General Information
| Description | Analysis and modeling of transcription factor binding sites using a motif-centric genomic footprinter |
| Homepage | https://bitbucket.org/bryancquach/defcom |
Publications
DeFCoM: analysis and modeling of transcription factor binding sites using a motif-centric genomic footprinter
Motivation:
Identifying the locations of transcription factor binding sites is critical for understanding how gene transcription is regulated across different cell types and conditions. Chromatin accessibility experiments such as DNaseI sequencing (DNase-seq) and Assay for Transposase Accessible Chromatin sequencing (ATAC-seq) produce genome-wide data that include distinct ‘footprint’ patterns at binding sites. Nearly all existing computational methods to detect footprints from these data assume that footprint signals are highly homogeneous across footprint sites. Additionally, a comprehensive and systematic comparison of footprinting methods for specifically identifying which motif sites for a specific factor are bound has not been performed.
Results:
Using DNase-seq data from the ENCODE project, we show that a large degree of previously uncharacterized site-to-site variability exists in footprint signal across motif sites for a transcription factor. To model this heterogeneity in the data, we introduce a novel, supervised learning footprinter called Detecting Footprints Containing Motifs (DeFCoM). We compare DeFCoM to nine existing methods using evaluation sets from four human cell-lines and eighteen transcription factors and show that DeFCoM outperforms current methods in determining bound and unbound motif sites. We also analyze the impact of several biological and technical factors on the quality of footprint predictions to highlight important considerations when conducting footprint analyses and assessing the performance of footprint prediction methods. Finally, we show that DeFCoM can detect footprints using ATAC-seq data with similar accuracy as when using DNase-seq data.
Availability and Implementation:
Python code available at https://bitbucket.org/bryancquach/defcom
Contact:
bquach@email.unc.edu or tsfurey@email.unc.edu
Supplementary information:
Supplementary data are available at Bioinformatics online.
Related Tools
Ceres
Published December 2009
Software for the integrated analysis of transcription factor binding sites and nucleosome positions in Saccharomyces cer...
BinDNase
Published May 2015
A discriminatory approach for transcription factor binding prediction using DNase I hypersensitivity data
Romulus
Published April 2016
Robust multi-state identification
of transcription factor binding sites from
DNase-seq data
FAIR Evaluation
Sorry, you must be signed in to evaluate this object.
