Document Type

Article

Publication Date

5-26-2022

Publication Title

Nucleic acids research

Keywords

JGM

JAX Source

Nucleic Acids Res 2022 May 26; 50(W1): W322-9

ISSN

1362-4962

PMID

35639768

DOI

https://doi.org/10.1093/nar/gkac393

Grant

HD103805

Abstract

While great advances in predicting the effects of coding variants have been made, the assessment of non-coding variants remains challenging. This is especially problematic for variants within promoter regions which can lead to over-expression of a gene or reduce or even abolish its expression. The binding of transcription factors to the DNA can be predicted using position weight matrices (PWMs). More recently, transcription factor flexible models (TFFMs) have been introduced and shown to be more accurate than PWMs. TFFMs are based on hidden Markov models and can account for complex positional dependencies. Our new web-based application FABIAN-variant uses 1224 TFFMs and 3790 PWMs to predict whether and to which degree DNA variants affect the binding of 1387 different human transcription factors. For each variant and transcription factor, the software combines the results of different models for a final prediction of the resulting binding-affinity change. The software is written in C++ for speed but variants can be entered through a web interface. Alternatively, a VCF file can be uploaded to assess variants identified by high-throughput sequencing. The search can be restricted to variants in the vicinity of candidate genes. FABIAN-variant is available freely at https://www.genecascade.org/fabian/.

Comments

This is an Open Access article distributed under the terms of the Creative Commons Attribution License

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