Details

Autor(en) / Beteiligte

• Boytsov, Alexandr
• Abramov, Sergey
• Makeev, Vsevolod J.
• Kulakovskiy, Ivan V.

Titel

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

Ist Teil von

• F1000 research, 2022, Vol.11, p.33

Ort / Verlag

F1000 Research Ltd

Erscheinungsjahr

2022

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• 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.