BARTweb: a web server for transcription factor association analysis
Ma, W.; Wang, Z.; Zhang, Y.; Magee, N. E.; Chen, Y.; Zang, C.
Show abstract
Identifying active transcription factors (TFs) that bind to cis-regulatory regions in the genome to regulate differential gene expression is a key task in gene regulation research. TF binding profiles from numerous existing ChIP-seq data can be utilized for association analysis with query data for TF identification, as alternative to DNA sequence motif analysis. Here, we present BARTweb, an interactive webserver for identifying TFs whose genomic binding patterns associate with input genomic features, by leveraging over 13,000 public ChIP-seq datasets for human and mouse. Using an updated Binding Analysis for Regulation of Transcription (BART) algorithm, BARTweb can identify functional TFs that regulate a gene set, or have a binding profile correlated with a ChIP-seq profile or enriched in a genomic region set, without a priori information of the cell type. Compared with the original BART package, BARTweb substantially reduces the execution time of a typical job by two orders of magnitude. We also show that BARTweb outperforms other existing tools in identifying true TFs from collected experimental data. BARTweb is a useful webserver for performing functional analysis of gene regulation. BARTweb is freely available at http://bartweb.org.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Expanding the coverage of regulons from high-confidence prior knowledge for accurate estimation of transcription factor activities 96%
- Best practices for perturbation MPRA--a computational evaluation framework of sequence design strategies 96%
- Systematic Prediction of Regulatory Motifs from Human ChIP-Sequencing Data Based on a Deep Learning Framework 95%
Similar papers in this journal
- RGT: a toolbox for the integrative analysis of high throughput regulatory genomics data 96%
- GeneSetCluster 2.0: a comprehensive toolset for summarizing and integrating gene-sets analysis 96%
- Comparative analysis of ChIP-exo peak-callers: impact of data quality, read duplication and binding subtypes 95%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.