ArticleNature communications2022
A method to build extended sequence context models of point mutations and indels.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.
- Meta-analysis of 46,000 germline de novo mutations linked to human inherited disease.Human genomics · 2024Pooled it
- Determinants of mutation susceptibility along the genome are largely invariant across human tissues.Genome biology · 2026Article
- MuRaL-indel: a deep learning framework for building insertion and deletion mutation rate maps.Briefings in bioinformatics · 2026Article
- A practical framework for predicting splicing single nucleotide variants in exome sequencing.NAR genomics and bioinformatics · 2025Article
- Joint, multifaceted genomic analysis enables diagnosis of diverse, ultra-rare monogenic presentations.Nature communications · 2025Article
- Sliding Window Interaction Grammar (SWING): a generalized interaction language model for peptide and protein interactions.Nature methods · 2025Article
- K-mer-based Approaches to Bridging Pangenomics and Population Genetics.Molecular biology and evolution · 2025Review
- A mutation rate model at the basepair resolution identifies the mutagenic effect of polymerase III transcription.Nature genetics · 2023Article
- Regularized sequence-context mutational trees capture variation in mutation rates across the human genome.PLoS genetics · 2023Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The mutation rate of a specific position in the human genome depends on the sequence context surrounding it. Modeling the mutation rate by estimating a rate for each possible k-mer, however, only works for small values of k since the data becomes too sparse for larger values of k. Here we propose a new method that solves this problem by grouping similar k-mers. We refer to the method as k-mer pattern partition and have implemented it in a software package called kmerPaPa. We use a large set of human de novo mutations to show that this new method leads to improved prediction of mutation rates and makes it possible to create models using wider sequence contexts than previous studies. As the first method of its kind, it does not only predict rates for point mutations but also insertions and deletions. We have additionally created a software package called Genovo that, given a k-mer pattern partition model, predicts the expected number of synonymous, missense, and other functional mutation types for each gene. Using this software, we show that the created mutation rate models increase the statistical power to detect genes containing disease-causing variants and to identify genes under strong selective constraint.
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