Positional weight matrices have sufficient prediction power for analysis of noncoding variants
Alexandr Boytsov1,2, Sergey Abramov1,2, Vsevolod J Makeev1,2
1Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, 119991, Russian Federation.
F1000Research
|July 18, 2022
Summary
Position weight matrices accurately predict transcription factor DNA binding, even for variants. Our re-analysis shows these models are effective for regulatory genomics and variant impact prediction.
Area of Science:
- Genomics
- Molecular Biology
- Bioinformatics
Background:
- Position weight matrices (PWMs) model transcription factor binding specificity to DNA.
- PWMs are crucial in regulatory genomics and predicting single-nucleotide variant impacts.
- Recent studies questioned PWM predictive power for variants using SNP-SELEX.
Purpose of the Study:
- To re-evaluate the predictive power of position weight matrices.
- To assess PWM efficacy in quantifying transcription factor binding to alternative DNA alleles.
- To address discrepancies highlighted by recent SNP-SELEX analyses.
Main Methods:
- Re-analysis of the experimental dataset from Yan et al. (SNP-SELEX).
- Selection and application of appropriate position weight matrices.
- Comparison of predicted binding affinities with experimental data for alternative alleles.
Main Results:
- Appropriately selected position weight matrices demonstrate adequate predictive power.
- PWMs can effectively quantify transcription factor binding to alternative alleles.
- The study refutes the claim that most PWMs lack predictive power for regulatory variants.
Conclusions:
- Position weight matrices remain a valuable tool in regulatory genomics.
- PWMs can reliably predict the impact of genetic variants on transcription factor binding.
- The choice of PWM is critical for accurate analysis of regulatory variants.
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