A method to build extended sequence context models of point mutations and indels

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 using IUPAC patterns. 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. Revealing that for some mutation types, the mutation rate of a position is significantly affected by nucleotides that are up to four base pairs away. As the first method of its kind, it does not only predict rates for point mutations but also indels. 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 constraint, e.g. haploinsufficient genes.

Regularized sequence-context mutational trees capture variation in mutation rates across the human genome

“…Here, we present Baymer, a Bayesian method to model mutation rate variation that computationally scales to large windows of nucleotide sequence context, robustly manages sparse data through an efficient regularization strategy, and emits posterior probabilities that capture uncertainty in estimated probabilities. Consistent with previous studies [24][25][26] , we show that expanded sequence context models in most current human datasets are overfit with classic empirical methods but considerably improve model performance when properly regularized. As a result, this method allows for renewed evaluation of experiments that originally were statistically limited to polymorphism probability models with small sequence context windows.…”

“…2E). This implies a considerable impact on polymorphism probabilities in extended sequence contexts, consistent with previous work 19,[23][24][25] . This general trend is similarly consistent across mutation types (Fig.…”

“…Previous work has sought to address these challenges, though methods introduced to date do not address all limitations simultaneously. Sparsity and scalability have been tackled through a deep-learning framework 24 as well as an IUPAC-motif-based clustering approach 25 which modeled polymorphism probabilities up through 9-mers. Another method explored polymorphism probabilities up through 7-mers using DNA shape covariates to reduce the parameter space 26 .…”

A mutation rate model at the basepair resolution identifies the mutagenic effect of Polymerase III transcription

“…The incorporation of transcription and replication directions makes the model strand-dependent with unequal rates for mutations of the same type on the two DNA strands. To our knowledge, strand-dependency has not been incorporated into existing context-dependent and regional mutation models 1,10,22 . In addition to the known epigenomic features listed above, unexplained regional variation in the mutation rate has been observed 17,18 .…”