TY  - JOUR
A1  - Baumgarten, Nina
A1  - Rumpf, Laura
A1  - Keßler, Thorsten
A1  - Schulz, Marcel Holger
T1  - A statistical approach for identifying single nucleotide variants that affect transcription factor binding
T2  - iScience
N2  - Highlights
• Single nucleotide variants (SNVs) may affect transcription factor (TF) binding
• Fast statistical approach to assess significance of differential TF binding for SNVs
• Validate new approach on in vitro and in vivo TF binding assays
• Applications on GWAS SNVs and large eQTL studies illustrate utility
Summary
Non-coding variants located within regulatory elements may alter gene expression by modifying transcription factor (TF) binding sites, thereby leading to functional consequences. Different TF models are being used to assess the effect of DNA sequence variants, such as single nucleotide variants (SNVs). Often existing methods are slow and do not assess statistical significance of results. We investigated the distribution of absolute maximal differential TF binding scores for general computational models that affect TF binding. We find that a modified Laplace distribution can adequately approximate the empirical distributions. A benchmark on in vitro and in vivo datasets showed that our approach improves upon an existing method in terms of performance and speed. Applications on eQTLs and on a genome-wide association study illustrate the usefulness of our statistics by highlighting cell type-specific regulators and target genes. An implementation of our approach is freely available on GitHub and as bioconda package.
Y1  - 2024
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/84468
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-844681
SN  - 2589-0042
VL  - 27
IS  - 5, 109765
SP  - 1
EP  - 14
PB  - Elsevier
CY  - Amsterdam
ER  -