Positional weight matrices have sufficient prediction power for analysis of noncoding variants [version 2; peer review: 2 approved]

The position weight matrix, also called the position-specific scoring matrix, is the commonly accepted model to quantify the specificity of transcription factor binding to DNA. Position weight matrices are used in thousands of projects and software tools in regulatory genomics, including computation...

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Bibliographic Details
Main Authors: Sergey Abramov, Alexandr Boytsov, Vsevolod J. Makeev, Ivan V. Kulakovskiy
Format: Article
Language:English
Published: F1000 Research Ltd 2022-06-01
Series:F1000Research
Subjects:
Online Access:https://f1000research.com/articles/11-33/v2
Description
Summary:The position weight matrix, also called the position-specific scoring matrix, is the commonly accepted model to quantify the specificity of transcription factor binding to DNA. Position weight matrices are used in thousands of projects and software tools in regulatory genomics, including computational prediction of the regulatory impact of single-nucleotide variants. Yet, recently Yan et al. reported that "the position weight matrices of most transcription factors lack sufficient predictive power" if applied to the analysis of regulatory variants studied with a newly developed experimental method, SNP-SELEX. Here, we re-analyze the rich experimental dataset obtained by Yan et al. and show that appropriately selected position weight matrices in fact can adequately quantify transcription factor binding to alternative alleles.
ISSN:2046-1402