BART (Binding Analysis for Regulation of Transcription) is a bioinformatics tool for predicting functional transcription factors (TFs) that bind at genomic cis-regulatory regions to regulate gene expression in the human or mouse genomes, given a query gene set or a ChIP-seq dataset as input. BART leverages 3,485 human TF binding profiles and 3,055 mouse TF binding profiles from the public domain (collected in Cistrome Data Browser) to make the prediction.
BART is implemented in Python and distributed as an open-source package along with necessary data libraries.
BART is developed and maintained by the Chongzhi Zang Lab at the University of Virginia.
We only provide the source code package on github. In order to run BART, you have to download the Data Libraries.
BART web interface (Beta version) can be accessed here.
BART uses Python's distutils tools for source installation. Before installing BART, please make sure either Python2 (Python2.7 or higher is recommended) or Python3 (Python 3.3 or higher is recommended) is installed in the system, and the following python packages are installed:
You can download the Human or Mouse Data Library separately under your own directory. In this case, you have to edit the config file (e.g. BART1.1/BART/bart.conf) after you unpack the source package to provide the directory for the data. For example, if you download the hg38_library.tar.gz (or mm10_library.tar.gz) and unpack it under /path/to/data, then you can modify the bart.conf file as:
hg38_library_dir = /path/to/data/To install a source distribution of BART, unpack the distribution tarball and open up a command terminal. Go to the directory where you unpacked BART, and simply run the install script an install BART globally or locally.
e.g., if you want to install the package BART-v1.1-py3.tar.gz:
$ tar zxf BART-v1.1-py3.tar.gz
$ cd BART-v1.1-py3Install with root/administrator permission (by default, the script will install python library and executable codes globally):
$ python setup.py installIf you want to install everything under your own directory, for example, a directory as /path/to/bart/, use these commands:
$ mkdir -p /path/to/bart/lib/pythonX.Y/site-packages
$ export PYTHONPATH=/path/to/bart/lib/pythonX.Y/site-packages/:$PYTHONPATH
$ python setup.py install --prefix /path/to/bart
$ export PATH=/path/to/bart/bin/:$PATHIn this value, X.Y stands for the major–minor version of Python you are using (such as 2.7 or 3.5 ; you can find this with sys.version[:3] from a Python command line).
You’ll need to add those two lines in your bash file (varies on each platform, usually is ~/.bashrc or ~/.bash_profile) so that you can use the BART command line directly:
$ export PYTHONPATH=/path/to/bart/lib/pythonX.Y/site-packages/:$PYTHONPATH
$ export PATH=/path/to/bart/bin/:$PATH{geneset,profile}
Given a query gene set (at least 100 genes recommended), predict functional transcription factors that regulate these genes.
bart geneset [-h] -i <file> -s <species> [-t <target>] [-p <processes>] [--nonorm] [--outdir <outdir>] [-o <ofilename>]
bart geneset -i name_enhancer_prediction.txt -s hg38 -t target.txt -p 4 --outdir bart_output
-i , --infile
Input file, the name_enhancer_prediction.txt profile generated from MARGE.
-s , --species
Species, please choose from "hg38" or "mm10".
-t , --target
Target transcription factors of interests, please put each TF in one line. BART will generate extra plots showing prediction results for each TF.
-p , --processes
Number of CPUs BART can use.
--nonorm
Whether or not do the standardization for each TF by all of its Wilcoxon statistic scores in our compendium. If set, BART will not do the normalization. Default: FALSE.
--outdir
If specified, all output files will be written to that directory. Default: the current working directory
-o , --ofilename
Name string of output files. Default: the base name of the input file.
The input file for BART geneset, i.e., the enhancer_prediction.txt file generated by MARGE might have these two formats (depends on py2 or py3 version):
- Python2 version:
1 98.19
2 99.76
3 99.76
4 9.49
5 44.37
6 18.14
- Python3 version:
chrom start end UDHSID Score
chr3 175483637 175483761 643494 3086.50
chr3 175485120 175485170 643497 2999.18
chr3 175484862 175485092 643496 2998.28
chr3 175484804 175484854 643495 2976.27
chr3 175491775 175491825 643507 2879.01
chr3 175478670 175478836 643491 2836.90
Given a ChIP-seq data file (bed or bam format mapped reads), predict transcription factors whose binding pattern associates with the input ChIP-seq profile.
bart profile [-h] -i <file> -f <format> [-n <int>] -s <species> [-t <target>] [-p <processes>] [--nonorm] [--outdir <outdir>] [-o <ofilename>]
bart profile -i ChIP.bed -f bed -s hg38 -t target.txt -p 4 --outdir bart_output
-i , --infile
Input ChIP-seq bed or bam file.
-f , --format
Specify "bed" or "bam" format.
-n , --fragmentsize
Fragment size of ChIP-seq reads, in bps. Default: 150.
-s , --species
Species, please choose from "hg38" or "mm10".
-t , --target
Target transcription factors of interests, please put each TF in one line. BART will generate extra plots showing prediction results for each TF.
-p , --processes
Number of CPUs BART can use.
--nonorm
Whether or not do the standardization for each TF by all of its Wilcoxon statistic scores in our compendium. If set, BART will not do the normalization. Default: FALSE.
--outdir
If specified, all output files will be written to that directory. Default: the current working directory
-o , --ofilename
Name string of output files. Default: the base name of input file.
The input file for BART profile should be BED or BAM format in either hg38 or mm10.
Bed is a tab-delimited text file that defines the data lines, and the BED file format is described on UCSC genome browser website. For BED format input, the first three columns should be chrom, chromStart, chromEnd, and the 6th column of strand information is required by BART.
BAM is a binary version of Sequence Alignment/Map(SAM) format, and for more information about BAM custom tracks, please click here.
- name_auc.txt contains the ROC-AUC scores for all TF datasets in human/mouse, we use this score to measure the similarity of TF dataset to cis-regulatory profile, and all TFs are ranked decreasingly by scores. The file should be like this:
AR_56254 AUC = 0.954
AR_44331 AUC = 0.950
AR_44338 AUC = 0.949
AR_50273 AUC = 0.947
AR_44314 AUC = 0.945
AR_44330 AUC = 0.943
AR_50100 AUC = 0.942
AR_44315 AUC = 0.942
AR_50044 AUC = 0.926
AR_50041 AUC = 0.925
FOXA1_50274 AUC = 0.924
AR_50042 AUC = 0.921
- name_bart_results.txt is a ranking list of all TFs, which includes the Wilcoxon statistic score, Wilcoxon p value, standard Wilcoxon statistic score (zscore), maximum ROC-AUC score, rank score (relative rank of z score, p value and max auc) and Irwin Hall p value (p value for the relative rank) for each TF. The most functional TFs of input data are ranked first. The file should be like this:
TF statistic pvalue zscore max_auc re_rank irwin_hall_pvalue
AR 18.654 5.861e-78 3.024 0.954 0.004 3.733e-07
FOXA1 13.272 1.673e-40 2.847 0.924 0.008 2.300e-06
PIAS1 3.987 3.339e-05 2.802 0.872 0.017 2.389e-05
SUMO2 5.213 9.269e-08 3.494 0.749 0.018 2.700e-05
HOXB13 3.800 7.230e-05 2.632 0.909 0.019 3.037e-05
GATA3 5.800 3.316e-09 2.549 0.769 0.025 7.410e-05
TOP1 2.254 1.210e-02 3.057 0.779 0.026 8.063e-05
HDAC3 2.310 1.044e-02 2.478 0.845 0.033 1.682e-04
NR3C1 4.500 3.394e-06 2.042 0.871 0.036 2.160e-04
GATA6 4.240 1.118e-05 2.602 0.632 0.043 3.549e-04
- name_plot is a folder which contains all the extra plots for the TFs listed in target files (target.txt file in test data). For each TF, we have rank dot plot, which shows the rank position of the TF amont all TFs on x-axis and Irwin Hall p value on y-axis (derived from the rank score in name_bart_results.txt), and the cumulative distribution plot, which compares the distribution of ROC-AUC scores from datasets of the TF and the scores of all background datasets (derived from the AUC scores in name_auc.txt).