Chapter 2

Chapter 2
Chapter 2
CORE TF: a User-Friendly
Interface to Identify
Evolutionary Conserved
Transcription Factor Binding
Sites in Sets of Co-Regulated
Genes
Matthew S. Hestand, Michiel van Galen, Michel P. Villerius,
Gert-Jan B. van Ommen, Johan T. den Dunnen, Peter A.C. ’t Hoen
The Center for Human and Clinical Genetics, Leiden University Medical Center, Postzone
S4-0P, PO Box 9600, 2300 RC Leiden, The Netherlands.
BMC Bioinformatics 2008, 9:495
Parts of this manuscript have been adapted to more appropriately fit this thesis.
19
2 CORE TF
2.1
Abstract
Background: The identification of transcription factor binding sites is difficult since
they are only a small number of nucleotides in size, resulting in large numbers of false
positives and false negatives in current approaches. Computational methods to reduce
false positives are to look for over-representation of transcription factor binding sites
in a set of similarly regulated promoters or to look for conservation in orthologous
promoter alignments.
Results: We have developed a novel tool, ”CORE TF” (Conserved and Over-REpresented Transcription Factor binding sites) that identifies common transcription factor
binding sites in promoters of co-regulated genes. To improve upon existing binding
site predictions, the tool searches for position weight matrices from the TRANSFACR
database that are over-represented in an experimental set compared to a random set
of promoters and identifies cross-species conservation of the predicted transcription
factor binding sites. The algorithm has been evaluated with expression and chromatinimmunoprecipitation on microarray data. We also implement and demonstrate the
importance of matching the random set of promoters to the experimental promoters
by GC content, which is a unique feature of our tool.
Conclusion: The program CORE TF is accessible in a user friendly web interface at
http://www.LGTC.nl/CORE TF. It provides a table of over-represented transcription factor binding sites in the users input genes’ promoters and a graphical view
of evolutionary conserved transcription factor binding sites. In our test data sets it
successfully predicts target transcription factors and their binding sites.
20
2.2 Background
2.2
Background
There are both experimental and computational approaches to identify transcription
factors (TFs) and their relevant binding sites. In the wet lab, hypothesis driven
techniques, such as deletion constructs with luciferase reporter assays and chromatinimmunoprecipitation on microarrays (ChIP-on-chip), can be used to identify TF binding site (TFBS) regions. Luciferase assays can prove that a specific region has regulatory function, but they are laborious and time consuming. ChIP-on-chip is more
global, but requires prior knowledge of which TF to target using a specific antibody
and is laborious, time consuming, and expensive. Faster and cheaper in silico methods have been in development which can identify potential TFs and their binding
sites. They also tend to target more precise the TFBS instead of just containing a
TFBS region. However, finding TFBSs can be extremely difficult since they may be
less than 12-14 bp long and their consensus binding sites may be fairly loose (49).
One method to identify TFBSs for known TFs is using position weight matrices
(PWMs) (50). PWMs summarize experimental information on the sequence preference of TFs. TRANSFAC (51; 52) is the leading PWM database for TFBSs with
834 matrices in total (release 11.4, December 2007), compared to 123 in JASPAR
(53; 54).
An additional method to look for new (de novo) TFBSs is by searching for conservation between orthologous promoters (58). This is based on the presumption that
functional elements are evolutionary conserved since mutations to such elements could
be detrimental to the organism (58; 59).
However, both the sequence conservation-based and the PWM approach alone
produce many false positives and false negatives. We therefore created CORE TF, a
program using both methods to reduce false predictions. We first look for TFs involved
in a biological process of interest, relying on the presumption that similarly expressed
genes have common TFs as regulators. To do this, and reduce false predictions with
PWMs, we search for TFBSs that occur more often in a co-regulated set of promoters
compared to random promoters. This algorithm, in analogy to the work of Elkon
et al, 2003 (57), implements a binomial test to evaluate for this over-representation.
Some PWMs have a bias towards certain nucleotides, such as T’s and A’s for a TATA
box binding TF and would therefore likely be over-represented if an experimental
set had high numbers of T’s and A’s and the random set had equal content of all
four nucleotides. We therefore also offer the option to exclude biases based on GC
content by matching random promoters with approximately equal GC content to
the experimental promoters. To identify individual TFBSs with increased precision,
and add additional support for the relevant TFs, we subsequently scan individual
promoters for cross-species conservation, again employing TRANSFAC matrices. All
steps are flexible allowing for a multitude of input types (Ensembl (62) gene IDs,
nucleotide sequences, or selected by CORE TF).
We also compared CORE TF to two existing programs: oPOSSUM (60) and
ConTra (61).
CORE TF is accessible as a web-page. In this paper, we present and evaluate the
performance of our web-based tool for identification of TFBSs.
21
2 CORE TF
2.3
2.3.1
Implementation
CORE TF Construction Format
The main script is written in Perl and presented in HTML on an Apache web-server.
Input and table sorting is done using an edited Java script: sorttable.js (63). By
default, following the title page, there are 6 pages that are run in a linear fashion
feeding the results of one page into the next (Figure 2.1).
Page one allows a user to select run options and input criteria, including a pvalue cut-off for highlighting data (see below), 6 different Match (the program that
aligns TRANSFAC PWMs to nucleotide sequences) (51; 55) settings (minimize false
positives, minimize false negatives, minimize the sum of both error rates, and nonredundant sets of these 3 settings), and data input type for a set of experimental
promoters and a set of random promoters. The experimental promoter lists are entered as sequences in fasta format or Ensembl gene IDs. Five options are available
for the random promoter list input: sequences in fasta format, an Ensembl gene ID
list, randomly retrieve Ensembl promoters, pre-constructed promoter sets, and preretrieved sequence sets that are matched to the experimental set based on percentage
of GC content. There is also an option to skip the over-representation analysis and
go directly to page 4.
Depending on the selections from page 1, page 2 presents text boxes to paste in
lists of fasta format sequences or Ensembl gene IDs, or radio-buttons to select a certain
number of random promoters for the appropriate species, or species based check boxes
for pre-constructed runs or %GC matched runs. If CORE TF must retrieve promoters
there are two options to define promoter sequences. The first option is to call a
promoter as exon 1 plus a user defined number of base-pairs (bp) upstream. The
second option is to define a promoter sequence as a user specified number of bp before
and after the start of exon 1. The pre-constructed (approximately 3000 promoters)
and pre-retrieved sets to match %GC on (approximately 10000 promoters, of which
3000 are selected) are based on 1000 bp upstream of exon 1 and exon 1 sequence.
If requested, page 3 (Figure 2.2) uses Ensembl API to retrieve promoters from a
locally installed Ensembl database or from the web-based Ensembl database depending on CORE TF installation. If the option to use %GC matched random sequences
is selected CORE TF matches pre-retrieved promoter sequences to the experimental
promoter sequences so that at least 3000 similar %GC promoters are obtained. It
then uses Match to scan all sequences for the presence of TRANSFAC Professional
(note: web based CORE TF is still free access to non-commercial users) vertebrate
PWMs passing the PWMs’ alignment threshold provided on page 1 (pre-constructed
random promoter sets also have pre-executed Match runs and initial number of hits
counted). A binomial test is carried out with the Perl module Math::Cephes (64) to
identify TFBSs that are over-represented in the experimental set over the random set.
This is displayed on the screen as a sortable table with the TFBSs’ name, p-value
(10 digits are displayed), hits and total number in the experimental and random sets,
as well as the number of PWM hits in each experimental promoter. For clarity, pvalues below a defined threshold from page 1 are highlighted in blue. The table can
be downloaded as an HTML file or a tab-delimited text file. The user can select a
number of TFBSs plus a promoter of interest and continue to the next page. There
is also a Java script with a button to automatically select all TFBSs with a p-value
22
2.3 Implementation
Figure 2.1: Flowchart of CORE TF runs: CORE TF runs linearly through 6 web
pages. Pages 1 and 2 take as input experimental gene/promoter lists and random
gene/promoter lists or requests to create random lists. Depending on format, sequences are retrieved with Ensembl API or random lists generated before identifying
TFBSs with Match/TRANSFAC. A binomial test is run to identify over-represented
TFBSs in the experimental set compared to the random set and displayed in page
3 as a table. In the table TFs and a promoter can be selected which are sent to
page 4. If requested homologs and sequences or genomic alignments are retrieved
from Ensembl for the selected promoter. If not already a genomic alignment, input
sequences or retrieved sequences are aligned with BLASTz. TFBSs are identified
with Match/TRANSFAC, overlapping TFBSs are identified and scores calculated,
and the data is displayed in page 5. Conserved TFBSs can be selected and displayed
as highlights in the alignment in page 6.
below the defined threshold.
Page 4 gives the user the opportunity to use Ensembl defined orthologs or aligned
23
2 CORE TF
Figure 2.2: Page 3 screen-shot: Page 3 of CORE TF displays the following columns:
selection boxes for the next page’s analysis, all TFBS PWMs with hits, the p-value,
the number of experimental promoters hit, the number of experimental promoters
analyzed, the number of random promoters hit, the number of random promoters
analyzed, frequency of hits in the random data, as well as a column for each experimental promoter analyzed indicating the number of TFBSs hit in it. Our page is
lengthy, so for display purposes in this figure we deleted the middle TFBSs as indicated by the large black bar. For a full color figure see www.biomedcentral.com/14712105/9/495/figure/F2.
genomic regions in a selection of species (currently H. sapiens, P. troglodytes, M.
musculus, R. norvegicus, B. taurus, C. familiaris, and G. gallus) or enter user defined
orthologous sequences in fasta format. There is also the option to define promoters
as was done in page 2. If the user skipped over-representation analysis there is a list
of TFBSs to chose from for analysis, otherwise CORE TF uses TFBS selection from
page 3.
This is given to page 5 which, if necessary, retrieves either orthologous IDs and
sequences or aligned genomic regions with Ensembl API. Aligned genomic regions are
pairwise alignments, but CORE TF places them into a multi-species viewed align24
2.3 Implementation
ment. Sequences are again scanned by Match and TRANSFAC. If Ensembl genome
alignments were not used, the first sequence entered or the ID used for orthologous
retrieval is used as the reference to carry out a promoter sequence alignment with
BLASTz (65). Alignments are displayed on the screen. Tables are shown with each
TFBS selected and the following information: total score, region score, number of
promoters aligned at that point, and the length of the TFBS. The region score is
defined by taking the sum of 100 times the percent of each nucleotide aligned (Figure
2.3A). The total score is defined as the region score divided by the pattern length
divided by 100 (Figure 2.3B). More specific details of these region numbers are displayed on additional tables lower in the page. The user may select a TF and submit
this to the final page.
Figure 2.3: Formulas for conservation scores.
Page 6 (Figure 2.4) allows for visualization in the alignment by displaying the
alignment with selected TFBSs highlighted according to the strand bound: blue (positive strand), purple (both strands), or red (negative strand). There is also evidence
that some TFs may preferentially bind one strand over the other (5). It is up to the
user to decide if their TF is strand specific or not.
2.3.2
CORE TF Evaluation with Expression and ChIP-on-chip
Data
To verify the performance of our algorithms we used expression and ChIP-on-chip data
from Cao et al 2006 (66). They studied the promoter binding of two major regulators of muscle differentiation (MyoD and Myog) and expression profiles in embryonic
fibroblasts from MyoD/Myf5 knockout mouse transduced with a MyoD-estrogen receptor hormone binding fusion protein (termed MDER cells). These cells have been
modified so that they can be studied during differentiation with or without MyoD or
25
2 CORE TF
Figure 2.4: Page 6 screen-shot of a conserved MyoD TFBS in the LAMA4 promoter: Page 6 of CORE TF displays two identical boxes containing aligned promoters with conserved TFBSs highlighted by color; blue if on the positive strand,
purple if on both strands, and red if on the negative strand. For a full color figure
see www.biomedcentral.com/1471-2105/9/495/figure/F4. If requested in the previous
page to show run details (not shown in this figure), boxes with score construction for
all conserved TFBSs are also displayed, as well as the patterns of all selected PWMs
hit. Here we show an example of a MyoD TFBS (PWM MyoD Q6 01) in the LAMA4
promoter conserved in human, chimp, and dog on both strands.
Myog present. Promoter binding was also studied in a common mouse myoblast cell
line (C2C12).
ChIP-on-chip is a technique using a TF targeting antibody that is used to pulldown TF bound DNA fragments, which are then amplified, labeled, and hybridized
to a (promoter or tiling) microarray. As a positive control set for TF binding, we
took those promoters from the ChIP-on-chip data that showed enrichment for MyoD
or Myog binding sites (p-value < 0.001). We re-analyzed the Affymetrix expression
data by applying a RMA summarization and normalization and using the R package
limma (67; 68) to fit a linear model containing the following factors: MyoD expression
(yes/no), Myog expression (yes/no), and time of differentiation (0, 24, 48, and 96 h).
As a positive control set for MyoD or Myog-induced regulation of gene expression
we took the top 200 or less genes based on the effect of MyoD or Myog expression,
respectively. When needed, accession numbers were converted to Ensembl gene IDs
using Idconverter (69).
For the 200 most significantly induced genes, we evaluated whether their promoters
contained MyoD or Myog TFBSs according to the ChIP-on-chip data. We expect
that the smaller more specific lists would have a higher percent of promoters with
true TFBSs (significant on the ChIP-on-chip platform) and therefore likely to contain
more significantly over-representated TFBSs in our predictions. We found that as
a general trend this is true that the smaller more specific expression lists contain a
higher percent of true positives (significant ChIP-on-chip genes) (Additional File 2.1).
2.3.3
Random Data Size Evaluation
We evaluated what would be an appropriate number of random promoters by running
a set of 14 experimental promoters against several random set sizes; 500, 1000, 2000,
and 4000. For this, the Match cutoff was set to minimize the sum of false positives
and negatives. For this test we used a promoter size of 1000 bp before exon 1 and all
of exon 1. The larger the random size used the more consistent the number of TFBSs
26
2.3 Implementation
that were identified (Additional File 2.2), but also the longer the run time. We found
a random size of 2000 promoters to be the best trade off between accuracy and speed.
2.3.4
Promoter Size Evaluation
We evaluated an appropriate promoter size for our TFs of interest by taking the
Cao et al. 2006 expression data top 50 MyoD- or Myog-responsive promoters for the
appropriate stimulation (MyoD or Myog) compared to 2000 purely random mouse
Ensembl promoters. We varied the promoter size to include exon 1 plus an additional
number of bp upstream; 500, 1000, 2000, and 4000. Analysis showed that with a
Match setting to minimize false positives a promoter size of 2000 bp + exon 1 was best,
whereas with a Match setting to minimize the sum of false positives and negatives a
promoter size of 1000 bp + exon 1 was preferable (Additional File 2.3). We continued
with a Match setting to minimize the sum of false positives and negatives setting
using 1000 bp upstream + exon 1 as our promoter size.
2.3.5
Evaluation of GC Content
To evaluate the effect of GC content we ran purely random Ensembl promoters (the
FAST setting of CORE TF) on all Cao et al ChIP data. We then compared that to
runs with the option to get random promoters of approximately equal %GC content
compared to the experimental set (the Similar %GC option).
2.3.6
Wet-lab Verification of a CORE TF Predicted Conserved
TFBS
To give wet-lab confirmation to the results of the CORE TF conservation predictions we used the TransFactor kit with double stranded DNA designed on a LAMA4
(ENSG00000112769) MyoD predicted TFBS conserved between human, chimp, and
dog (Figure 2.4). This was an Ensembl genomic alignment run with a Match setting
to minimize the sum of false positives and false negatives. The promoter size was
defined as 3000 bp upstream of exon 1 and including exon 1. We also included a negative control of the same DNA sequence with four mutations. Recombinant MyoD
protein was used to test for binding. For more details on the TransFactor run see the
additional material (Additional File 2.4).
2.3.7
CORE TF Compared to an Existing Program: oPOSSUM
To evaluate our script with existing technology we ran the Cao et al 2006 expression
data (most significant 20, 50, 100, and 200 genes) through the oPOSSUM website (60).
We chose oPOSSUM for comparison since it performs similar analysis and is freely
available. We used their custom single site analysis page. Other than setting to mouse,
vertebrate JASPAR PWMs, retrieving 1000 bp up and 433 bp downstream (using
Ensembl API we calculated this as the average size of exon 1) of the transcription
start site, and showing all results, all settings used their defaults. It must be noted
that JASPAR only has a PWM for Myf, which represents a TF family including
27
2 CORE TF
MyoD and Myog. We also used their number of hits in their background and target
genes to run a binomial test in the statistical package R to match our data.
2.3.8
CORE TF Compared to an Existing Program: ConTra
We also chose to evaluate CORE TF versus an additional easily viewable cross-species
conservation program, ConTra (61). As a test promoter for comparison we used
the LAMA4 (ENSG00000112769) promoter, for which we had a lab verified MyoD
TFBS. The ConTra website was run on all default parameters (selecting transcript
ENST00000230538), except for looking at 3000 bp upstream instead of 2000 bp upstream (giving a promoter the same size as the CORE TF run). We looked at the
PWM MyoD Q6 01. This was the only PWM for MyoD available at the ConTra
website and the best performing for CORE TF with this promoter.
2.4
2.4.1
Results and Discussion
CORE TF Work Flow and Function
We have developed a series of web pages to identify TFBSs in two sequential processes.
First, pages 1 to 3 allow a user to predict TFs that regulate a set of co-regulated
genes. This is done by identifying TFBSs that are over-represented in the promoters
of an experimental (e.g. similar expressed genes from microarray data) compared
to a random data set, taking GC content into account if requested. These results
are displayed in a sortable table in page 3 (Figure 2.2). Secondly, pages 4 to 6
allow a user to identify specific TFBSs by looking for across species conservation of
TFBSs selected from the TFBSs in page 3 and the promoters of page 3. This is done
on Ensembl genomic alignments or BLASTz alignments of orthologous promoters
provided by Ensembl or the user. Across species conserved TFBSs are displayed in
tables (calculations as in Figure 2.3) in page 5 and as aligned promoters in a graphical
format (Figure 2.4) in page 6.
Alternatively, if a user did not wish to look at a list of promoters, but just a
single promoter they could look purely for cross-species conserved TFBSs by skipping
straight to page 4 from page 1. They must then provide which promoter they want
to search and a set of TFBSs from a web displayed list. In theory they could paste
the sequences conserved in the alignments back into the over-representation pages to
find TFBSs over-represented in conserved regions (as opposed to the normal order of
looking for conservation with over-represented TFBSs).
2.4.2
Prediction of Over-Represented TFBSs
To evaluate the performance of our tool we first used the Cao et al 2006 ChIP-on-chip
data as a positive control. We tested whether the promoters in the ChIP pull-down
were enriched for the TFBSs for the TFs targeted in the ChIP experiments compared
to a random set of promoters. To evaluate the effect of matching promoters for %GC
content, CORE TF was run with a purely random selected set of promoters (FAST
option) and a random set of promoters with matched %GC content as controls (similar
%GC option). Using both sets of random promoters, CORE TF found a significant
28
2.4 Results and Discussion
over-representation (p-value < 0.05, after applying multiple test correction with Benjamini Hochberg in R (70)) for the MyoD PWM MYOD Q6 in the MyoD bound
promoters and the Myog PWM MYOGENIN Q6 in the Myog bound promoters, in
both C2C12 and MDER cells (Additional File 2.5). The MyoD PWM MYOD Q6 01
was also significant in all MyoD targeted runs except the MDER MyoD with random
promoters matched on %GC content.
Strikingly, by ranking TFBSs on p-value, we demonstrate that the target TFs
were higher ranked with the %GC matched promoters as control rather than with the
purely random set of control promoters (Table 2.1), indicating that improper matching
of GC content leads to false positive identification of TFBSs. By evaluating the
distribution of p-values for all TFs using both random sets, we observed purely random
promoters yield more high and low p-values than a random set of promoters matched
on %GC content (Additional File 2.6). Since our target ChIP TFs remained significant
when using %GC matched promoters, resulting in a smaller list of significant TFBSs,
we believe this method to yield less false positives.
To demonstrate that our algorithm is able to find shared regulatory sites in coregulated genes identified in expression microarray data we evaluated whether genes
for which the expression level increased upon MyoD or Myog activation were enriched for MyoD or Myog TFBSs. We ran sets consisting of the 20, 50, 100, and 200
genes most significantly affected by MyoD or Myog activation versus a random set of
approximately equal %GC content (Additional File 2.7). We found significant enrichment of the MyoD Q6 PWM in all MyoD enriched sets. We also found MYOD Q6 01
enriched in the top 50 and top 100 MyoD enriched sets. MYOGENIN Q6 was found
enriched in the top 20 Myog enriched set only. Other PWMs for MyoD or Myog
and other sets of promoters were not significant or considered ”NA” due to 100%
of promoters hit in the experimental data. The same data was also run through
with the CORE TF FAST setting. We found that the two settings perform similar,
with slightly higher frequencies but slightly less significant p-values when matching on
%GC (Figure 2.5). Additionally, as expected the smaller more specific lists generally
have higher frequencies and lower p-values than larger, less specific lists (Figure 2.5).
29
30
C2C12 MyoD %GC
MYOGENIN Q6
AP4 Q5
E2A Q2
AP4 Q6
MYOD Q6
AP4 Q6 01
E47 01
E12 Q6
LBP1 Q6
E2A Q6
SMAD Q6 01
MYOD Q6 01
AP1FJ Q2
AP1 Q4
E47 02
C2C12 Myog %GC
AP4 Q5
AP4 Q6
MYOGENIN Q6
MYOD Q6
AP4 Q6 01
E2A Q2
AREB6 01
MYOD Q6 01
LBP1 Q6
AP4 01
AP1 Q4
ZEC 01
E2A Q6
ATF6 01
E47 01
p-val*
0
0
0
0
0
0
0
0
0
0
7.9E-09
7.9E-09
2.2E-08
5.8E-08
6.2E-08
p-val*
0
0
0
0
0
0
0
0
0
4.7E-09
1.6E-07
2.3E-07
1.3E-06
6.5E-06
7.5E-06
p-val*
0
0
0
5.0E-06
1.1E-05
9.0E-04
6.9E-03
1.4E-02
1.4E-02
1.9E-02
2.2E-02
2.2E-02
7.1E-02
8.0E-02
8.2E-02
p-val*
1.5E-06
2.3E-06
2.7E-06
8.8E-06
8.8E-06
5.1E-05
1.1E-03
1.4E-03
4.0E-03
4.6E-03
2.7E-02
4.4E-02
4.5E-02
5.8E-02
9.4E-02
MDER Myog FAST
AP1 Q6 01
AP4 Q5
AP4 Q6
COUP DR1 Q6
E2F1DP1 01
E2F4DP2 01
E2F Q4
E2F Q6 01
LBP1 Q6
MAF Q6 01
MYOGENIN Q6
NF1 Q6 01
NFE2 01
OSF2 Q6
AP4 Q6 01
MDER MyoD FAST
AP1 Q6 01
AP4 Q5
COUP DR1 Q6
E2F1DP1 01
E2F4DP2 01
E2F Q4
E2F Q6 01
MAF Q6 01
NF1 Q6 01
NFE2 01
NFKB Q6
OSF2 Q6
AP4 Q6
GATA3 01
LBP1 Q6
p-val*
0
0
0
0
0
0
0
0
0
0
0
0
0
0
6.4E-09
p-val*
0
0
0
0
0
0
0
0
0
0
0
0
3.5E-09
3.5E-09
6.5E-08
MDER Myog %GC
AP4 Q5
AP4 Q6
MYOGENIN Q6
AP4 Q6 01
LBP1 Q6
MYOD Q6
E2A Q2
MYOD Q6 01
CLOCKBMAL Q6
AP2ALPHA 01
ZEC 01
AP2 Q6
AP4 01
PPARG 01
CMYC 02
MDER MyoD %GC
AP4 Q5
AP4 Q6
MYOGENIN Q6
AP4 Q6 01
AP4 01
MYOD Q6
E2A Q2
LBP1 Q6
HEN1 02
TAL1BETAE47 01
MYOD Q6 01
HEB Q6
HELIOSA 01
AP1 Q4
HNF4 01
Table 2.1: Cao et al 2006 top ChIP-on-chip predictions with CORE TF
p-val*
0
0
0
2.5E-06
8.0E-06
8.1E-06
7.1E-03
8.6E-03
4.5E-02
5.7E-02
5.8E-02
7.7E-02
1.1E-01
1.2E-01
1.2E-01
p-val*
0
3.1E-06
2.7E-05
2.7E-04
1.2E-03
4.5E-03
5.2E-03
2.3E-02
7.6E-02
7.6E-02
1.7E-01
2.9E-01
2.9E-01
4.0E-01
4.0E-01
CORE TF predictions on Cao et al 2006 ChIP-on-chip data. Target TFBSs are presented in bold. * = p-values are Benjamini
Hochberg corrected. Note: in the MyoD FAST runs MYOD Q6 and MYOD Q6 01 had p-values < 0.05 but were not in the top 15
significant TFBSs.
C2C12 MyoD FAST
AP1 Q6 01
E2F1DP1 01
E2F4DP2 01
E2F Q4
E2F Q6 01
GATA3 01
MAF Q6 01
NF1 Q6 01
NFE2 01
OSF2 Q6
AP4 Q5
MYOGENIN Q6
LBP1 Q6
AP4 Q6
AP4 Q6 01
B.Myog ChIP-on-chip
C2C12 Myog FAST
AP1 Q6 01
AP4 Q5
AP4 Q6
E2F1DP1 01
MAF Q6 01
MYOGENIN Q6
NF1 Q6 01
NFE2 01
OSF2 Q6
E2F4DP2 01
COUP DR1 Q6
AP4 Q6 01
E2F Q4
GATA3 01
MYOD Q6
A. MyoD ChIP-on-chip
2 CORE TF
2.4 Results and Discussion
Figure 2.5: Significance of myogenic TFBSs in expression data: The (A) significance (as the absolute value of the log10 p-value) and (B) frequency of MyoD (PWM
MyoD Q6) or Myog (PWM MYOGENIN Q6) TFBSs in varying number of promoters
from genes with increasingly less significant differences in expression upon MyoD or
Myog activation are shown. As would be expected, the smaller more significant lists
generally have higher frequency and more significant p-values than larger less specific
lists.
2.4.3
Orthologous Alignments Versus Genomic Alignments
In many CORE TF runs we assessed the conserved TFBSs using alignments based
on homologous Ensembl promoters as well as Ensembl genomic alignments. Ensembl
pairwise alignments can be considered syntenic (they are grouped to make the actual
Ensembl synteny blocks) (71). Ensembl orthologs are identified using protein tree
calculations (62). The number of promoters aligning and the quality of the alignment
to the reference promoter varies tremendously amongst different promoters for both
methods (data not shown), but we did not find one method outperforming the other.
Synteny does not imply the start of one gene corresponds to the start of a gene in
another species. Therefore, this could give poor predictions for TFs that bind and
function close to the transcription start site. However, due to many incorrect exon 1
annotations it is also possible that using orthologous promoter alignments may align
regions that are not corresponding regions (if an annotation missed exon 1, exon 2
would be annotated as exon 1 and we would instead align to it). Therefore there is
not one alignment method that outperforms another to predict conserved TFBSs.
2.4.4
TFBSs Conserved in Orthologous Alignments
The top 10 ranked genes of the Myog-induced genes were inspected for the presence of
MYOGENIN Q6 motifs. To this end, all available orthologs for the mouse genes were
retrieved. All conserved TFBSs and their conservation scores are reported in Table
2.2. There are seven promoters which appear to have conserved TFBSs. Four of these
promoters (Chrng, Myog, Acta1, and Tnnc1 ) had hits in three or more orthologs. We
also inspected the MyoD induced genes presence of MyoD 01 motifs using the same
approach and identified two promoters with conserved TFBSs (Table 2.2). Only one
promoter was found conserved over three or more orthologs (Rgs16 ). In addition, of
the nine across species conserved TFBSs all except Tnnc1 (not on the array), Tnnc2,
31
2 CORE TF
Rgs16, and Nptx1 were found significant in the ChIP-on-chip data. Literature was
examined to see if predictions were correct. We found evidence for binding of Myog
to Myog (72), Tnni1 (73), and Chrng (74). We also found evidence for MyoD binding
Nptx1, also called NP1 (75).
Table 2.2: Orthologous conservation of target TFBSs in target genes
A
Tot. Score #Promos Length
Gene GeneID
TF Name
Name
Score
1 1000
5
10
Chrng ENSMUSG00000026253 MYOGENIN Q6
1 1000
5
10
Chrng ENSMUSG00000026253 MYOGENIN Q6
1 1000
2
10
Tnnt3 ENSMUSG00000061723 MYOGENIN Q6
1 800
2
8
Tnnc2 ENSMUSG00000017300 MYOGENIN Q6
1 800
2
8
Tnni1 ENSMUSG00000026418 MYOGENIN Q6
5
8
Myog ENSMUSG00000026459 MYOGENIN Q6 0.83 666.7
4
8
Acta1 ENSMUSG00000031972 MYOGENIN Q6 0.8 640
4
10
Tnnc1 ENSMUSG00000021909 MYOGENIN Q6 0.72 720
3
8
Acta1 ENSMUSG00000031972 MYOGENIN Q6 0.6 480
B
Tot. Score #Promos Length
Gene GeneID
TF Name
Name
Score
1 1200
4
12
Rgs16 ENSMUSG00000026475 MYOD 01
0.5 600
2
12
Rgs16 ENSMUSG00000026475 MYOD 01
0.4 840
2
21
Nptx1 ENSMUSG00000025582 MYOD 01
0.4 480
2
12
Nptx1 ENSMUSG00000025582 MYOD 01
Conserved TFBSs for (A) Myog (PWM MYOGENIN Q6) and (B) MyoD (PWM
MYOD 01) from target genes’ promoters in expression data. Total score represents a
score of conservation from 0 to 1 over the conserved TFBS length. Score represents an
additive score over the TFBS. Promos is the number of promoters with the conserved
TFBS. Length is the length of the TFBS.
2.4.5
Wet-lab Confirmation of a CORE TF Predicted Conserved TFBS
To confirm a CORE TF conserved TFBS in the lab we looked at a MyoD predicted
TFBS in the LAMA4 promoter. Using Ensembl defined genomic alignments we found
the matrix MyoD Q6 01 conserved in human, chimp, and dog (Figure 2.4). Using a
recombinant MyoD protein and the TransFactor kit we found significant (p-value
1.5E-35) binding to our target TFBS compared to a mutated one (Additional File
2.4).
2.4.6
CORE TF Compared to Existing Programs: oPOSSUM
We compared the performance of CORE TF (using a random set with similar %GC)
to oPOSSUM, a webtool with similar objectives as ours. oPOSSUM looks for overrepresented JASPAR PWMs in pre-defined species alignments, but is limited to spe32
2.4 Results and Discussion
cific species alignments (e.g. human-mouse) and use of the smaller JASPAR PWM
database. We used the previously mentioned expression microarray datasets for
the evaluation of both programs performances. Our runs on the oPOSSUM website showed that our binomial test performs similar to their Fisher test (Additional
File 2.8). Unlike our frequency observations, the frequency identified by oPOSSUM
of TFBS hits in the MyoD induced set did not show the expected high to low pattern (Additional File 2.9). When comparing p-values from the binomial tests for the
predictions by the two programs, we see similar patterns between the two programs
across the top 20, 50, 100, and 200 genes, but CORE TF has more significant MyoD
predictions and oPOSSUM has more significant Myog predictions (Additional File
2.9). It must be noted that we are only comparing over-represented TFBSs whereas
oPOSSUM has already taken conservation into their program at this point which
may explain higher sensitivity for Myog promoters. We instead do this on individual promoters and display it graphically in the next step. We believe this graphical
representation to be more interpretable.
Since we can do better in one out of two tested TFs without our orthologous
promoter conservation we believe CORE TF to be a superior tool. The two programs
differ on several other levels. oPOSSUM only takes Ensembl IDs as input, whereas
we also accept nucleotide sequences. We also offer a larger choice of random data
sets and conservation methods, as well as the choice to account for GC content.
In addition, our number of vertebrate species available is six, all of which can be
compared together. oPOSSUM only accepts two species comparisons at a time. For
vertebrates oPOSSUM is limited to only human and mouse, both of which are in
CORE TF. In addition, we display our across-species TFBSs in a graphical format,
whereas oPOSSUM presents their data in a less intuitive tabular format.
2.4.7
CORE TF Compared to an Existing Program: ConTra
We also evaluated CORE TF versus ConTra using the LAMA4 promoter, for which
we had experimental data available, as an example. ConTra is a website to identify and easily view conserved TFBSs in a single cross-species promoter alignment,
but cannot look for over-representation in a large promoter set. We found that in
CORE TF genomic alignment predictions there were three MyoD TFBSs conserved
between human and chimp and one TFBS conserved between human, chimp, and dog
(Figure 2.4). ConTra found the same TFBSs, but also three additional (Additional
File 2.10 and data not shown). Two of the three human/chimp CORE TF conserved
TFBSs and the human/chimp/dog CORE TF conserved TFBS were also found conserved in the macaque in ConTra. CORE TF did not search for macaque, but it is
extremely similar to human and chimp so we believe it would not add much information. However, if a user wanted any Ensembl species added to CORE TF adding an
additional species to the scripts is very simple. It is not surprising the same TFBSs
were identified since both programs use Ensembl alignments and TRANSFAC PWMs.
ConTra does have the disadvantage of only using human as a reference genome for
automated alignment retrievals, whereas CORE TF can do this for all six species
currently installed. Additionally, CORE TF does not use an Ensembl multi-species
defined alignment, but combines many Ensembl pair-wise alignments into one, allowing any number of Ensembl species to be included in one alignment. ConTra does not
33
2 CORE TF
display strand specific binding which CORE TF does by color coding. Additionally,
ConTra does not search for over-represented TFBSs in a group of promoters.
2.4.8
Future Efforts
An item that can be improved in the future is our evolutionary scoring algorithm, e.g.
by taking into account the confidence of each nucleotide in the PWM. An additional
improvement will be to analyze combinations of TFBSs.
2.5
Conclusion
We have developed a tool for identifying over-represented TFBSs in promoters from
co-expressed genes aided by the evaluation of cross-species conservation. CORE TF is
easy to use and displays results in tables or graphically allowing for easy interpretation
of the results. Our method seems to correctly predict the presence of experimentally
verified TFBSs, as shown by our extensive analysis on Cao et al. 2006 expression
and ChIP-on-chip data and wet-lab confirmation of a MyoD predicted TFBS in the
LAMA4 promoter. We also show improvements over two existing programs (oPOSSUM and ConTra) with greater flexibility in input data, coverage of a larger number
of species, more intuitive output, and the option to account for GC content.
Our tool is provided as a web service free to all non-commercial users.
2.6
Availability and Requirements
Project name: CORE TF
Project home page: http://www.LGTC.nl/CORE TF
Operating system(s): Linux
Programming language: Perl (we used 5.8.4)
Other requirements: TransFac Professional (we used 11.2), BLASTz, sorttable.js,
Math::Cephes (Perl module), Apache (we used 1.3.33)
License: GNU General Public License, v3 http://www.gnu.org/licenses/
Any restrictions to use by non-academics: none for website use, TransFac Professional license for a local install
2.7
Authors’ contributions
MH, JD, GO, and PH conceived of the primary concepts of the software. MH and MG
did the primary programming and debugging. MV performed all primary installations
on the web-server and helped in debugging code. MH, MG, and PH performed the
software evaluation on expression and ChIP-on-chip data. Wet-lab work was done by
MH. Manuscript drafting was done by MH, MG, JD, GO, and PH. All authors read
and approved the final manuscript.
34
2.8 Acknowledgements
2.8
Acknowledgements
We would like to thank Renee de Menezes and Maarten van Iterson for their statistical
comments and Ivo Fokkema for his programming and implementation assistance. This
work was funded by the Center for Biomedical Genetics (in the Netherlands). PH
was supported by a VENI-grant from the Dutch Organization for Scientific Research
(NWO grant 2005/03808/ALW).
35
2 CORE TF
2.9
Additional Files
Additional File 2.1
Overlap of most significant expression genes in ChIP-on-chip data. Indicated are the
size of the lists for the top expressed genes and the percent of those contained in the
significant ChIP-on-chip genes (true-positives). There is a trend that the smaller
more selective expression gene lists contain a higher percent of true positives.
36
2.9 Additional Files
Additional File 2.2
Consistency of TF identification in different random set sizes. Indicated are the
number of TFs that occur in 1, 2, or 3 out of 3 total runs. As expected, the larger
the random set size (500, 1000, 2000, or 4000 promoters) the larger the consistency
over runs. However, as indicated by the y-axis scale, this is not a very large effect.
37
2 CORE TF
Additional File 2.3
Optimal promoter size. The p-value and frequency of promoters with size 500, 1000,
2000, and 4000 bp and exon 1 with Match settings to minimize false positives
(Min pos) or minimize the sum of false positives and negatives (Min sum). Overall,
we see a promoter of (A) 1000 bp + exon 1 works best for Min sum runs and (B)
2000 bp + exon 1 works best for Min pos runs. As expected, (C and D) frequency of
TFBSs hit increases as the promoters become larger. For a full color figure see
www.biomedcentral.com/content/supplementary/1471-2105-9-495-s3.tiff.
38
2.9 Additional Files
Additional File 2.4
TransFactor LAMA4 -MyoD. Set-up and data analysis of MyoD binding a LAMA4
promoter derived sequence with the TransFactor kit.
TransFactor confirmation MyoD binds the LAMA4
(ENSG00000112769:ENST00000230538) promoter
Materials:
TransFactor Kit (Clontech product 631956)
Oligos: (ordered from Operon, bring up in TE to 100μm)
LAMA4 MyoD F biotin tgctttcCACCAGCTGTGCgaccttg
caaggtcgcacagctggtggaaacga
LAMA4 MyoD R
biotin tgctttcCTCGAGGAGTGCgaccttg
Neg MyoD F
caaggtcgcactcctcgaggaaagca
Neg MyoD R
* Nucleotides are the mutated nucleotides from the original target sequence
Antibodies:
Primary: Santa Cruz MyoD (M318): sc760
Secondary: goat anti rabbit IgGHRP from TransFactor Kit
Protein: Recombinant MyoD protein
Plate Reader: BIOTEK Synergy HT
Methods:
Oligo preparation done as:
-mix 10μl forward + 10μl reverse oligo
-place 95◦ C heat block 10 minutes
-cool on desktop 30 minutes
-mix 20μl with 198μl Mg to make 1μM concentration, vortex briefly
The TransFactor Kit User Manual: V. Colorimetric TransFactor ELISA
Procedure is followed with the following additions/changes:
-dilute MyoD antibody 1:100
-dilute goat anti rabbit antibody 1:1000
-step F1: after adding the TMB substrate place directly into the reader
-plate reader protocol: 1. Kinetic 13x5 minute intervals
2. Absorbance
3. Wavelength: 655nm
4. Shake 30s/read
39
2 CORE TF
Results:
Measurements over 5 time points:
slope: Tn-T(n-1)
T2-T1 sample
9-26-06
0.01
0.008
Neg MyoD
0.021
0.034
LAMA4 MyoD
T3-T2 sample
0.008
0.007
Neg MyoD
0.019
0.029
LAMA4 MyoD
T4-T3 sample
0.007
0.006
Neg MyoD
0.017
0.024
LAMA4 MyoD
T5-T4 sample
0.007
0.006
Neg MyoD
0.015
0.02
LAMA4 MyoD
9-29-06
0.003
0.026
0.005
0.03
10-6-06
0.027
0.612
0.028
0.455
0.001
0.024
0.003
0.024
0.023
0.48
0.022
0.355
0.001
0.019
0.001
0.02
0.017
0.387
0.02
0.292
0 0.003
0.019
0.017
0.019
0.017
0.322
0.246
P-value
1.5E-35
F critical
3.06029
Gnumeric spreadsheet Anova single factor results:
Groups
Count
Sum
Average
measurements
48
3.756 0.07825
sample
48
72
1.5
day
48
96
2
ANOVA
Source of Variation
Between Groups
Within Groups
Total
SS
95.4319
45.0561
140.488
df
2
141
143
MS
47.7160
0.31955
Variance
0.02247
0.25532
0.68085
F
149.324
Conclusion: With a p-value of 1.5E-35 there is a very significant difference in
MyoD binging between the negative and target oligos. It is therefore highly likely
that the target sequence is a TFBS for MyoD.
40
2.9 Additional Files
Additional File 2.5
Cao et al 2006 ChIP CORE TF. CORE TF run results to identify over-represented
TFBSs in MyoD/Myog ChIP-on-chip data.
(http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2613159/bin/1471-2105-9-495-S5.
xls)
Additional File 2.6
CORE TF using random FAST runs vs runs with similar %GC. It is visible that in
all ChIP-on-chip data tested the runs on purely random Ensembl promoters (FAST
runs) has a bias towards high and low p-values while the random set with a similar
%GC follows a more normal distribution. This could account for false positives in
the FAST runs.
Additional File 2.7
Cao et al 2006 expression CORE TF. CORE TF run results to identify over-represented
TFBSs in expression array data.
(http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2613159/bin/1471-2105-9-495-S7.
xls)
41
2 CORE TF
Additional File 2.8
oPOSSUM runs on expression data. Custom oPOSSUM runs using the top 10, 20,
50, 100, and 200 genes from Cao et al 2006 expression data. oPOSSUM supplies (A)
Fisher and (B) z-scores. (C) We also used their hits in the experimental and
background data to generate a binomial test p-value similar to our program. (D)
Frequency of TFBS hits overall declines as we stray from the top hits, as expected,
but this is not an entirely smooth curve.
Additional File 2.9
CORE TF vs oPOSSUM. CORE TF and oPOSSUM binomial test p-values for the
top 20, 50, 100, and 200 genes from Cao et al 2006 expression data for
over-expression (A) of MyoD or Myog in the appropriately induced cell line. We see
comparable results in the top 20, 50, 100, and 200 sets, but better overall
performance in oPOSSUM for Myog and in CORE TF for MyoD. Frequency (B) of
MyoD or Myog hits was also plotted. As expected, the smaller more significant lists
generally have higher frequency and more significant p-values than larger less
specific lists. Frequency of TFBSs in the promoters was also overall higher in
experimental data than random promoters as expected. The oPOSSUM MyoD
frequency was the only plot that did not seem concordant.
42
2.9 Additional Files
Additional File 2.10
Identifying MyoD TFBSs conserved in the LAMA4 promoter with ConTra and
CORE TF. Many conserved TFBSs were found identically between the two
programs. Shown here is the most conserved TFBSs found, a MyoD TFBS
conserved between human, chimp, and dog in (B) CORE TF and also macaque in
(A) ConTra. Though found by both programs, CORE TF also identifies the TFBS
is on both strands of the DNA. For a full color figure see
www.biomedcentral.com/content/supplementary/1471-2105-9-495-s10.png.
43
2 CORE TF
44
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