you can set up the parameters for the sequencing run on your
NextSeq instrument.
BaseSpace User Guide for NextSeq, Miseq, and HiSeq
41
1
Enter a name for your planned run.
2
Optional: Enter the reagent barcode you plan to use. This will link a reagent kit to this
run.
3
Select the rehyb checkbox if you are performing a rehybridization.
4
5
Verify the Review Indexes section for the indexing strategy. For indexing, it will be
set according to the index/library prep type chosen previously. If you choose to
override this default indexing scheme, you will be required to select the Index type
(Single, Dual, or No Index) and make sure that you enter the number of index cycles
accordingly. If you have selected multiple libraries, you can not specify No Index.
BaseSpace automatically checks if the indices chosen have enough diversity; if not, it
will warn you that you should change your index strategy.
6
Verify the pool included in the planned run.
7
42
Fill out the Enter Cycles section:
} Single- vs. paired-end
} Number of cycles per read
Once your settings are complete, choose one of these two options to continue:
} Click the Sequence button. This opens the Planned Runs list , and sets the state of
the recently planned run to Ready to Sequence.
Part # 15050652 Rev. A
NOTE
A planned run must be in the Ready to Sequence state in order for it to show up in the
Planned Runs list in the control software on the instrument.
8
If you want to change a planned run to the Ready to Sequence state, select the planned
run from the list and click the Sequence arrow link in the top navbar on the
Planned Runs list page.
Your run now shows up in the Planned Runs list in the control software on your
NextSeq sequencing system. Complete the run from your sequencing instrument. A
sample sheet is not required. BaseSpace will automatically generate FASTQ files once the
sequencing run is complete; for more information, see Generate FASTQ on page 58.
Download Files
The following topics describe how to download files in BaseSpace.
For more information about file types, see BaseSpace Files on page 75.
Download File Package from a Run
What is it
BaseSpace allows you to download data either as a pre-defined package for MiSeq runs,
one-by-one, or your own selection. This topic describes how to download packages. For a
selection of files, see Download Files with the BaseSpace Downloader on page 44; for
individual FASTQ files, see Download FASTQ Files with the File Browser on page 44.
The packages available depend on your workflow; packages that are grayed out are not
available for download. There are four types of data packages:
}
}
}
}
Variant Data, containing vcf files with variant calls.
Aligned Data, containing BAM files with aligned reads.
Unaligned Data, containing FASTQ files with unaligned reads.
SAV Data, containing file describing the set up of the run and interop files.
For more information about file types, see BaseSpace Files on page 75.
When to use it
} Use this when you want to download a packaged(zipped) file for Variant, Aligned,
Unaligned or SAV data.
} Don’t use if you only want individual files.
Why to use it
If you want to download for Variant, Aligned, Unaligned or SAV data in a neatly
packaged file versus downloading the files one-by-one.
How to use it
1
Click the Runs icon.
BaseSpace User Guide for NextSeq, Miseq, and HiSeq
43
How To Use BaseSpace
} Click the Save & Continue Later button. This opens the Planned Runs list , and sets
the state of the recently planned run to Planning.
2
Click the desired run.
3
Click the Download button.
4
Select the desired data option.
Download FASTQ Files with the File Browser
What is it
BaseSpace allows you to download data either as a pre-defined package, one-by-one, or
your own selection. This topic describes how to download individual FASTQ files with
the file browser. For packages, see Download FASTQ Files with the File Browser on page 44;
for a selection of files, see Download Files with the BaseSpace Downloader on page 44.
When to use it
Use this option when you want to download FASTQ files per sample.
Why to use it
If you only want to download the FASTQ files of a sample, it saves you time, because
you are not downloading all the other files.
How to use it
1
Click the Runs icon or Projects icon.
2
Click the desired run or project.
3
Click the desired sample in the Samples pane.
4
In the Files pane, select the FASTQ Files section.
5
Click the file you want to download.
6
Click the Download button.
BaseSpace will now download the files to the desired location.
For more information about FASTQ files, see FASTQ Files on page 84.
Download Files with the BaseSpace Downloader
What is it
BaseSpace allows you to download data either as a package or individually. This topic
describes how to download multiple files with the downloader; for packages, see
Download File Package from a Run on page 43.
When to use it
Use this option when you want to download multiple files per sample, but do not want
to download a pre-defined package.
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Part # 15050652 Rev. A
If you only want to download a number files of a sample, it saves you time, because you
are not downloading all the other files.
How to use it
1
Click the Runs icon or Projects icon.
2
Click the desired run or project.
3
Click the desired sample in the Samples pane.
4
In the Files pane, select the checkboxes for the desired files.
5
Click the Download Selected button.
The BaseSpace Downloader will guide you through the download process, and start the
download of the files to the desired location.
Share Data
Data in BaseSpace can be shared with collaborators in a couple of different ways. You
can either share data at a run or project level, via an email invitation or through a
hyperlink. With the email invitation option, only the accounts with the specified email
can view shared data. Sharing via a hyperlink option allows anyone with access to the
hyperlink to be able to view the shared data, as long as the hyperlink is still active.
Sharing is for read-only access. If you want a collaborator to have write access, see
Transfer Ownership on page 51.
NOTE
Runs and projects have separate permissions. If you share a run, the project
associated with that run is not shared automatically, meaning samples and
app results will not be accessible to collaborators of the run.
The following topics describe how to share.
Share a Project with Get Link
What is it
Sharing using the Get Link option allows you to share a project or a run with any
collaborator who has access to the link. The hyperlink can be turned on or off by setting
the activate or deactivate option. Be aware that anyone can access the project or run
when the link is activated. Furthermore, anyone who previously accepted the link will
still have access to the run even though the link is deactivated.
NOTE
If you want more control, use the email share option where you can specify who can view
the project (Share a Project Using the Email Option on page 46).
BaseSpace User Guide for NextSeq, Miseq, and HiSeq
45
How To Use BaseSpace
Why to use it
When to use it
} Use this when you don’t want to assign the project to a specific person.
} This share link can be forwarded to many other collaborators while the link is still
active.
} Don’t use this option if you want to confine the list of who has access to this project.
Why to use it
If you want an easy way to share a link without the hassle of adding specific people by
email and setting permissions, then this is the way to go.
How to use it
1
Click the Projects icon.
2
Click the desired project.
3
Click the Get Link button.
4
Click the Activate button.
5
Copy the URL to share with collaborators.
The link is active until the Deactivate option is selected. The path to deactivate a sharing
link is very similar:
1
Navigate to the shared item.
2
Click the Get Link button.
3
Click the Deactivate button.
Share a Project Using the Email Option
What is it
Sharing using the "Share" option allows you to share a Project or Run with a specified
collaborator via an email link. The specified collaborators will receive an email with a
link to the Project or Run and only that person can view the corresponding data.
NOTE
The Email option allows greater control over who can view your data as opposed to
sharing using the Get Link options which gives anyone access to your data if the link is left
activated. For more information, see Share a Project with Get Link on page 45
When to use it
} Use this option of you want to easily share your project with collaborators.
} Use this option if you want to be able to control who has access to the projects.
Why to use it
Use this option if you want to be able to control who has access to the project.
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Part # 15050652 Rev. A
1
Click the Projects icon.
2
Click the desired project.
3
Click Share Project.
4
In the Share Settings dialog box, enter the collaborators email address and click the
Invite button.
NOTE
The invitation email address must match your BaseSpace login's email address or else
your collaborator will not be able to view the project.
5
4. Click Save Settings.
Share a Run with Get Link
What is it
Sharing using the Get Link option allows you to share a run with any collaborator who
has access to the link. The hyperlink can be turned on or off by setting the activate or
deactivate option. Be aware that anyone can access the project or run when the link is
activated. Furthermore, anyone who previously accepted the link will still have access to
the run even though the link is deactivated.
Sharing runs with the Get Link option is very similar to sharing projects with the Get
Link option.
NOTE
If you want more control, use the email share option where you can specify who can view
the project (Share a Run Using the Email Option on page 48).
When to use it
} Use this when you don’t want to assign the run to a specific person.
} This share link can be forwarded to many other collaborators while the link is still
active.
} Don’t use this option if you want to confine the list of who has access to this run.
Why to use it
If you want an easy way to share a link without having to specify people's email and
setting permissions.
How to use it
1
Click the Runs icon.
2
Click the desired run.
3
Click the More button and select the Get Link option.
4
Click the Activate button.
BaseSpace User Guide for NextSeq, Miseq, and HiSeq
47
How To Use BaseSpace
How to use it
5
Copy the URL to share with collaborators.
The link is active until the Deactivate option is selected. The path to deactivate a sharing
link is very similar:
1
Navigate to the run
2
Click the Get Link button.
3
Click the Deactivate button.
NOTE
Runs and projects have separate permissions. If you share a run, the project
associated with that run is not shared automatically, meaning samples and
app results will not be accessible to collaborators of the run.
Share a Run Using the Email Option
What is it
Sharing using the Share option allows you to share a project or run with a specified
collaborator via an email link. Specified collaborators will receive an email with a link to
the project or run and only that person can view the corresponding data.
NOTE
The Email option allows greater control over who can view your data as opposed to
sharing using the Get Link options which gives anyone access to your data if the link is left
activated. See Share a Run with Get Link on page 47 for more information.
When to use it
} Use this option of you want to easily share your run with collaborators.
} Use this option if you want to be able to control who has access to the run.
Why to use it
Use this option if you want to be able to control who has access to the run.
How to use it
1
Click the Runs icon.
2
Click the desired run.
3
Click the Share button.
4
In the Share Settings dialog box, enter the collaborators email address and click the
Invite button.
NOTE
The invitation email address must match your BaseSpace login's email address or else
your collaborator will not be able to view the project.
5
4. Click the Save Settings button.
48
NOTE
Runs and projects have separate permissions. If you share a run, the project
associated with that run is not shared automatically, meaning samples and
app results will not be accessible to collaborators of the run.
Part # 15050652 Rev. A
The following topics describe how to manage projects and samples in BaseSpace.
Edit Project Details
What is it
The way to edit project details.
When to use it
Use this when you want to change details regarding the project such as the description
or project name.
Why to use it
Use this if you need to edit the project name or description
How to use it
1
Click the Projects icon.
2
Click the desired project.
3
Click the Edit Project button.
4
Make changes in the Edit Project dialog box
5
Click the Save.
Set Up a New Project
What is it
A method to set up a new project.
When to use it
} When you want to analyze a sample in the context of two different projects
} When you want to transfer ownership of samples to a collaborator, but still keep a
copy yourself
} When you want to split a project into multiple projects
How to use it
1
Click the Projects icon.
2
Click New Project link in the top left corner.
3
Enter a new name and description.
4
Click the Create button.
To copy samples into the new project, seeCopy Samples on page 50.
BaseSpace User Guide for NextSeq, Miseq, and HiSeq
49
How To Use BaseSpace
Project and Sample Management
Combine Samples
What is it
A method to combine (merge) samples.
When to use it
When you want to merge the data from two different sequencing runs on the same
sample.
How to use it
1
Click the Projects icon.
2
Click the desired project.
3
Click the Samples link from the sidebar navigation menu.
4
Select the checkboxes of the samples you want to combine.
5
Click the Combine button.
6
Click the Combine button in the pop-up screen.
Copy Samples
What is it
A method to copy samples from one project to another.
When to use it
} When you want to analyze a sample in the context of two different projects
} When you want to transfer ownership of a sample to a collaborator, but still keep a
copy yourself
} When you have assigned a sample to the wrong project
How to use it
50
1
Click the Projects icon.
2
Click the desired project.
3
Click the Samples link from the sidebar navigation menu.
4
Select the checkboxes of the samples you want to combine.
5
Click the Copy button.
6
Select the new project in the dropdown list.
7
Click the Copy button.
Part # 15050652 Rev. A
What is it
A method to hand control of data over to a collaborator or customer.
When to use it
} If you want to give control of your data to a collaborator
} If you sequenced samples for a customer, for example, if you are a core lab or service
provider.
How to use it
1
Select the project or run you want to transfer:
• Project:
a
Click the Projects icon.
b
Click the desired project.
c
Click the Transfer Owner button.
• Run:
2
a
Click the Runs icon.
b
Click the desired run.
c
Click the More button, and then select the Transfer Ownership option.
Enter the new owner's email and an optional message in the Transfer Ownership
dialog box.
3
Click Continue.
BaseSpace sends the new owner an email asking to accept the ownership of the run or
project. The ownership transfer of the project or run will complete once the new owner
accepts. At this point, you will have no control over that run or project. You will also not
be able to see that run or project, unless the new owner shares it with you; see Share Data
on page 45 for more information.
Purchasing
In order to buy app sessions from third-party vendors, you need to have iCredits. This
chapter describes how to purchase iCredits and manage your wallet and purchases.
Access Your Wallet
What is it
The wallet contains your iCredits and credit card information.
When to use it
Use it to update credit cards or add iCredits.
BaseSpace User Guide for NextSeq, Miseq, and HiSeq
51
How To Use BaseSpace
Transfer Ownership
Why to use it
You need iCredits if you want to purchase app sessions. The wallet allows you to
manage your iCredits.
How to use it
1
Go to your account.
2
Click on the Wallet button.
When you are on the Wallet screen, you can add iCredits or credit cards.
Adding iCredits
What is it
iCredits allows you to purchase app sessions.
When to use it
Use Adding iCredits when you are running low on iCredits, and you want to buy app
sessions.
Why to use it
Third-party apps provide functionality that may be exactly needed for your analysis, but
need to be purchased with iCredits.
How to use it
You can either add iCredits directly, or create a purchase order.
} Add iCredits directly:
1
Go to the Wallet screen.
2
Click the Add More button.
3
Enter the amount and select the desired credit card from the drop down list
4
Click the Continue button.
5
Click the Purchase button.
A message appears stating how many credits have been added.
6 Click the OK button.
} Create a purchase order:
1
Go to the Wallet screen.
2
Click the Create a Quote link.
3
Enter the amount and the desired account.
4
Click the Create Quote button.
The purchase order appears, and once processed, Illumina will credit the
account with the iCredits.
52
Part # 15050652 Rev. A
You can generate a paper copy using the Print button.
Adding Credit Card
What is it
You can use a credit card to purchase iCredits, but you need to add it first to BaseSpace.
When to use it
When you want to buy iCredits with a new credit card.
Why to use it
Third-party apps provide functionality that may be exactly needed for your analysis, but
need to be purchased with iCredits.
How to use it
1
Go to the Wallet screen.
2
Click on the Add Credit Card button.
3
Fill in the credit card info and click Submit.
View Purchase History
What is it
The Purchase History page contains detailed information about purchases, adjustments,
and balance for your account.
When to use it
When you want to review your purchases.
Why to use it
You may want to track where you spend your iCredits, or to see if a refund has been
processed
How to use it
1
Go to your account.
2
Click on the Purchase History button.
Now you can review your purchases, filter on type of transaction, and sort by order
number, vendor, date, or total iCredits used.
Search for Runs, Projects, and Samples
What is it
The Search box allows you to find runs, projects, and samples.
BaseSpace User Guide for NextSeq, Miseq, and HiSeq
53
How To Use BaseSpace
5
When to use it
When you want to do a quick search for something
Why to use it
Use if there are a lot of runs and you want a quick way to search.
How to use it
1
Type in the run, project, or sample name in the search field and hit enter or click on
magnifying glass icon.
2
Select the desired run, project, or sample in Search Results. You can also filter the
search results by these categories using the dropdown list at the left of the Search
Results page.
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Part # 15050652 Rev. A
Workflow Reference
Workflow Reference
This section describes the Illumina workflow apps listed below.
Resequencing on page 55
Custom/PCR Amplicon on page 55
Library QC on page 56
Small RNA Analysis on page 56
Metagenomics Analysis on page 57
De Novo Assembly Samples Page on page 57
Generate FASTQ on page 58
Isaac on page 58
Resequencing
The sample sheet driven Resequencing app compares the DNA sequence in the samples
against a reference genome and identifies any variants (SNPs or indels) relative to the
reference sequence. The main output files generated by the Resequencing workflow are
.bam files (containing the alignment results) and .vcf files (containing the variant calls).
The Resequencing workflow can only be used to analyze MiSeq sequencing results.
The Resequencing App Results Page provides four graphs, described below:
}
}
}
}
Low Percentage Graph on page 61
High Percentage Graph on page 62
Clusters Graph on page 63
Mismatch Graph on page 65
The Resequencing Sample Details Page provides five panes, described below:
}
}
}
}
}
Samples Table on page 87
Coverage Graph on page 89
Q-Score Graph on page 89
Variant Score Graph on page 89
Variants Table on page 89
The graphs and variants table display data for the chromosome that is selected in the
dropdown list.
Custom/PCR Amplicon
The Custom/PCR Amplicon workflow evaluates short regions of amplified DNA
(amplicons) for variants. The focused sequencing of amplicons enables high-coverage
sequencing of particular regions across a large number of samples. The main output files
generated by the Custom/PCR Amplicon workflow are .bam files (containing the aligned
reads) and .vcf files (containing the variant calls). The Custom/PCR Amplicon workflow
supports multiple manifests (containing the probe regions) and consensus sequence
reporting for multi-manifest runs.
BaseSpace User Guide for NextSeq, Miseq, and HiSeq
55
The Custom/PCR Amplicon workflow can only be used to analyze MiSeq sequencing
results
The Custom/PCR Amplicon App Results Page provides a four graphs, described below:
}
}
}
}
Low Percentage Graph on page 61
High Percentage Graph on page 62
Clusters Graph on page 63
Mismatch Graph on page 65
The PCR amplicon Sample Details page provides six panes, described below:
}
}
}
}
}
}
Samples Table on page 87
Amplicons Table on page 89
Coverage Graph on page 89
Q-Score Graph on page 89
Variant Score Graph on page 89
Variants Table on page 89
The graphs and variants table display data for the amplicon that is selected in the
Amplicon Table.
Library QC
The Library QC workflow is intended for evaluating the abundance, fragment length,
and sample quality of libraries. The analysis performed in the Library QC workflow is
very similar to the Resequencing workflow. The Library QC workflow does not perform
variant calling; instead, it provides a report of the characteristics of each sample.
The Library QC workflow can only be used to analyze MiSeq sequencing results
The Library QC App Results page provides a four graphs, described below:
}
}
}
}
Low Percentage Graph on page 61
High Percentage Graph on page 62
Clusters Graph on page 63
Mismatch Graph on page 65
The Library QC Sample Details Page provides four panes, described below:
}
}
}
}
Samples Table on page 87
Coverage Graph on page 89
Q-Score Graph on page 89
Sample QC Table on page 91
The graphs display data for the chromosome that is selected in the dropdown list.
Small RNA Analysis
The Small RNA workflow measures the abundance of various types of short RNA
sequences, particularly miRNA. It is suitable for identifying and quantifying miRNA
expression and for comparing abundance across samples.
The Small RNA workflow can only be used to analyze MiSeq sequencing results
The small RNA analysis App Results page provides access to two graphs, described
below.
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The Small RNA Sample Details Page provides three panes, described below:
} Small RNA Samples Table on page 88
} Small RNA Pie Chart on page 90
} Small RNA Graph on page 90
Metagenomics Analysis
The Metagenomics workflow enables the analysis of 16S ribosomal RNA, a component
of the 30S subunit of prokaryotic ribosomes. The 16S ribosomal sequences from an
environmental sample can be analyzed to determine which organisms are present. In
MiSeq Reporter, a naïve Bayesian classifier (based on Wang et al., Appl Environ Microbiol
(2007) Aug;73(16):5261-7) has been implemented that has been optimized for Illumina
paired-end reads. Our 16S rRNA data store is populated by sequences in the May 2011
release of the GreenGenes 16S rRNA database. The main output of this workflow is a
classification of reads at several taxonomic levels (kingdom, phylum, class, order, family,
genus).
The Metagenomics workflow can only be used to analyze MiSeq sequencing results
The metagenomics App Results page provides one graph, described below.
} Clusters Graph on page 63
The Metagenomics Sample Details Page provides two panes, described below:
} Samples Table on page 87
} Metagenomics Pie Chart on page 90
De Novo Assembly Samples Page
The Assembly workflow enables de novo assembly of a draft genome directly from the
sequencing reads. Because assembly relies upon significant coverage of the genome, this
workflow is best suited for the assembly of small genomes (up to 5 to 10 MB). The
assembly process is performed by the Velvet software (Velvet: algorithms for de novo
short read assembly using de Bruijn graphs (2008) D.R. Zerbino and E. Birney. Genome
Research 18:821–829).
The Assembly workflow can only be used to analyze MiSeq sequencing results
The de novo assembly App Results page provides access to three graphs, described
below:
} Low Percentage Graph on page 61
} High Percentage Graph on page 62
} Clusters Graph on page 63
The De Novo Assembly Sample Details Page provides two panes, described below:
} De Novo Assembly Samples Table on page 88
} Samples Graph on page 91
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Workflow Reference
} Clusters Graph on page 63
} Trimmed Lengths on page 66
Generate FASTQ
The app Generate FASTQ does not perform any analysis, but generates FASTQ files for
download and shows basic summary data. The Generate FASTQ app can be used with
all sequencing instruments that are supported by BaseSpace. For more information, see
FASTQ Files on page 84.
Generate FASTQ is also used to analyze RNA-Seq samples from MiSeq.
Isaac
Alignment and variant calling in the Isaac app are performed with the Isaac Alignment
Software and the Isaac Variant Caller. The Isaac workflow generates output that consists
of the realigned and duplicate marked reads in a BAM file format, variants in a VCF file
format, an additional Genome VCF (gVCF) file that has an entry for every base in the
reference, which differentiates reference calls and no calls, and a summary of the run
quality.
The Isaac app is intended for use with HiSeq sequencing runs.
See Isaac App Results Page on page 91.
Isaac Aligner
The Isaac aligner1 aligns DNA sequencing data, single or paired end, with read lengths
and low error rates using the following steps:
} Candidate mapping positions—Identifies the complete set of relevant candidate
mapping positions using a 32-mer seed-based search.
} Mapping selection—Selects the best mapping among all candidates.
} Alignment score—Determines alignment scores for the selected candidates based on
a Bayesian model.
} Alignment output—Generates final output in a sorted duplicate-marked BAM file
and summary file.
1
Come Raczy, Roman Petrovski, Christopher T. Saunders, Ilya Chorny, Semyon
Kruglyak, Elliott H. Margulies, Han-Yu Chuang, Morten Källberg, Swathi A. Kumar,
Arnold Liao, Kristina M. Little, Michael P. Strömberg and Stephen W. Tanner (2013)
Isaac: Ultra-fast whole genome secondary analysis on Illumina sequencing
platforms. Bioinformatics 29(16):2041-3
bioinformatics.oxfordjournals.org/content/early/2013/06/04/bioinformatics.btt314
Candidate Mapping
To align reads, the Isaac aligner first identifies a small but complete set of relevant
candidate mapping positions. The Isaac aligner begins with a seed-based search using
32-mers as seeds. The initial single-seed search is followed by a multi-seed search for
only those reads that were not mapped unambiguously with a single seed.
Mapping Selection
Following a seed-based search, the Isaac aligner selects the best mapping among all the
candidates. For paired-end data sets, all mappings where only one end is aligned (called
orphan mappings) trigger a local search to find additional mapping candidates. These
candidates (called shadow mappings) are defined by the expected minimum and
maximum insert size. After optional trimming of low quality 3' ends and adapter
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Alignment Scores
The alignment scores of each read pair are based on a Bayesian model, where the
probability of each mapping is inferred from the base qualities and the positions of the
mismatches. The final mapping quality is the alignment score, truncated to 60 if above
60, and possibly corrected to known ambiguities in the reference as flagged in the seeds.
Following alignment, reads are sorted. Further analysis is performed to identify
duplicates and optionally to realign indels.
Alignment Output
After sorting the reads, the Isaac aligner generates compressed binary alignment output
files, called BAM (*.bam) files, using the following process:
} Marking duplicates—Detection of duplicates is based on the location and observed
length of each fragment. The Isaac aligner identifies and marks duplicates even
when they appear on oversized fragments or chimeric fragments. Note that optical
duplicates are already filtered out during RTA processing.
} Realigning indels—The Isaac aligner tracks previously-detected indels, over a
window large enough for the current read length, and applies the known indels to
all reads with mismatches.
} Generating BAM files—The first step in BAM file generation is creation of the BAM
record, which contains all required information except the name of the read. The
Isaac aligner reads data from base call (BCL) files that were written during primary
analysis on the sequencer to generate the read names. Data is then compressed into
blocks of 64 Kb or less to create the BAM file.
Isaac Variant Caller
The Isaac Variant Caller (the algorithm is also referred to as Starling2) identifies single
nucleotide polymorphisms (SNPs) and small indels using the following steps:
} Read filtering—Filters out reads failing quality checks.
} Indel calling—Identifies a set of possible indel candidates and realigns all reads
overlapping the candidates using a multiple sequence aligner.
} SNP calling—Computes the probability of each possible genotype given the aligned
read data and a prior distribution of variation in the genome.
} Indel genotypes—Calls indel genotypes and assigns probabilities.
} Variant call output—Generates output in a compressed genome variant call (gVCF)
file. See gVCF Files on page 78 for details.
Indel Candidates
Input reads are filtered by removing any of the following:
}
}
}
}
Reads that failed primary analysis quality checks.
Reads marked as PCR duplicates.
Paired-end reads not marked as a proper pair.
Reads with a mapping quality less than 20.
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sequences, the possible mapping positions of each fragment are compared. This takes
into account pair-end information (when available), possible gaps using a banded SmithWaterman gap aligner, and possible shadows. The selection is based on the SmithWaterman score and on the log-probability of each mapping.
Indel Calling
The variant caller proceeds with candidate indel discovery and generates alternate read
alignments based on the candidate indels. As part of the realignment process, the variant
caller selects a representative alignment to be used for site genotype calling and depth
summarization by the SNP caller.
SNP Calling
The variant caller runs a series of filters on the set of filtered and realigned reads for SNP
calling without affecting indel calls. First, any contiguous trailing sequence of N base
calls are trimmed from the ends of reads. Using a mismatch density filter, reads having
an unexpectedly high number of disagreements with the reference are masked, as
follows:
} The variant caller treats each insertion or deletion as a single mismatch.
} Base calls with more than two mismatches to the reference sequence within 20 bases
of the call are ignored.
} If the call occurs within the first or last 20 bases of a read, the mismatch limit is
applied to a 41-base window at the corresponding end of the read.
} The mismatch limit is applied to the entire read when the read length is 41 or
shorter.
Indel Genotypes
All bases marked by the mismatch density filter and any N base calls that remain after
the end-trimming step are filtered out by the variant caller. These filtered base calls are
not used for site-genotyping but appear in the filtered base call counts in the variant
caller output for each site.
All remaining base calls are used for site-genotyping. To account for the possibility of
error dependencies, the genotyping method heuristically adjusts the joint error
probability that is calculated from multiple observations of the same allele on each
strand of the genome. This method treats the highest quality base call from each allele
and strand as an independent observation and leaves the associated base call quality
scores unmodified. However, quality scores for subsequent base calls for each allele and
strand are adjusted to increase the joint error probability of the given allele above the
error expected from independent base call observations.
Variant Call Output
After the site and indel genotyping methods are complete, the variant caller applies a
final set of heuristic filters to produce the final set of non-filtered calls in the output.
The output in the genome variant call (gVCF) file captures the genotype at each position
and the probability that the consensus call differs from reference, which is expressed as a
phred-scaled quality score.
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This section provides the data references, and describes the files, charts, graphs, and
tables listed below.
Workflow Graphs on page 61
Run Summary on page 66
Indexing QC on page 68
Charts on page 69
BaseSpace Files on page 75
Sample Details Page Components on page 87
Isaac App Results Page on page 91
Workflow Graphs
The workflow graphs provide metrics that allow you to judge the success of the
sequencing run for that sample. The following topics provide information about these
charts.
Low Percentage Graph
What is it?
The Low Percentage Graph represents statistics of the run that are generally near zero in
an ideal run. These are a subset of all metrics of the sequencing run itself.
When to use it.
Use the Low Percentage Graph to judge sequencing metrics for a sample. This should
also be used when troubleshooting unexpected results.
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Data Reference
When not to use it.
This graph is not a good predictor of yields or quality of final results.
How to use it
Metric
Description
Phasing 1
The percentage of molecules in a cluster that fall behind the current cycle within
Read 1.
Phasing 2
The percentage of molecules in a cluster that fall behind the current cycle within
Read 2.
PrePhasing
1
The percentage of molecules in a cluster that run ahead of the current cycle
within Read 1.
PrePhasing
2
The percentage of molecules in a cluster that run ahead of the current cycle
within Read 2.
Mismatch
1
The average percentage of mismatches for Read 1 over all cycles.
Mismatch
2
The average percentage of mismatches for Read 2 over all cycles.
You can expand a chart by clicking on the expand button.
High Percentage Graph
What is it?
The High Percentage Graph represents run statistics that are generally near 100% in an
ideal run. These are metrics of the sequencing run or the analysis step.
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Use the High Percentage Graph to judge sequencing metrics for a sample. This should
also be used when troubleshooting unexpected results.
When not to use it.
Do not use the High Percentage Graph to look at tertiary analysis metrics.
How to use it
Metric
Description
|20/|1 1
The ratio of intensities at cycle 20 to the intensities at cycle 1 for Read 1.
|20/|1 2
The ratio of intensities at cycle 20 to the intensities at cycle 1 for Read 2.
Align 1
The percentage of clusters that aligned to the reference in Read 1.
Align 2
The percentage of clusters that aligned to the reference in Read 2.
PE
Orientation
The percentage of paired-end alignments with the expected orientation.
PE
Resynthesis
The ratio of first cycle intensities for Read 1 to first cycle intensities for Read
2.
PF
The percentage of clusters passing filters.
You can expand a chart by clicking on the expand button.
Clusters Graph
What is it?
The Clusters graph provides information about the amount of clusters that are detected
during sequencing, split out by the following groups:
}
}
}
}
}
Total
Passing filter
Unaligned
Unindexed
Duplicates
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When to use it.
When to use it.
Use the Clusters Graph to judge clustering success and relative cluster density between
lanes (on HiSeq), and as a snap shot of the overall run. Can assist with identifying
overclustering issues.
When not to use it.
Do not use the Clusters Graph to look at tertiary analysis metrics.
How to use it
A cluster represents a clonal spot on the flow cell that contains the amplified DNA
strands that will be sequenced.
x-axis
Description
Raw
The total number of clusters detected in the run.
PF
The total number of clusters passing filter in the run.
Unaligned
The total number of clusters passing filter that did not align to the reference
genome, if applicable. Clusters that are unindexed are not included in the
unaligned count.
Unindexed
The total number of clusters passing filter that were not associated with any
index sequence in the run.
Duplicate
The total number of clusters for a paired-end sequencing run that are
considered to be PCR duplicates. PCR duplicates are defined as two clusters
from a paired-end run where both clusters have the exact same alignment
positions for each read.
You can expand a chart by clicking on the expand button.
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What is it?
The Mismatch Graph plots the mismatches between a sequence read and a reference
genome after alignment.
When to use it.
To judge the quality of the sequencing run. Poor sequencing runs usually lead to high
numbers of mismatches.
When not to use it.
} When you are using a reference genome that may have a lot of errors or low
confidence stretches.
} When sample and reference are quite different
} In de novo applications.
} In Methyl-seq applications
How to use it
Mismatch refers to any mismatch between sequence read and a reference genome after
alignment.
} Cycle: Plots the % mismatches for all clusters in a run versus cycle
Note that mismatches can be due to two main reasons:
} Sequencing errors (non-specific, random)
} Differences between your sample and the reference genomes
Make sure to keep this in mind when interpreting the mismatch rates.
You can expand a chart by clicking on the expand button.
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Mismatch Graph
Trimmed Lengths
Y Axis
X Axis
Clusters
Trimmed
Lengths
Description
Histogram of reads indicating length at trimming because they
reached adapter.
Run Summary
What is it?
The Run Summary page displays tables with basic data quality metrics summarized per
lane and per read. All the statistics are given as means and standard deviations over the
tiles used in the lane.
When to use it.
When looking at basic data quality metrics for a run from primary analysis.
When not to use it.
The tables do not contain information about samples or projects. The tables also do not
contain app-generated information (secondary or tertiary analysis).
How to use it.
The following metrics are displayed in the top table, split out by read and total:
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Level
The level or read of the run.
Cycles
The number of cycles in the level.
Yield Total
The number of bases sequenced. This is updated as the run
progresses
Projected Total Yield
The projected number of bases expected to be sequenced at
the end of the run.
Yield Perfect
The number of bases in reads that align perfectly, as
determined by a spiked in PhiX control sample. If no PhiX
control sample is run in the lane, this chart is not available.
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The number of bases in reads that align with 3 errors or less,
as determined by a spiked in PhiX control sample. If no PhiX
control sample is run in the lane, this chart is not available,
and will show a zero value.
Aligned
The percentage of the sample that aligned to the PhiX
genome. This is determined for each level or read
independently.
% Perfect [Num Usable Cycles]
The percentage of bases in reads that align perfectly, as
determined by a spiked in PhiX control sample, at the cycle
indicated in the brackets. If no PhiX control sample is run in
the lane, this chart will display 0% but will still show the
number of cycles used.
% <=3 errors [Num Usable
Cycles]
The percentage of bases in reads that align with 3 errors or
less, as determined by a spiked in PhiX control sample, at the
indicated cycle. If no PhiX control sample is run in the lane,
this chart If no PhiX control sample is run in the lane, this
chart will display 0% but will still show the number of cycles
used.
Error Rate
The calculated error rate of the reads that aligned to PhiX.
Intensity Cycle 1
The average of the A channel intensity measured at the first
cycle averaged over filtered clusters.
% Intensity Cycle 20
The corresponding intensity statistic at cycle 20 as a
percentage of that at the first cycle. 100%x(Intensity at cycle
20)/(Intensity at cycle 1).
%Q>=30
The percentage of bases with a quality score of 30 or higher,
th
respectively. This chart is generated after the 25 cycle, and
the values represent the current cycle.
The following metrics are available in the Read tables, split out by lane:
Tiles
The number of tiles per lane.
Density
The density of clusters (in thousands per mm ) detected by
image analysis, +/- one standard deviation.
Clusters PF
The percentage of clusters passing filtering, +/- one standard
deviation.
Phas./Prephas.
The value used by RTA for the percentage of molecules in a
cluster for which sequencing falls behind (phasing) or jumps
ahead (prephasing) the current cycle within a read.
Reads
The number of clusters (in millions).
Reads PF
The number of clusters (in millions) passing filtering.
%Q>=30
The percentage of bases with a quality score of 30 or higher,
th
respectively. This chart is generated after the 25 cycle, and
the values represent the current cycle.
Yield
The number of bases sequenced which passed filter.
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Yield <=3 errors
Cycles Err Rated
The number of cycles that have been error rated with respect
to PhiX starting at cycle 1.
Aligned
The percentage that aligned to the PhiX genome.
Error Rate
The calculated error rate, as determined by the PhiX
alignment. Subsequent columns display the error rate for
cycles 1–35, 1–75, and 1–100.
Intensity Cycle 1
The average of the A channel intensity measured at the first
cycle averaged over filtered clusters.
%Intensity Cycle 20
The corresponding intensity statistic at cycle 20 as a
percentage of that at the first cycle. 100%x(Intensity at cycle
20)/(Intensity at cycle 1).
Indexing QC
What is it?
The Indexing QC page lists count information for indices used in the run as designated
in the sample sheet. Note that the Indexing QC will only be available if the run is an
index run.
When to use it.
Look at this page when you want to see indexing information for a lane after the index
read ias completed.
When not to use it.
This page only provides indexing information. Do not use it for runs that were not
indexed, or to look at other primary, secondary, or tertiary analysis metrics. This is a
quick estimation and may vary slightly from final output.
How to use it.
You can select the displayed lane through the dropdown list.
The first table provides an overall summary of the indexing performance for that lane,
including:
Total Reads
The total number of reads for this lane.
PF Reads
The total number of passing filter reads for this lane.
% Reads Identified (PF)
The total fraction of passing filter reads assigned to an index.
CV
The coefficient of variation for the number of counts across
all indices.
Min
The lowest representation for any index.
Max
The highest representation for any index.
Further information is provided regarding the frequency of individual indices in both
table and graph form. The table contains several columns, including
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A unique number assigned to each index by BaseSpace for
display purposes.
Sample ID
The sample ID assigned to an index in the sample sheet.
Project
The project assigned to an index in the sample sheet.
Index 1 (I7)
The sequence for the first index read.
Index 2 (I5)
The sequence for the second index read.
% Reads Identified (PF)
The number of reads (only includes Passing Filter reads)
mapped to this index.
This information is also displayed in graphical form. In the graphical display, indices
are ordered according to the unique Index Number assigned by BaseSpace.
Charts
The Charts page displays five charts with run metrics. You can expand a chart by
clicking on the expand button.
The following topics provide information about these
charts.
Flow Cell Chart
What is it?
The Flow Cell Chart displays color-coded graphical quality metrics per tile for the entire
flow cell.
When to use it.
Use the Flow Cell Chart to judge local differences per cycle, per lane, or per read in
sequencing metrics on a flow cell. It is also an easy way to see the %Q30 metric, which is
an excellent single metric to judge a run.
When not to use it.
Do not use the Flow Cell Chart to look at secondary or tertiary analysis metrics.
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Index Number
How to use it.
The Flow Cell Chart has the following features:
} You can select the displayed metric, surface, cycle, and base through the dropdown
lists.
} The color bar to the right of the chart indicates the values that the colors represent.
} The chart is displayed with tailored scaling by default.
} Tiles that have not been measured or are not monitored are gray.
You can monitor the following quality metrics with this chart:
Intensity
This chart displays the intensity by color and cycle of the 90%
percentile of the data for each tile.
FWHM
The average full width of clusters at half maximum (in
pixels). Used to display focus quality.
% Base
The percentage of clusters for which the selected base (A, C,
T, or G) has been called.
%Q>20, %Q>30
The percentage of bases with a quality score of >20 or >30,
th
respectively. These charts are generated after the 25 cycle,
and the values represent the current scored cycle.
Median Q-Score
The median Q-Score for each tile over all bases for the
th
current cycle. These charts are generated after the 25 cycle.
This plot is best used to examine the Q-scores of your run as
it progresses. Bear in mind that the %Q30 plot can give an
over simplified view due to its reliance on a single threshold.
Density
The density of clusters for each tile (in thousands per mm ).
Density PF
The density of clusters passing filter for each tile (in
2
thousands per mm ).
Clusters
The number of clusters for each tile (in millions).
Clusters PF
The number of clusters passing filter for each tile (in
millions).
Error Rate
The calculated error rate, as determined by a spiked in PhiX
control sample. If no PhiX control sample is run in the lane,
this chart is not available.
% Phasing, % Prephasing.
The estimated percentage of molecules in a cluster for which
sequencing falls behind (phasing) or jumps ahead
(prephasing) the current cycle within a read.
% Aligned
The percentage of reads from clusters in each tile that aligned
to the PhiX genome.
Perfect Reads
70
2
The percentage of reads that align perfectly, as determined
by a spiked in PhiX control sample. If no PhiX control sample
is run in the lane, this chart is all gray.
Corrected Intensity
The intensity corrected for cross-talk between the color
channels by the matrix estimation and phasing and
prephasing.
Called Intensity
The intensity for the called base.
Signal to Noise
The signal to noise ratio is calculated as mean called intensity
divided by standard deviation of non called intensities.
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Data By Cycle Plot
What is it?
The Data by Cycle plot displays the progression of quality metrics during a run as a line
graph.
When to use it.
Use the Data By Cycle Plot to judge the progression of quality metrics during a run on a
cycle by cycle basis.
When not to use it.
Do not use the Data By Cycle Plot to look at secondary or tertiary analysis metrics, or
aggregate analysis for a whole lane regardless of cycle.
How to use it.
The Data by Cycle plot displays plots that allow you to follow the progression of quality
metrics during a run. These plots have the following features:
} You can select the displayed metric and base through the dropdown lists.
} The symbol in the top right hand corner toggles the plot between pane view and full
screen view.
You can monitor the following quality metrics with this plot:
Intensity
This chart displays the intensity by color and cycle of the 90%
percentile of the data for each tile.
FWHM
The average full width of clusters at half maximum (in
pixels). Used to display focus quality.
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Note the variable scales used on these different parameters.
% Base
The percentage of clusters for which the selected base (A, C,
T, or G) has been called.
%Q>20, %Q>30
The percentage of bases with a quality score of >20 or >30,
th
respectively. These charts are generated after the 25 cycle,
and the values represent the current scored cycle.
Median Q-Score
The median Q-Score for each tile over all bases for the
th
current cycle. These charts are generated after the 25 cycle.
This plot is best used to examine the Q-scores of your run as
it progresses. Bear in mind that the %Q30 plot can give an
over simplified view due to its reliance on a single threshold.
Error Rate
The calculated error rate, as determined by a spiked in PhiX
control sample. If no PhiX control sample is run in the lane,
this chart is not available.
Perfect Reads
The percentage of reads that align perfectly, as determined
by a spiked in PhiX control sample. If no PhiX control sample
is run in the lane, this chart is all gray.
Corrected Intensity
The intensity corrected for cross-talk between the color
channels by the matrix estimation and phasing and
prephasing.
Called Intensity
The intensity for the called base.
Signal to Noise
The signal to noise ratio is calculated as mean called intensity
divided by standard deviation of non called intensities.
You can expand a chart by clicking on the expand button.
QScore Distribution
What is it?
The QScore Distribution Plot displays a bar graph that allows you to view the number of
bases by quality score. The quality score is cumulative for current cycle and previous
cycles, and only bases from reads that pass the quality filter are included.
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Use it to judge the QScore distribution for a run, which is an excellent indicator for run
performance.
When not to use it.
Do not use the QScore Distribution Plot to look at secondary or tertiary analysis metrics,
or metrics other than quality scores.
How to use it.
The QScore Distribution pane displays plots that allow you to view the number of reads
by quality score. The quality score is cumulative for current cycle and previous cycles,
and only reads that pass the quality filter are included.
These plots have the following features:
} You can select the displayed read, and cycle through the dropdown lists.
} The symbol in the top right hand corner toggles the plot between pane view and full
screen view.
Note that the QScore is based on the Phred scale. The following list displays Q-scores
and the corresponding chance that the base call is wrong:
}
}
}
}
Q10: 10% chance of wrong base call
Q20: 1% chance of wrong base call
Q30: 0.1% chance of wrong base call
Q40: 0.01% chance of wrong base call
You can slide the threshold (set at >=Q30 by default) to examine the proportion of bases
at or above any particular Q-score, note that when using Q-score binning this plot will
reflect the subset of Q-scores used.
Data by Lane Plot
What is it?
The Data by Lane Plot displays plots that allow you to view quality metrics per lane.
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When to use it.
When to use it.
Use the Data By Lane Plot to judge the difference in quality metrics between lanes.
When not to use it.
Do not use the Data By Lane Plot to look at secondary or tertiary analysis metrics.
How to use it.
The Data by Lane plots have the following features:
} You can select the displayed metric through the dropdown lists.
} The symbol in the top right hand corner toggles the plot between pane view and full
screen view.
The plots share a number of characteristics.
} The plots show the distribution of mean values for a given parameter across all tiles
in a given lane.
} The red line indicates the median tile value for the parameter displayed.
} Blue boxes are for raw clusters, green boxes for clusters passing filter.
} The box outlines the interquartile range (the middle 50% of the data) for the tiles
analyzed for the data point.
} The error bars delineate the minimum and maximum without outliers.
} The outliers are the values that are more than 1.5 times the interquartile range below
the 25th percentile, or more than 1.5 times the interquartile range above the 75th
percentile. Outliers are indicated as dots.
You can monitor the following quality metrics with this plot:
} The density of clusters for each tile (in thousands per mm2).
} The number of clusters for each tile (in millions).
} The estimated percentage of molecules in a cluster for which sequencing falls behind
(phasing) or jumps ahead (prephasing) the current cycle within a read.
} The percentage of reads from clusters in each tile that aligned to the PhiX genome.
You can expand a chart by clicking on the expand button.
QScore Heatmap
What is it?
A heatmap of the Q-scores.
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Data Reference
When to use it.
For a quick overview of the Q-scores over the cycles.
When not to use it.
Do not use the QScore Distribution Plot to look at secondary or tertiary analysis metrics,
or metrics other than quality scores.
How to use it.
The QScore Heatmap displays plots that allow you to view the QScore by cycle. These
plots have the following features:
} The color bars to the right of each chart indicate the values that the colors represent.
The charts are displayed with tailored scaling; the scale is always 0 to 100% of
maximum value.
} The symbol in the top right hand corner toggles the plot between pane view and full
screen view.
You can expand a chart by clicking on the expand button.
BaseSpace Files
BaseSpace uses and produces a variety of files. See the topics in this section for details.
Sample Sheet
What is it?
The sample sheet is a comma-delimited file (SampleSheet.csv) that stores the information
needed to set up and analyze a sequencing experiment. The file includes a list of
samples and their index sequences, as well as the workflow to be employed by
BaseSpace.
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When to use it.
Every run in BaseSpace needs to have an associated sample sheet in order to define
projects and samples, assign indices, and run sample sheet driven workflow apps.
When not to use it.
Not applicable; you will always employ a sample sheet with BaseSpace.
How to use it
The following table is for reference purposes only. For details about creating or modifying
a sample sheet, see the MiSeq Reporter User Guide, MiSeq Sample Sheet Quick Reference
Guide or HiSeq User Guide. You can create a sample sheet using the Illumina Experiment
Manager Software.
Table 1 Sample Sheet Fields
76
Row
Description
Investigator
Name
(Optional) The name of the investigator.
Project Name
(Optional) A descriptive name of the run.
Experiment
Name
(Optional) A descriptive name of the experiment.
Date
The date the sequencing run was performed.
Workflow
The analysis workflow for the run.
Manifests
This section is only used by the Amplicon workflow and is
the name of the file (provided by Illumina or created by
IEM) used in the Amplicon Workflow. It is required for the
Amplicon workflow and ignored by other workflows. The
file specifies the alignments to a reference and the targeted
reference regions used in the Amplicon workflow.
Part # 15050652 Rev. A
Description
Site Reports
This section is optional and used by only the Resequencing
and Custom Amplicon workflows. Each line below the
SiteReports section header is the name of a SiteReport
Input File. This file designates positions on a given
chromosome to report the genotype found at that position.
Data
• Contaminants – The path to the folder containing FASTA
files of contaminants (used only for SmallRNA)
• GenomePath – The reference genome folder containing
the FASTA files to be used in the alignment step
• Index – Represents the sequence string of a sample's first
index. Valid characters in this string are A, C, G, T and N.
'N' matches any base.
• Index2 – Represents the sequence string of this sample's
second index. Valid characters in this string are A,C,G,T
and N. 'N' matches any base.
• MiRNA – The path to the folder containing FASTA files
of mature miRNAs (used only for SmallRNA)
• RNA – The path to the folder containing FASTA files of
small RNAs (used only for SmallRNA)
• SampleID – A string identifier for the sample. This is
usually a bar code but can have any value. Letters and
numbers only; some special characters can be detrimental
for file creation.
• Manifest – The manifest file letter as designated by the
manifest field.
• Name – A string identifier for the sample. This is used in
the reporting web page.
Data Reference
Row
BAM Files
What is it?
The Sequence Alignment/Map (SAM) format is a generic alignment format for storing
read alignments against reference sequences, supporting short and long reads (up to 128
Mb) produced by different sequencing platforms. SAM is a text format file that is humanreadable. The Binary Alignment/Map (BAM) keeps exactly the same information as
SAM, but in a compressed, binary format that is only machine-readable.
When to use it.
Allows you to see alignments. Use it for direct interpretation or as a starting point for
tertiary analysis with downstream analysis tools that are compatible with BAM. BAM
files are suitable for viewing with an external viewer such as IGV or the UCSC genome
browser.
When not to use it.
Do not use it with tools that are not compatible with the BAM format, or with
applications that cannot handle large files, as BAM files can get big, depending on the
application and data.
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How to use it
If you use an app in BaseSpace that uses BAM files as input, the app will locate the file
when launched. If using BAM files in other tools, download the file to use it in the
external tool.
Detailed Description
Go to http://samtools.sourceforge.net/SAM1.pdf to see the exact SAM specification.
gVCF Files
What is it?
This application also produces the genome Variant Call Format file (gVCF). gVCF was
developed to store sequencing information for both variant and non-variant positions,
which is required for human clinical applications. gVCF is a set of conventions applied
to the standard variant call format (VCF) 4.1 as documented by the 1000 Genomes
Project. These conventions allow representation of genotype, annotation, and other
information across all sites in the genome in a compact format. Typical human whole
genome sequencing results expressed in gVCF with annotation are less than 1 Gbyte, or
about 1/100 the size of the BAM file used for variant calling. If you are performing
targeted sequencing, gVCF is also an appropriate choice to represent and compress the
results.
gVCF is a text file format, stored as a gzip compressed file (*.genome.vcf.gz).
Compression is further achieved by joining contiguous non-variant regions with similar
properties into single ‘block’ VCF records. To maximize the utility of gVCF, especially for
high stringency applications, the properties of the compressed blocks are conservative -thus block properties like depth and genotype quality reflect the minimum of any site in
the block. The gVCF file can be indexed (creating a .tbi file) and used with existing VCF
tools such as tabix and IGV, making it convenient both for direct interpretation and as a
starting point for tertiary analysis.
For more information, see https://sites.google.com/site/gvcftools/home/about-gvcf.
When to use it.
Use it for direct interpretation or as a starting point for tertiary analysis with
downstream analysis that is compatible with gVCF, such as tabix and IGV.
When not to use it.
Do not use it with tools that are not compatible with the gVCF format.
How to use it
Apps that use gVCF files find it when kicked off and directed to the sample. If using
gVCF files in other tools, download the file to use it in the outside tool.
Detailed Description
The following conventions are used in the variant caller gVCF files.
Samples per File
There is only one sample per gVCF file.
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Part # 15050652 Rev. A
Contiguous non-variant segments of the genome can be represented as single records in
gVCF. These records use the standard 'END' INFO key to indicate the extent of the
record. Even though the record can span multiple bases, only the first base is provided
in the REF field to reduce file size.
The following is a simplified segment of a gVCF file, describing a segment of non-variant
calls (starting with an A) on chromosome 1 from position 51845 to 51862.
##INFO=<ID=END,Number=1,Type=Integer,Description="End position
of the variant described in this record">#CHROM POS ID REF
ALT QUAL FILTER INFO FORMAT NA19238chr1 51845 . A . . PASS
END=51862
Any fields provided for a block of sites, such as read depth (using the DP key), will show
the minimum value observed among all sites encompassed by the block. Each sample
value shown for the block, such as the depth (using the DP key), is restricted to a range
where the maximum value is within 30% or 3 of the minimum, i.e. for sample value
range [x,y], y <= x+max(3,x*0.3). This range restriction applies to each of the sample
values printed out in the final block record.
Indel Regions
Note that sites which are "filled in" inside of deletions have additional changes:
All deletions:
} Sites inside of any deletion are marked with the deletion's filters, in addition to any
filters which have already been applied to the site.
} Sites inside of deletions cannot have a genotype or alternate allele quality score
higher than the corresponding value from the enclosing indel.
Heterozygous deletions:
} Sites inside of heterozygous deletions are altered to have haploid genotype entries
(e.g. "0" instead of "0/0", "1" instead of "1/1").
} Heterozygous SNV calls inside of heterozygous deletions are marked with the
"SiteConflict" filter and their genotype is unchanged.
Homozygous deletions:
} Homozygous reference and no-call sites inside of homozygous deletions have
genotype "."
} Sites inside of homozygous deletions which have a non-reference genotype are
marked with a “SiteConflict” filter, and their genotype is unchanged.
} Site and genotype quality are set to "."
The above modifications reflect the notion that the site confidence is bound by the
enclosing indel confidence.
Also note that on occasion, the variant caller will produce multiple overlapping indel
calls which cannot be resolved into two haplotypes. If this occurs all indels and sites in
the region of the overlap will be marked with the “IndelConflict” filter (see below).
Genotype Quality for Variant and Non-variant Sites
The gVCF file uses an adapted version of genotype quality for variant and non-variant
site filtration. This value is associated with the key GQX. The GQX value is intended to
represent the minimum of {Phred genotype quality assuming the site is variant, Phred
genotype quality assuming the site is non-variant}. The reason for using this is to allow a
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Data Reference
Non-Variant Blocks Using END Key
single value to be used as the primary quality filter for both variant and non-variant
sites. Filtering on this value corresponds to a conservative assumption appropriate for
applications where reference genotype calls must be determined at the same stringency
as variant genotypes, i.e.:
} An assertion that a site is homozygous reference at GQX >= 30 is made assuming the
site is variant.
} An assertion that a site is a non-reference genotype at GQX >= 30 is made assuming
the site is non-variant.
Section Descriptions
The gVCF file contains the following sections:
} Meta-information lines start with ## and contain meta-data, config information, and
define the values that the INFO, FILTER and FORMAT fields can have.
} The header line starts with # and names the fields that the data lines use. These are
#CHROM, POS, ID,REF, ALT, QUAL, FILTER, INFO, FORMAT, followed by one or
more sample columns.
} Data lines that contain information about one or more positions in the genome.
Note that if you extract the variant lines from a gVCF file, you produce a conventional
variant VCF file.
Field Descriptions
The fixed fields #CHROM, POS, ID, REF, ALT, QUAL are defined in the VCF 4.1
standard provided by the 1000 Genomes Project, while the fields ID, INFO, FORMAT,
and sample are described in the meta-information. Descriptions are provided below.
} CHROM: Chromosome: an identifier from the reference genome or an anglebracketed ID String ("<ID>") pointing to a contig.
} POS: Position: The reference position, with the 1st base having position 1. Positions
are sorted numerically, in increasing order, within each reference sequence CHROM.
There can be multiple records with the same POS. Telomeres are indicated by using
positions 0 or N+1, where N is the length of the corresponding chromosome or
contig.
} ID: Semi-colon separated list of unique identifiers where available. If this is a dbSNP
variant it is encouraged to use the rs number(s). No identifier should be present in
more than one data record. If there is no identifier available, then the missing value
should be used.
} REF: Reference base(s): A,C,G,T,N; there can be multiple bases. The value in the POS
field refers to the position of the first base in the string. For simple insertions and
deletions in which either the REF or one of the ALT alleles would otherwise be
null/empty, the REF and ALT strings include the base before the event (which is
reflected in the POS field), unless the event occurs at position 1 on the contig in
which case they include the base after the event. If any of the ALT alleles is a
symbolic allele (an angle-bracketed ID String "<ID>") then the padding base is
required and POS denotes the coordinate of the base preceding the polymorphism.
} ALT: Comma separated list of alternate non-reference alleles called on at least one of
the samples. Options are:
• Base strings made up of the bases A,C,G,T,N
• angle-bracketed ID String (”<ID>”)
• breakend replacement string as described in the section on breakends.
If there are no alternative alleles, then the missing value should be used.
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Data Reference
} QUAL: Phred-scaled quality score for the assertion made in ALT. i.e. -10log_10 prob
(call in ALT is wrong). If ALT is ”.” (no variant) then this is -10log_10 p(variant),
and if ALT is not ”.” this is -10log_10 p(no variant). High QUAL scores indicate
high confidence calls. Although traditionally people use integer phred scores, this
field is permitted to be a floating point to enable higher resolution for low confidence
calls if desired. If unknown, the missing value should be specified. (Numeric)
} FILTER: PASS if this position has passed all filters, i.e. a call is made at this
position. Otherwise, if the site has not passed all filters, a semicolon-separated list of
codes for filters that fail. gVCF files use the following values:
• PASS: position has passed all filters.
• IndelConflict: Locus is in region with conflicting indel calls.
• SiteConflict: Site genotype conflicts with proximal indel call. This is typically a
heterozygous SNV call made inside of a heterozygous deletion.
• LowGQX: Locus GQX (minimum of {Genotype quality assuming variant
position,Genotype quality assuming non-variant position}) is less than 30 or not
present.
• HighDPFRatio: The fraction of basecalls filtered out at a site is greater than 0.3.
• HighSNVSB: SNV strand bias value (SNVSB) exceeds 10. High strand bias
indicates a potential high false-positive rate for SNVs.
• HighSNVHPOL: SNV contextual homopolymer length (SNVHPOL) exceeds 6.
• HighREFREP: Indel contains an allele which occurs in a homopolymer or
dinucleotide track with a reference repeat greater than 8.
• HighDepth: Locus depth is greater than 3x the mean chromosome depth.
} INFO: Additional information. INFO fields are encoded as a semicolon-separated
series of short keys with optional values in the format: <key>=<data>[,data]. gVCF
files use the following values:
• END: End position of the region described in this record.
• BLOCKAVG_min30p3a: Non-variant site block. All sites in a block are
constrained to be non-variant, have the same filter value, and have all sample
values in range [x,y], y <= max(x+3,(x*1.3)). All printed site block sample values
are the minimum observed in the region spanned by the block.
• SNVSB: SNV site strand bias.
• SNVHPOL: SNV contextual homopolymer length.
• CIGAR: CIGAR alignment for each alternate indel allele.
• RU: Smallest repeating sequence unit extended or contracted in the indel allele
relative to the reference. RUs are not reported if longer than 20 bases.
• REFREP: Number of times RU is repeated in reference.
• IDREP: Number of times RU is repeated in indel allele.
} FORMAT: Format of the sample field. FORMAT specifies the data types and order of
the subfields. gVCF files use the following values:
• GT: Genotype.
• GQ: Genotype Quality.
• GQX: Minimum of {Genotype quality assuming variant position,Genotype
quality assuming non-variant position}.
• DP: Filtered basecall depth used for site genotyping.
• DPF: Basecalls filtered from input before site genotyping.
• AD: Allelic depths for the ref and alt alleles in the order listed. For indels this
value only includes reads which confidently support each allele (posterior
probability 0.999 or higher that read contains indicated allele vs all other
intersecting indel alleles).
• DPI: Read depth associated with indel, taken from the site preceding the indel.
} SAMPLE: Sample fields as defined by the header.
VCF Files
What is it?
VCF is a text file format which contains information about variants found at specific
positions in a reference genome. The file format consists of meta-information lines, a
header line, and then data lines. Each data line contains information about a single
variant.
When to use it.
Use it for direct interpretation or as a starting point for tertiary analysis with
downstream analysis that are compatible with VCF, such as IGV or the UCSC genome
browser.
When not to use it.
Do not use it with tools that are not compatible with the VCF format.
NOTE
Windows recognizes vcf files as an Outlook contact file. Do not open VCF files in Outlook.
How to use it
If you use an app in BaseSpace that uses VCF files as input, the app will locate the file
when launched. If using VCF files in other tools, download the file to use it in the
external tool.
Detailed Description
The file naming convention for VCF files is as follows: SampleName_S#.vcf (where # is
the sample number determined by ordering in the sample sheet).
The header of the VCF file describes the tags used in the remainder of the file and has
the column header:
##fileformat=VCFv4.1
##fileDate=20120317
##source=SequenceAnalysisReport.vshost.exe
##reference=
##phasing=none
##INFO=<ID=DP,Number=1,Type=Integer,Description="Total Depth">
##INFO=<ID=TI,Number=.,Type=String,Description="Transcript ID">
##INFO=<ID=GI,Number=.,Type=String,Description="Gene ID">
##INFO=<ID=CD,Number=0,Type=Flag,Description="Coding Region">
##FILTER=<ID=q20,Description="Quality below 20">
##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype">
##FORMAT=<ID=GQ,Number=1,Type=Integer,Description="Genotype
Quality">
#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT SAMPLE
A sample line of the VCF file is shown below. The data that is used to populate each
column is also described:
chr22 16285888 rs76548004 T C 17 d15;q20 DP=11;TI=NM_
001136213;GI=POTEH;CD GT:GQ 1/0:17
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Description
ALT
The allele(s) that differ from the reference read. For example, an insertion of a
single T could be represented by reference A and alternate AT.
CHROM
The chromosome of the reference genome. Chromosomes appear in the same
order as the reference FASTA file (generally karyotype order)
FILTER
If all filters are passed, the' PASS' is written. The possible filters are as follows:
• q20 – The variant score is less than 20. (Configurable using the
VariantFilterQualityCutoff setting in the config file)
• r8 – For an Indel, the number of repeats in the reference (of a 1- or 2-base
repeat) is greater than 8. (Configurable using the IndelRepeatFilterCutoff
setting in the config file)
FORMAT
The format column lists fields (separated by colons), for example, "GT:GQ". The
list of fields provided depends on the variant caller used. The available fields are
as follow:
AD – Entry of the form X,Y where X is the number of reference calls, Y the
number of alternate calls
GQ – Genotype quality
GT – Genotype. 0 corresponds to the reference base, 1 corresponds to the first
entry in the ALT column, 2 corresponds to the second entry in the ALT column,
etc. The '/' indicates that there is no phasing information.
NL – Noise level; an estimate of base calling noise at this position
SB – Strand bias at this position. Larger negative values indicate more bias;
values near zero indicate little strand bias.
VF – Variant frequency. The percentage of reads supporting the alternate allele.
ID
The rs number for the snp obtained from dbSNP. If there are multiple rs
numbers at this location, the list is semi-colon delimited. If no dbSNP entry exists
at this position, the missing value ('.') is used.
INFO
These are the possible entries in the INFO column:
• AD – Entry of the form X,Y where X is the number of reference calls, Y the
number of alternate calls.
• CD – A flag indicating that the snp occurs within the coding region of at least
one refGene entry
• DP – The depth (number of base calls aligned to a this position)
• GI – A comma separated list of gene IDs read from refGene
• NL – Noise level; an estimate of base calling noise at this position.
• TI – A comma separated list of transcript IDs read from refGene
• SB – Strand bias at this position.
• VF – Variant frequency. The number of reads supporting the alternate allele.
POS
The 1-based position of this variant in the reference chromosome. The
convention for .vcf files is that, for SNPs, this is the reference base with the
variant; for indels or deletions, this is the reference base immediately before the
variant. Variants are ordered by position.
QUAL
A phred-scaled quality score assigned by the variant caller. Higher scores
indicate higher confidence in the variant (and lower probability of errors). For a
quality score of Q, the estimated probability of an error is 10-(Q/10). For
example, the set of Q30 calls should have a 0.1% error rate. Note that many
variant callers assign quality scores (based on their statistical models) which are
high relative to the error rate observed in practice.
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Data Reference
Setting
Setting
Description
REF
The reference genotype. For example, a deletion of a single T could be
represented by reference TT and alternate T.
SAMPLE
The sample column gives the values specified in the FORMAT column. One
MAXGT sample column is provided for the normal genotyping (assuming the
reference). For reference, a second column is provided for genotyping assuming
the site is polymorphic. See the Starling documentation for more details.
NOTE
Variant files for Isaac also contain off-target variant calls, with filter.
FASTQ Files
What is it?
BaseSpace converts *.bcl files into FASTQ files, which contains base call and quality
information for all reads passing filtering.
When to use it.
FASTQ files can be used as sequence input for alignment and other secondary analysis
software.
When not to use it.
Do not use it with tools that are not compatible with the FASTQ format.
How to use it
BaseSpace automatically generates FASTQ files in sample sheet-driven workflow apps.
Other apps that perform alignment and variant calling also automatically use FASTQ
files.
A detailed description of the FASTQ format is provided below.
Naming
FASTQ files are named with the sample name and the sample number, which is a
numeric assignment based on the order that the sample is listed in the sample sheet. For
example:
Data\Intensities\BaseCalls\samplename_S1_L001_R1_001.fastq.gz
• samplename—The sample name provided in the sample sheet. If a sample
name is not provided, the file name includes the sample ID, which is a required
field in the sample sheet and must be unique.
• S1—The sample number based on the order that samples are listed in the
sample sheet starting with 1. In this example, S1 indicates that this sample is the
first sample listed in the sample sheet.
NOTE
Reads that cannot be assigned to any sample are written to a FASTQ file for sample
number 0, and excluded from downstream analysis.
• L001—The lane number.
• R1—The read. In this example, R1 means Read 1. For a paired-end run, there is
at least one file with R2 in the file name for Read 2.
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Data Reference
• 001—The last segment is always 001.
Compression
FASTQ files are saved compressed in the GNU zip format (an open source file
compression program), indicated by the .gz file extension.
Format
Each entry in a FASTQ file consists of four lines:
} Sequence identifier
} Sequence
} Quality score identifier line (consisting only of a +)
} Quality score
Each sequence identifier, the line that precedes the sequence and describes it, is in the
following format:
@<instrument>:<run number>:<flowcell ID>:<lane>:<tile>:<xpos>:<y-pos> <read>:<is filtered>:<control number>:<sample
number>
The following table describes the elements:
Element
Requirements
@
@
<instrument> Characters allowed:
a–z, A–Z, 0–9 and
underscore
<run number> Numerical
<flowcell
Characters allowed:
ID>
a–z, A–Z, 0–9
<lane>
Numerical
<tile>
Numerical
<x_pos>
Numerical
<y_pos>
Numerical
<read>
Numerical
<is
filtered>
<control
number>
<sample
number>
Y or N
Numerical
Numerical
Description
Each sequence identifier line starts with @
Instrument ID
Run number on instrument
Lane number
Tile number
X coordinate of cluster
Y coordinate of cluster
Read number. 1 can be single read or Read 2 of
paired-end
Y if the read is filtered (did not pass), N otherwise
0 when none of the control bits are on, otherwise it
is an even number.
Sample number from samplesheet
An example of a valid entry is as follows; note the space preceding the read number
element:
@SIM:1:FCX:1:15:6329:1045 1:N:0:2
TCGCACTCAACGCCCTGCATATGACAAGACAGAATC
+
<>;##=><9=AAAAAAAAAA9#:<#<;<<<????#=
Control Values
If the read is not identified as a control, then the tenth column (<control number>) is
zero. If the read is identified as a control, the number is greater than zero, and the value
specifies what type of control it is. The value is the decimal representation of a bit-wise
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85
encoding scheme. In that scheme bit 0 has a decimal value of 1, bit 1 a value of 2, bit 2 a
value of 4, and so on.
The bits are used as follows:
• Bit 0: always empty (0)
• Bit 1: was the read identified as a control?
• Bit 2: was the match ambiguous?
• Bit 3: did the read match the phiX tag?
• Bit 4: did the read align to match the phiX tag?
• Bit 5: did the read match the control index sequence?
• Bits 6, 7: reserved for future use
• Bits 8–15: the report key for the matched record in the controls.fasta file (specified by the
REPORT_KEY metadata)
Quality Scores
A quality score (or Q-score) expresses an error probability. In particular, it serves as a
convenient and compact way to communicate very small error probabilities.
Given an assertion, A, the quality score, Q(A), expresses the probability that A is not true,
P(~A), according to the relationship:
Q(A) =-10 log10(P(~A))
where P(~A) is the estimated probability of an assertion A being wrong.
The relationship between the quality score and error probability is demonstrated with the
following table:
Quality score, Q
(A)
10
20
30
Error probability, P
(~A)
0.1
0.01
0.001
Quality Scores Encoding
In FASTQ files, quality scores are encoded into a compact form, which uses only 1 byte
per quality value. In this encoding, the quality score is represented as the character with
an ASCII code equal to its value + 33. The following table demonstrates the relationship
between the encoding character, its ASCII code, and the quality score represented.
Table 2 ASCII Characters Encoding Q-scores 0–40
Symbol ASCII
QSymbol ASCII
Code
Score
Code
!
33
0
/
47
"
34
1
0
48
#
35
2
1
49
$
36
3
2
50
%
37
4
3
51
&
38
5
4
52
'
39
6
5
53
(
40
7
6
54
)
41
8
7
55
*
42
9
8
56
86
QScore
14
15
16
17
18
19
20
21
22
23
Symbol
=
>
?
@
A
B
C
D
E
F
ASCII
Code
61
62
63
64
65
66
67
68
69
70
QScore
28
29
30
31
32
33
34
35
36
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+
,
.
ASCII
Code
43
44
45
46
QScore
10
11
12
13
Symbol
9
:
;
<
ASCII
Code
57
58
59
60
QScore
24
25
26
27
Symbol
G
H
I
ASCII
Code
71
72
73
QScore
38
39
40
Health Runs
What is it?
A user can choose whether or not to send anonymous system health information to
Illumina. Health runs help Illumina diagnose issues and improve our products. The
information consists of interop files and log files, and is not tied to any user account.
This option is on by default.
Sample Details Page Components
The Sample Details Page shows metrics for a sample that are generated by the app that
ran the analysis. Different panes are displayed on this page depending on the app; for
descriptions, see topics below.
Samples Table
The samples table contains general analysis information for the sample. Depending on
the workflow, the following metrics can be shown:
Column
Description
Sample Name
The sample name from the sample sheet.
Sample ID
The sample ID from the sample sheet. Sample ID must always be a unique
value.
Genome
The name of the reference genome.
Chr
The reference target or chromosome name.
Cluster PF
The number of clusters passing filter for the sample that aligned to the
reference genome.
Mismatch
The percentage mismatch to reference averaged over cycles per read
(Read 1/Read 2).
No Call
The percentage of bases that could not be called (no-call) for the sample
averaged over cycles per read (Read 1/Read 2).
Coverage
Median coverage (number of bases aligned to a given reference position)
averaged over all positions.
Het SNPs
The number of heterozygous SNPs detected for the sample.
Hom SNPs
The number of homozygous SNPs detected for the sample.
Insertions
The number of insertions detected for the sample.
Deletions
The number of deletions detected for the sample.
BaseSpace User Guide for NextSeq, Miseq, and HiSeq
87
Data Reference
Symbol
The workflows apps Small RNA and De Novo Assembly have custom samples tables,
these are decribed here:
} Small RNA Samples Table on page 88
} De Novo Assembly Samples Table on page 88
Small RNA Samples Table
Column
Description
Sample Name
The sample name from the sample sheet.
Cluster Raw
The number of raw clusters detected for the sample.
Cluster PF
The number of clusters passing filter for the sample.
Cluster Align
Contam
The number of clusters that match records in the Contaminants
database.
Cluster Align
miRNA
The number of clusters that exactly match records in the Mature miRNA
database.
Cluster Align
RNA
The number of clusters that match records in the RNA database.
Cluster Align
Genome
The number of clusters that match records in the genomic database.
Cluster Unaligned
The number of clusters that did not align against any reference
database.
De Novo Assembly Samples Table
88
Column
Description
Sample Name
The sample name from the sample sheet.
Num Contigs
The number of contigs assembled for this sample.
Mean Contig
Length
The average contig length for this sample.
Median Contig
Length
The median contig length for this sample.
Min Contig
Length
The minimum contig length for this sample.
Max Contig
Length
The maximum contig length for this sample.
Base Count
The total length of the resulting assembly.
N50
N50 length is the length of the shortest contig such that the sum of
contigs of equal length or longer is at least 50% of the total length of all
contigs.
Part # 15050652 Rev. A
Data Reference
Amplicons Table
Column
Description
#
An ordinal identification number in the table.
Amplicons
The amplicon name.
Location
The position at which the variant was found.
Variants #
The number of variants for this amplicon.
Coverage Graph
Y Axis
X Axis
Description
Coverage
Position
The green curve is the number of aligned reads that cover each
position in the reference.
The red curve is the number of aligned reads that have a miscall at
this position in the reference. SNPs and other variants show up as
spikes in the red curve.
Q-Score Graph
Y Axis
X Axis
QScore
Position
Description
The average quality score of bases at the given position of the reference.
Variant Score Graph
Y Axis X Axis
Score
Position
Description
Graphically depicts quality score and the position of SNPs and indels.
Variants Table
The variants table shows variants for you sample per chromosome or amplicon.
Column
Description
#
An ordinal identification number in the table.
Location
The position at which the variant was found.
Score
The quality score for this variant.
Type
The variant type, which can be either SNP or indel.
Call
A string representing how the base or bases changed at this location in the
reference.
dbSNP
The dbSNP name of the variant, if applicable.
BaseSpace User Guide for NextSeq, Miseq, and HiSeq
89
Column
Description
RefGene
The gene according to RefGene in which this variant appears.
Frequency
The fraction of reads for the sample that includes the variant. For example,
if the reference base is A, and sample 1 has 60 A reads and 40 T reads, then
the SNP has a variant frequency of 0.4.
Depth
The number of reads for a sample covering a particular position. The
GATK variant caller subsamples data in regions of high coverage.
Filter
The criteria for a filtered variant.
Small RNA Pie Chart
The Small RNA pie chart provides a visualization of clusters identified as mature
miRNA, other forms of RNA, genomic sequence, or contaminants.
Figure 13 Small RNA Pie Chart
Common categories for the Small RNA pie chart are as follows:
} Unaligned clusters that did not align against any reference
} Genome clusters that aligned to the reference genome
} miRNA clusters that aligned to the mature miRNA database
Hits to the mature miRNA database are counted only if the cluster aligned to the correct
strand and position for the mature miRNA.
The remaining category names in the Small RNA pie chart are taken from the FASTA file
names in the databases. For example, if the RNA database contains a file named
rRNA.fa, then matches to this file are reported as the category rRNA.
Small RNA Graph
The Small RNA graph provides a plot of the common mature miRNA sequences for a
sample and their abundances. The most common miRNA sequences for the selected
sample (up to ten records) are shown in proportion to the number of clusters matched.
Metagenomics Pie Chart
The Metagenomics pie chart provides a visualization of how many clusters from each
sample were assigned to a category in each taxonomic level.
Click another row in the taxonomy table to change the pie chart to that sample or
taxonomic level.
90
Part # 15050652 Rev. A
Contigs are arranged end-to-end along the X axis and the reference chromosomes are
arranged bottom-to-top along the Y axis. Each pixel of the plot is colored according to
how many short sequences of the corresponding contig have a match in the
corresponding portion of the reference genome.
An identical assembly results in a diagonal line. A vertical gap in the plot might indicate
a portion of the reference that is absent in the assembly, such as a plasmid, which is
found in some bacteria populations.
Y Axis
X Axis
Reference
Assembly
Position
Description
A syntenic plot of assembled contigs compared to a reference. A
reference genome must be specified in the sample sheet.
Sample QC Table
Column
Description
Sample Name
The sample name from the sample sheet.
Clusters Count
The number of clusters sequenced for this sample.
Clusters
Percentage
The percentage of the total cluster number matching the index for this
sample.
Pass Filter
The percentage of clusters passing filter for this sample.
Alignment
R1/R2
The percentage of clusters successfully aligned in Read 1/ read 2.
Length Median
The median fragment length for the sample.
Length Min
The low percentile of fragment lengths for this sample as they correspond
to three standard deviations from the median.
Length Max
The high percentile of fragment lengths for this sample as they
correspond to three standard deviations from the median.
Mismatch
R1/R2
The percentage mismatch to reference averaged over cycles per read
(Read 1/Read 2).
Estimated
Diversity
An estimate of the total library diversity derived from the observed
diversity and the number of apparent PCR duplicates. This calculation is
available for paired-end runs unless PCR duplicate flagging was disabled
in the sample sheet.
Observed
Diversity
Number of distinct aligned positions. Reads with the same aligned
positions are assumed to be PCR duplicates. PCR duplicates are defined as
sequences with identical Read 1 and Read 2 start sites.
Isaac App Results Page
The Isaaac App Results Page consists of three panes, which are described in the topics
below.
BaseSpace User Guide for NextSeq, Miseq, and HiSeq
91
Data Reference
Samples Graph
Isaac Alignment Statistics
Isaac Alignment Statistics display alignment information for the sample.
Column
Description
Number of
Reads
The number of reads sequenced for this sample.
Coverage
Median coverage (number of bases aligned to a given reference position)
averaged over all positions.
Fragment Length
Median
The median fragment length for the sample.
Fragment Length
Standard
Deviation
The standard deviation of the fragment length for the sample.
Aligned %
The total count of PF clusters aligning for the sample (Read 1/Read 2).
Mismatch
The percentage mismatch to reference averaged over cycles per read
(Read 1/Read 2).
Isaac Variants Statistics
The variants table shows three tables of variant statistics, for Single Nucleotide Variants
(SNVs), insertions, and deletions. The rows contain the following information.
Column
Description
Total number
Total numbers of the specific variant.
Het/Hom Ratio
The ratio between heterozygote and homozygote variants.
% in dbSNP 131
The percentage of the specific variants found in dbSNP 131.
Transitions /
Transversions
The ratio of transitions (A-G or C-T changes) to transversions (other
changes).
Isaac Coverage Graph
92
Y Axis
X Axis
Description
# Reference
Bases
Read
Depth
The coverage graph displays the number of bases that are covered
at each read depth.
Part # 15050652 Rev. A
For technical assistance, contact Illumina Technical Support.
Table 3 Illumina General Contact Information
Illumina Website
Email
www.illumina.com
[email protected]
Table 4 Illumina Customer Support Telephone Numbers
Region
Contact Number
Region
North America
1.800.809.4566
Italy
Austria
0800.296575
Netherlands
Belgium
0800.81102
Norway
Denmark
80882346
Spain
Finland
0800.918363
Sweden
France
0800.911850
Switzerland
Germany
0800.180.8994
United Kingdom
Ireland
1.800.812949
Other countries
Contact Number
800.874909
0800.0223859
800.16836
900.812168
020790181
0800.563118
0800.917.0041
+44.1799.534000
Safety Data Sheets
Safety data sheets (SDSs) are available on the Illumina website at
www.illumina.com/msds.
Product Documentation
Product documentation in PDF is available for download from the Illumina website. Go
to www.illumina.com/support, select a product, then click Documentation & Literature.
BaseSpace User Guide for NextSeq, Miseq, and HiSeq
Technical Assistance
Technical Assistance
Illumina
San Diego, California 92122 U.S.A.
+1.800.809.ILMN (4566)
+1.858.202.4566 (outside North America)
[email protected]
www.illumina.com
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