Insights gained from a comprehensive all-against-all transcription factor binding motif benchmarking study.
Details
Serval ID
serval:BIB_2246D7C89D46
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Insights gained from a comprehensive all-against-all transcription factor binding motif benchmarking study.
Journal
Genome biology
ISSN
1474-760X (Electronic)
ISSN-L
1474-7596
Publication state
Published
Issued date
11/05/2020
Peer-reviewed
Oui
Volume
21
Number
1
Pages
114
Language
english
Notes
Publication types: Evaluation Study ; Journal Article ; Research Support, Non-U.S. Gov't
Publication Status: epublish
Publication Status: epublish
Abstract
Positional weight matrix (PWM) is a de facto standard model to describe transcription factor (TF) DNA binding specificities. PWMs inferred from in vivo or in vitro data are stored in many databases and used in a plethora of biological applications. This calls for comprehensive benchmarking of public PWM models with large experimental reference sets.
Here we report results from all-against-all benchmarking of PWM models for DNA binding sites of human TFs on a large compilation of in vitro (HT-SELEX, PBM) and in vivo (ChIP-seq) binding data. We observe that the best performing PWM for a given TF often belongs to another TF, usually from the same family. Occasionally, binding specificity is correlated with the structural class of the DNA binding domain, indicated by good cross-family performance measures. Benchmarking-based selection of family-representative motifs is more effective than motif clustering-based approaches. Overall, there is good agreement between in vitro and in vivo performance measures. However, for some in vivo experiments, the best performing PWM is assigned to an unrelated TF, indicating a binding mode involving protein-protein cooperativity.
In an all-against-all setting, we compute more than 18 million performance measure values for different PWM-experiment combinations and offer these results as a public resource to the research community. The benchmarking protocols are provided via a web interface and as docker images. The methods and results from this study may help others make better use of public TF specificity models, as well as public TF binding data sets.
Here we report results from all-against-all benchmarking of PWM models for DNA binding sites of human TFs on a large compilation of in vitro (HT-SELEX, PBM) and in vivo (ChIP-seq) binding data. We observe that the best performing PWM for a given TF often belongs to another TF, usually from the same family. Occasionally, binding specificity is correlated with the structural class of the DNA binding domain, indicated by good cross-family performance measures. Benchmarking-based selection of family-representative motifs is more effective than motif clustering-based approaches. Overall, there is good agreement between in vitro and in vivo performance measures. However, for some in vivo experiments, the best performing PWM is assigned to an unrelated TF, indicating a binding mode involving protein-protein cooperativity.
In an all-against-all setting, we compute more than 18 million performance measure values for different PWM-experiment combinations and offer these results as a public resource to the research community. The benchmarking protocols are provided via a web interface and as docker images. The methods and results from this study may help others make better use of public TF specificity models, as well as public TF binding data sets.
Keywords
Animals, Benchmarking, Chromatin Immunoprecipitation Sequencing, Humans, Mice, Protein Interaction Domains and Motifs, Software, Transcription Factors/metabolism, ChIP-seq, HT-SELEX, PBM, PWM, Transcription factor binding sites
Pubmed
Web of science
Open Access
Yes
Create date
24/04/2021 12:25
Last modification date
08/08/2024 7:30