We developed tool ESDEG that estimates the enrichment of motifs respecting transcription factor binding sites (TFBS) in promoters of differentially expressed genes (DEGs) derived from RNA-seq experiment. We applied this tool to perform analysis on promoters of the genes upregulated and downregulated in the brain tissue samples of tame rats 1. To identify enriched motifs, we first performed motif recognition in the given set of promoter (upstream regions of the same length), utilizing each from a library of nucleotide frequency matrices taken from the JASPAR. Next, a table of recognition thresholds was compiled as described in works23, where expected recognition rates (ERRs) for each threshold were calculated as probabilities of site prediction in the whole-genome dataset of promoters. Given that the enrichment of sites depends on a chosen threshold, subsequent calculations were performed for 20 recognition thresholds (equidistant on a logarithmic scale of ERRs) in the range of 5E−5 to 1E−3. Next, all promoters were categorized into either a foreground or background group. The foreground group contained the promoters of the significantly DEGs: either only upregulated or only downregulated or both upregulated and downregulated genes. The parameters (adjusted p-value and log2(fold change)) were used to determine whether a gene was differentially expressed. DEGs are defined as follows: adjusted p-value < 0.05 and |log2(fold change)| > 1. The background group included the same number of promoters as the foreground group, but they were randomly selected from none-DEGs according to given parameters (adjusted p-value, log2(fold change) and G/C content) and have the same G/C content as foreground. non-DEGs are defined as follows: adjusted p-value > 0.05 and |log2(fold change)| < log2(5/4). Next, we calculated the frequency of a motif in each of the gene promoter groups. To estimate the significance of the motif enrichment, the Monte Carlo approach was applied. For a given motif and a recognition threshold, site frequency was calculated for the foreground group (AVFOR), whereas a background group was generated many times to estimate the site frequency distribution. Next, the average (AVBACK) and standard deviation (SDBACK) for the frequencies in the background group set were calculated. Then, a Z-score was calculated according to the formula (AVFOR − AVBACK)/SDBACK. This Z-score allowed us to compute the significance of enrichment (p-value) by fitting a site’s frequency distribution to a normal distribution. To confirm enrichment for a given motif, multiple p-values were combined, referring all recognition thresholds into a single unified p-value according to the Bonferroni method 4. Thus, a unified p-value was calculated for each motif. Finally, an adjusted p-value (padj) was calculated from the set of unified p-values for all motifs with the Benjamini–Hochberg correction for multiple comparisons; in this way, our analysis involved multiple simultaneous statistical tests for various motifs.
- You can find more details in article Oshchepkov, Dmitry, Irina Chadaeva, Rimma Kozhemyakina, Svetlana Shikhevich, Ekaterina Sharypova, Ludmila Savinkova, Natalya V. Klimova, Anton Tsukanov, Victor G. Levitsky, and Arcady L. Markel. 2022. "Transcription Factors as Important Regulators of Changes in Behavior through Domestication of Gray Rats: Quantitative Data from RNA Sequencing" International Journal of Molecular Sciences 23, no. 20: 12269. https://doi.org/10.3390/ijms232012269
ESDEG is a command-line toolkit and Python package for estimating the enrichment of motifs in promoters. ESDEG runs on Python 3.7 and later. Your operating system might already provide Python, which you can check on the command line:
python --version
If you haven't already satisfied these dependencies on your system, install
these Python packages via pip:
- numpy
- scipy
- pandas
- biopython
- pyjaspar
- xlsxwriter
- panel
pip install numpy scipy plotly pandas biopython pyjaspar xlsxwriter panel
git clone https://github.com/ubercomrade/esdeg.git
cd esdeg
pip install -e .
The command ESDEG -h return:
usage: ESDEG [-h] {jaspar,hocomoco,deg,set,annotation} ...
positional arguments:
{jaspar,hocomoco,deg,set,annotation}
Available commands:
jaspar Run preparation DB based on JASPAR 2024 motifs (motifs
are selected based on taxon)
hocomoco Run preparation DB based on HOCOMOCO 2024 motifs (all
core motifs are used [M. musculus and H. sapiens])
deg Run test on DEGs
set Run test on SET of genes
annotation Add expression level and different expression data for
each TF/motif to filter ESDEG results
options:
-h, --help show this help message and exit
ESDEG includes five commands: jaspar, hocomoco, deg, set and annotaion.
The first step is to prepare the database using the commands jaspar or hocomoco depending on motifs database. This step requires (a) the library of motifs, which are extracted from the JASPAR or HOCOMOCO, and (b) promoters of the same length in FASTA format example, which are set by the user. The results of the command preparation represent a database including the list of motifs and their annotations in .npy format. This database is used further as an input for deg and set commands.
The next step is depending on type of input data.
If you have the results of RNA-seq analysis of different gene expression (example) you should use command deg.
If you have only set of genes without any additional information as log2(fold change) and p-value adjusted (example) you should use command set.
In the final step, ESDEG results can be complemented with gene expression information to improve filtering and refine reliable TFs. Expression level values (FPKM) as well as expression level changes (log2(Fold Change)) and its significance (padj) are given for all TFs that are included in the list of motifs. For this step you have to have results of RNA-seq analysis of different gene expression (example) and normalized counts (FPKM or other) (example). The annotation command is used for this step.
This step requires (a) the library of motifs, which are extracted from the JASPAR, and (b) promoters of the same length in FASTA format example, which are set by the user. The results of the command preparation represent a database including the list of motifs and their annotations in .npy format. This database is used further as an input for deg and set commands.
Timing 40–120 min
usage: ESDEG jaspar [-h] [-p NPROC]
{plants,vertebrates,insects,urochordates,nematodes,fungi}
promoters output
positional arguments:
{plants,vertebrates,insects,urochordates,nematodes,fungi}
Prepare database for respective JASPAR CORE taxonomic
group of motifs. Possible options are plants,
vertebrates, insects, urochordates, nematodes, fungi.
For more detailes see https://jaspar.uio.no/ and
https://pyjaspar.readthedocs.io/en/latest/index.html
promoters Path to promoters in fasta format. All promoters have
to be with same length. After the symbol ">" unique
gene ID has to be written
(>ENSG00000160072::1:1469886-1472284 or
>ENSG00000160072)
output Name of directory to write output files
options:
-h, --help show this help message and exit
-p NPROC, --nproc NPROC
Number of processes to split the work between.
Default= 4
First positional argument matrices:
It's name of taxon that is avaliable in JASPAR database5. Possible options for taxon are plants, vertebrates, insects, urochordates, nematodes, fungi. Only motifs from CORE COLLECTION and with length >= 8 are used for analysis. For more details see https://jaspar.elixir.no and https://pyjaspar.readthedocs.io/en/latest/index.html
Second positional argument promoters:
Argument promoters is the path to file with promoters in FASTA format. Promoters should have same length. After ">" unique gene ID have to be written (>ENSG00000160072::1:1469886-1472284 or >ENSG00000160072)
>ENSG00000160072::1:1469886-1472284
ACATTCCACCATTGTGATTTGTTTCTGCCCCACCCTAG...
>ENSG00000142611::1:3067261-3069659
TCGATAGACCCTCGAAAGGACGGCAGGGAATGGGGCTG...
>ENSG00000157911::1:2412102-2414502
GGACCTGCCCTGAGCTGGGGACGGGAAGGGCTTGGGCG...
>ENSG00000142655::1:10472968-10475366
ATCGGAGAGGAAAGGGGAAATGCGACTCGGCACTGTTG...
This type of FASTA file can be generated by using bedtools getfasta (https://bedtools.readthedocs.io/en/latest/) 6 with flag -name+
Third positional argument output:
Directory to write prepared database of motifs.
First optional argument -h; --help :
Print help to STDOUT
Second optional argument -p; --nproc :
The number of processes (threads) that will be used to prepare the database of motifs. By default, the number of processes is four. Increasing the number of processes allows you to speed up calculations; as the number of processes increases, the amount of RAM used increases.
This step requires (a) the library of motifs, which are extracted from the HOCOMOCO, and (b) promoters of the same length in FASTA format example, which are set by the user. The results of the command preparation represent a database including the list of motifs and their annotations in .npy format. This database is used further as an input for deg and set commands.
Timing 40–120 min
usage: ESDEG preparation [-h]
N output
positional arguments:
promoters Path to promoters in fasta format. All promoters have
to be with same length. After the symbol ">" unique gene ID has
to be written (>ENSG00000160072::1:1469886-1472284 or
>ENSG00000160072)
output Name of directory to write output files
options:
-h, --help show this help message and exit
-p NPROC, --nproc NPROC
Number of processes to split the work between.
Default= 4
First positional argument promoters:
Argument promoters is the path to file with promoters in FASTA format. Promoters should have same length. After ">" unique gene ID have to be written (>ENSG00000160072::1:1469886-1472284 or >ENSG00000160072)
>ENSG00000160072::1:1469886-1472284
ACATTCCACCATTGTGATTTGTTTCTGCCCCACCCTAG...
>ENSG00000142611::1:3067261-3069659
TCGATAGACCCTCGAAAGGACGGCAGGGAATGGGGCTG...
>ENSG00000157911::1:2412102-2414502
GGACCTGCCCTGAGCTGGGGACGGGAAGGGCTTGGGCG...
>ENSG00000142655::1:10472968-10475366
ATCGGAGAGGAAAGGGGAAATGCGACTCGGCACTGTTG...
This type of FASTA file can be generated by using bedtools getfasta (https://bedtools.readthedocs.io/en/latest/) 6 with flag -name+
Second positional argument output:
Directory to write prepared database of motifs.
First optional argument -h; --help :
Print help to STDOUT
Second optional argument -p; --nproc :
The number of processes (threads) that will be used to prepare the database of motifs. By default, the number of processes is four. Increasing the number of processes allows you to speed up calculations; as the number of processes increases, the amount of RAM used increases.
This command is used when you have comma-separated table including results of differentially expressed genes (example). This command allows you to detect which motifs are enriched in differentially expressed genes (foreground) compared to a background. Differentially expressed genes are determined by the parameters: --pvalue and --log2fc_deg. Background is determined by the parameters: --pvalue and --log2fc_back. See below.
Timing from 10 seconds to 5 minutes
usage: ESDEG deg [-h] [-v VISUALIZATION] [-x XLSX] [-R REPORT] [-p PARAMETER] [-r N] [-P PVALUE] [-l LOG2FC_DEG] [-L LOG2FC_BACK]
[-c CONTENT]
deg matrices output
positional arguments:
deg Input file in CSV format with results of RNA-seq analysis. File must contain next columns: id,
log2FoldChange, padj
matrices Directory with prepared database contained .npy files (e.g. /path/to/database)
output Output file in TSV format (e.g. /path/to/output/file.tsv)
options:
-h, --help show this help message and exit
-v VISUALIZATION, --visualization VISUALIZATION
Path to write interactive picture in HTML format (path/to/pic.html). if '-v' is given, then ESDEG creates
picutre. By default it isn't used
-x XLSX, --xlsx XLSX Path to write table with results in XLSX format (path/to/table.xlsx). XLSX table contains logo of motifs. if
'-x' is given, then ESDEG creates XSLX table. By default it isn't used
-R REPORT, --report REPORT
Path to write interactive table with results in HTML format (path/to/report.html). HTML report contains logo
of motifs. if '-r' is given, then ESDEG creates HTML report. By default it isn't used
-p PARAMETER, --parameter PARAMETER
Parameter estimated in test (enrichment or fraction), default= enrichment
-r N, --regulated N The parameter is used to choose up/down/all DEGs, default= all
-P PVALUE, --pvalue PVALUE
The pvalue is used as threshold to choose DEGs, default= 0.05
-l LOG2FC_DEG, --log2fc_deg LOG2FC_DEG
The absolute value of log2FoldChange used as threshold (L2FC_THR) to choose DEGs promoters (actual L2FC >=
L2FC_THR OR actual L2FC <= -L2FC_THR), default= 1.0
-L LOG2FC_BACK, --log2fc_back LOG2FC_BACK
The absolute value of log2FoldChange used as threshold (L2FC_BACK_THR) to choose background promoters
(-L2FC_BACK_THR <= actual L2FC <= L2FC_BACK_THR), default= log2(5/4)=0.321928...
-c CONTENT, --content CONTENT
The maximal GC content difference between promoters of foreground and background in Monte Carlo algorithm.
Range of possible threshold [0.01 .. 1.0]. If threshold is equal to 1.0 then GC content is not taken into
account. In this case (thr = 1.0) algorithm works faster. Default= 0.3.
First positional argument deg :
It is PATH to your comma-separated file (.csv) with full list of DEGs with required columns: id, log2FoldChange, padj (example). Similar table can be generated by DESeq27 (https://bioconductor.org/packages/release/bioc/html/DESeq2.html) or IRIS8 (https://bmbls.bmi.osumc.edu/IRIS/).
id,log2FoldChange,padj
ENSG00000000003,-0.2,0.472804281
ENSG00000000419,0.2,0.46039097
ENSG00000000457,-0.1,0.841129918
ENSG00000000460,0.2,0.534296601
ENSG00000000971,-1.4,1.06E-05
ENSG00000001036,0.1,0.81968747
ENSG00000001167,-0.2,0.444068007
ENSG00000001460,-0.1,0.808782152
Second positional argument matrices:
It is PATH to directory with prepared database by ESDEG.py preparation
Third positional argument output:
Output file in tab-separated format (.tsv) e.g. /path/to/output/file.tsv.
First optional argument -h; --help :
Print help to STDOUT
Second optional argument -v; --visualization :
It's path to write HTML report contained dotplot (log2(OR) vs -log10(padj)) (e.g. /path/to/file/with/graphics.html). Plotly is used for visualization.
Third optional argument -x; --xlsx :
It's path to write results in XLSX format. For this format logo is ploted for each motif in last column.
Fourth optional argument -R; --report :
It's path to write results in HTML format. For this format logo is ploted for each motif in last column.
Fifth optional argument -p; --parameter :
The value of -p; --parameter can be enrichment or fraction. If you choose enrichment option, statistics is calculated based on the number of predicted sites in promoters of DEGs. In this case the number of predicted sites in each promoter influence the result. If you choose fraction option, statistics is calculated based on the number of DEGs with predicted sites. In this case the number of predicted sites in each promoter is not so important. The default value is enrichment.
Sixth optional argument -r; --regulated :
The argument -r/--regulated are used to choose DEGs: down (promoters of down regulated genes), up (promoters of up regulated genes) and all (promoters of up and down regulated genes). The default value is all.
Seventh optional argument -p; --pvalue :
The argument -p; --pvalue is p-value threshold (P_THR) for DEGs selection (actual p-value of gene <= P_THR). The default value is 0.05.
Eighth optional argument -l; --log2fc_deg :
The argument -l; --log2fc_deg is Log2FoldChange threshold (L2FC_THR) for DEGs selection (actual value of Log2FoldChange of gene >= L2FC_THR OR actual Log2FoldChange of gene <= -L2FC_THR). The default value is 1.
Ninth optional argument -l; --log2fc_back :
The argument -l; --log2fc_back is Log2FoldChange threshold for background (L2FC_BACK_THR) selection (-L2FC_BACK_THR <= actual value of Log2FoldChange of gene <= L2FC_BACK_THR). The default value is log2(5/4) = 0.376287495.
Tenth optional argument -c; --content :
The argument -c; --content is used to set threshold of GC content for generating background.
This command is used when you have only set of genes from arbitrary source without any information related of expression of genes (example). This command allows you to detect which motifs are enriched in a given set of genes (foreground) compared to a background. In this case background includes all genes from database except given as a foreground.
Timing from 10 seconds to 5 minutes
usage: ESDEG set [-h] [-v VISUALIZATION] [-x XLSX] [-R REPORT] [-p PARAMETER] [-c CONTENT] set matrices output
positional arguments:
set File with list of genes.
matrices Path to prepared data base of matrices
output Path to write table with results
options:
-h, --help show this help message and exit
-v VISUALIZATION, --visualization VISUALIZATION
Path to write interactive picture in HTML format (path/to/pic.html). if '-v' is given, then ESDEG creates
picutre. By default it isn't used
-x XLSX, --xlsx XLSX Path to write table with results in XLSX format (path/to/table.xlsx). XLSX table contains logo of motifs. if
'-x' is given, then ESDEG creates XSLX table. By default it isn't used
-R REPORT, --report REPORT
Path to write interactive table with results in HTML format (path/to/report.html). HTML report contains logo
of motifs. if '-r' is given, then ESDEG creates HTML report. By default it isn't used
-p PARAMETER, --parameter PARAMETER
Parameter estimated in test (enrichment or fraction), default= enrichment
-c CONTENT, --content CONTENT
The maximal GC content difference between promoters of foreground and background in Monte Carlo algorithm.
Range of possible threshold [0.01 .. 1.0]. If threshold is equal to 1.0 then GC content is not taken into
account. In this case (thr = 1.0) algorithm works faster. Default= 0.3.
First positional argument set :
It's PATH to file with your SET of genes. Example:
ENSG00000072415
ENSG00000094796
ENSG00000143217
ENSG00000038427
ENSG00000186480
...
Second positional argument matrices:
It is PATH to directory with prepared database by ESDEG.py preparation
Third positional argument output:
Output file in tab-separated format (.tsv) e.g. /path/to/output/file.tsv.
First optional argument -h; --help :
Print help to STDOUT
Second optional argument -v; --visualization :
HTML output report contained graphical files (e.g. /path/to/file/with/graphics.html). Plotly is used for visualization.
Third optional argument -x; --xlsx :
It's path to write results in XLSX format. For this format logo is ploted for each motif in last column.
Fourth optional argument -R; --report :
It's path to write results in HTML format. For this format logo is ploted for each motif in last column.
Fifth optional argument -p; --parameter :
The value of -p; --parameter can be enrichment or fraction. If you choose enrichment option, statistics is calculated based on the number of predicted sites in promoters of DEGs. In this case the number of predicted sites in each promoter influence the result. If you choose fraction option, statistics is calculated based on the number of DEGs with predicted sites. In this case the number of predicted sites in each promoter is not so important. The default value is enrichment.
Sixth optional argument -c; --content :
The argument -c; --content is used to set threshold of G/C content for generating background.\
ESDEG results can be complemented with gene expression information to improve filtering and refine reliable TFs. Expression level values (FPKM) as well as expression level changes (log2(Fold Change)) and its significance (padj) are given for all TFs that are included in the list of motifs. For this step you have to have results of RNA-seq analysis of different gene expression (example) and normalized counts (FPKM or other) (example).
Timing from 1 minutes to 5 minutes
usage: ESDEG annotation [-h] [-x XLSX] [-b] [-f] [-m ME_PADJ] [-l LOG_ODDS]
[-d DE_PADJ] [-L LOG_FC] [-n NCOUNTS]
esdeg deg counts gtf output
positional arguments:
esdeg Path to esdeg result in TSV format
deg Input file in CSV format with results of RNA-seq
analysis. File must contain next columns: id,
log2FoldChange, padj
counts Input file in CSV format with normalized counts of
read (FPKM or other). File must contain next columns:
id, counts
gtf Input file in GTF format with genome features
annotation (ENSEMBL is prefered). It`s used to convert
gene names and IDs
output Path to write table with annotated ESDEG results.
Table includes information related to expression level
and different expression level of each TF/motif
options:
-h, --help show this help message and exit
-x XLSX, --xlsx XLSX Path to write table with results in XLSX format
(path/to/table.xlsx). XLSX table contains logo of
motifs. if '-x' is given, then ESDEG creates XSLX
table. By default it isn't used
-b, --best If this argument is used, then for each TF only the
motif with best enrichment will be left (Some times TF
have several motifs in DB)
-f, --filter If this argument is used, then filtration will be
applied to ESDEG table based on expression level of
TFs.
-m ME_PADJ, --me_padj ME_PADJ
p-value threshold for motif enrichment (adj.pval
column in table. adj.pval will be renamed to me_padj).
Default value = 0.05. Applied only when flag --filter
is used.
-l LOG_ODDS, --log_odds LOG_ODDS
log(odds ratio) threshold for motif enrichment
(log2(or) column in table). Default value = 1.0.
Applied only when flag --filter is used.
-d DE_PADJ, --de_padj DE_PADJ
p-value threshold for DEGs. It`s used to found TF
among DEGs. Default value = 0.05. Applied only when
flag --filter is used.
-L LOG_FC, --log_fc LOG_FC
log(fold change) threshold for DEGs. It`s used to
found TF among DEGs. Default value = 1.0. Applied only
when flag --filter is used.
-n NCOUNTS, --ncounts NCOUNTS
Counts threshold (expression level threshold) for
genes. It`s used to remove TFs that are not expressed
(With the exception of TFs, which are DEGs). Default
value = 5.0. Applied only when flag --filter is used.
First positional argument esdeg :
It's PATH to esdeg result in TSV format
Second positional argument deg :
It is PATH to your comma-separated file (.csv) with full list of DEGs with required columns: id, log2FoldChange, padj (example). Similar table can be generated by DESeq27 (https://bioconductor.org/packages/release/bioc/html/DESeq2.html) or IRIS8 (https://bmbls.bmi.osumc.edu/IRIS/).
id,log2FoldChange,padj
ENSG00000000003,-0.2,0.472804281
ENSG00000000419,0.2,0.46039097
ENSG00000000457,-0.1,0.841129918
ENSG00000000460,0.2,0.534296601
ENSG00000000971,-1.4,1.06E-05
ENSG00000001036,0.1,0.81968747
ENSG00000001167,-0.2,0.444068007
ENSG00000001460,-0.1,0.808782152
Third positional argument counts:
It is PATH to CSV file that contained normalized counts (FPKM or other) for each gene with required columns: id, counts.
id,counts
ENSG00000000003,0.0206177149643403
ENSG00000000005,0.0567211708870934
ENSG00000000419,7.76915277767639
ENSG00000000457,1.79968444256887
ENSG00000000460,6.17131000049197
ENSG00000000938,0.0310136079961313
ENSG00000000971,0.524588698013393
ENSG00000001036,59.3947651344486
ENSG00000001084,2.42592773904779
Fourth positional argument gtf:
It is PATH to GTF format with genome features annotation (ENSEMBL is prefered). It is used to convert gene names and IDs
Fifth positional argument output:
Output file in tab-separated format (.tsv) e.g. /path/to/output/file.tsv.
First optional argument -h; --help :
Print help to STDOUT
Second optional argument -x; --xlsx :
It's path to write results in XLSX format. For this format logo is ploted for each motif in last column.
Third optional argument -b; --best :
If this argument is used, then filtration will be applied to ESDEG table based on enrichment of motifs and expression level of TFs. For filtration next parameters are used:
Fourth optional argument -f; --filter :
If this argument is used, then filtration will be applied to ESDEG table based on enrichment of motifs and expression level of TFs. For filtration next parameters are used:
--me_padj- p-value threshold for motif enrichment--log_odds- log(odds ratio) threshold for motif enrichment--de_padj- p-value threshold for DEGs--log_fc- log(fold change) threshold for DEGs--ncounts- counts threshold (expression level threshold) for genes.
Fifth optional argument -m; --me_padj :
The p-value threshold for motif enrichment (adj.pval column in table. adj.pval will be renamed to me_padj). Default value = 0.05. Applied only when flag --filter is used.
Sixth optional argument -l; --log_odds :
The log(odds ratio) threshold for motif enrichment (log2(or) column in table). Default value = 1.0. Applied only when flag --filter is used.
Seventh optional argument -d; --de_padj :
The p-value threshold for DEGs. It is used to found TF among DEGs. Default value = 0.05. Applied only when flag --filter is used.
Eighth optional argument -L; --log_fc :
The log(fold change) threshold for DEGs. It is used to found TF among DEGs. Default value = 1.0. Applied only when flag --filter is used.
Ninth optional argument -L; --log_fc :
The counts threshold (expression level threshold) for genes. It is used to remove TFs that are not expressed (With the exception of TFs, which are DEGs). Default value = 5.0. Applied only when flag --filter is used.
Example bash script (for Linux), example batch script (for Windows) and data are located in ./example. You should run this scripts in ./example directory.
ESDEG jaspar \
vertebrates \
promoters.p400m100.fa \
./matrices_db \
--nproc 4
ESDEG deg \
./E-MTAB-6598.degs.csv \
./matrices_db \
./stat_irf.montecarlo.enrichment.tsv \
--visualization ./stat_irf.montecarlo.enrichment.html \
--report ./stat_irf.montecarlo.enrichment.report.html \
--xlsx ./stat_irf.montecarlo.enrichment.report.xlsx \
--parameter enrichment \
--regulated all
| motif_id | tf_name | tf_class | tf_family | jaspar_cluster | log2(or) | log10(pval) | log10(adj.pval) | adj.pval | genes |
|---|---|---|---|---|---|---|---|---|---|
| MA0680.2 | Pax7 | Paired box factors | Paired plus homeo domain | cluster_41 | 0.354260746038436 | -56.5585489729083 | -53.6399944423581 | 2.29089696905926E-54 | ENSG00000000971;ENSG00000008311;ENSG00000009694;... |
Where: motif_id - jaspar id from data base.
tf_name - name of Transcription factor
tf_class - class of DBD's transcrition factor
tf_family - family of DBD's transcrition factor
jaspar_cluster - сluster to which the motif belongs (https://jaspar.genereg.net/matrix-clusters/) [writen only for JASPAR database]
log(or) - it's -log2 transformation of odds ratio (OR). For the option fraction it can be defined as follow enrichment it can be defined as follow
log10(pval) - it's log10(combined p-value). Combined p-value is calculated by the Bonferroni method 4.
log10(adj.pval) - it's log10(adjusted p-value) obtained by using Benjamini-Hochberg FDR correction.
adj.pval - it's adjusted p-value obtained by using Benjamini-Hochberg FDR correction.
genes - list of genes with predicted sites for the best case (min p-value).
logo - for XLSX and HTML format files contain logo column instead of genes.
| motif_id | tf_name | tf_class | tf_family | log2(or) | me_padj | counts | lfc | de_padj | genes |
|---|---|---|---|---|---|---|---|---|---|
| IRF3.H12CORE.0.PS.A | IRF3 | Tryptophan cluster factors | IRF | 3.44067709622015 | 2.44862817869797E-122 | 17.7506790122365 | -0.0822566108747206 | 0.999587214534912 | ENSG00000002549;ENSG00000010030;... |
Where: motif_id - jaspar id from data base.
tf_name - name of Transcription factor
tf_class - class of DBD's transcrition factor
tf_family - family of DBD's transcrition factor
jaspar_cluster - сluster to which the motif belongs (https://jaspar.genereg.net/matrix-clusters/) [writen only for JASPAR database]
log(or) - it's -log2 transformation of odds ratio (OR). For the option fraction it can be defined as follow enrichment it can be defined as follow
me_padj - it's adjusted p-value (significance for motif enrichment) obtained by using Benjamini-Hochberg FDR correction.
counts - it's normalized number of reads (expression level) for each TF.
lfc - it's log2(Fold Change) for each TF.
de_padj - it's adjusted p-value (significance for different expression) for each TF.
genes - list of genes with predicted sites for the best case (min p-value). This column is not available for XLSX and HTML files.
logo - for XLSX and HTML format files contain logo column instead of genes.
Oshchepkov, D., Chadaeva, I., Kozhemyakina, R., Shikhevich, S., Sharypova, E., Savinkova, L., Klimova, N. V., Tsukanov, A., Levitsky, V. G., & Markel, A. L. (2022). Transcription Factors as Important Regulators of Changes in Behavior through Domestication of Gray Rats: Quantitative Data from RNA Sequencing. International journal of molecular sciences, 23(20), 12269. https://doi.org/10.3390/ijms232012269
Copyright (c) 2021 Anton Tsukanov
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Footnotes
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Oshchepkov, Dmitry, Irina Chadaeva, Rimma Kozhemyakina, Svetlana Shikhevich, Ekaterina Sharypova, Ludmila Savinkova, Natalya V. Klimova, Anton Tsukanov, Victor G. Levitsky, and Arcady L. Markel. 2022. "Transcription Factors as Important Regulators of Changes in Behavior through Domestication of Gray Rats: Quantitative Data from RNA Sequencing" International Journal of Molecular Sciences 23, no. 20: 12269. https://doi.org/10.3390/ijms232012269 ↩
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Levitsky, V., Zemlyanskaya, E., Oshchepkov, D., Podkolodnaya, O., Ignatieva, E., Grosse, I., Mironova, V., & Merkulova, T. (2019). A single ChIP-seq dataset is sufficient for comprehensive analysis of motifs co-occurrence with MCOT package. Nucleic acids research, 47(21), e139. https://doi.org/10.1093/nar/gkz800 ↩
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Tsukanov, A. V., Mironova, V. V., & Levitsky, V. G. (2022). Motif models proposing independent and interdependent impacts of nucleotides are related to high and low affinity transcription factor binding sites in Arabidopsis. Frontiers in plant science, 13, 938545. https://doi.org/10.3389/fpls.2022.938545 ↩
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Cinar, O., & Viechtbauer, W. (2022). The poolr package for combining independent and dependent p values. Journal of Statistical Software, 101, 1-42. https://doi.org/10.18637/jss.v101.i01 ↩ ↩2
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Castro-Mondragon, J. A., Riudavets-Puig, R., Rauluseviciute, I., Lemma, R. B., Turchi, L., Blanc-Mathieu, R., Lucas, J., Boddie, P., Khan, A., Manosalva Pérez, N., Fornes, O., Leung, T. Y., Aguirre, A., Hammal, F., Schmelter, D., Baranasic, D., Ballester, B., Sandelin, A., Lenhard, B., Vandepoele, K., … Mathelier, A. (2022). JASPAR 2022: the 9th release of the open-access database of transcription factor binding profiles. Nucleic acids research, 50(D1), D165–D173. https://doi.org/10.1093/nar/gkab1113 ↩
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Quinlan, A. R., & Hall, I. M. (2010). BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics (Oxford, England), 26(6), 841–842. https://doi.org/10.1093/bioinformatics/btq033 ↩ ↩2
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Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15(12), 550. https://doi.org/10.1186/s13059-014-0550-8 ↩ ↩2
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Monier, B., McDermaid, A., Wang, C., Zhao, J., Miller, A., Fennell, A., & Ma, Q. (2019). IRIS-EDA: An integrated RNA-Seq interpretation system for gene expression data analysis. PLOS Computational Biology, 15(2), e1006792. https://doi.org/10.1371/journal.pcbi.1006792 ↩ ↩2