Statistical Methods for Identifying Sequence Motifs Affecting Point Mutations [Population and Evolutionary Genetics]

We present new log-linear models that allow explicit examination of these questions, along with sequence logo style visualization to enable identifying specific motifs. We demonstrate the performance of these methods by analyzing mutation processes in human germline and malignant melanoma. We recapitulate the known CpG effect, and identify novel motifs, including a highly significant motif associated with A$$\to $$G mutations. We show that major effects of neighbors on germline mutation lie within $$\pm 2$$ of the mutating base. Models are also presented for contrasting the entire mutation spectra (the distribution of the different point mutations). We show the spectra vary significantly between autosomes and X-chromosome, with a difference in T$$\to $$C transition dominating. Analyses of malignant melanoma confirmed reported characteristic features of this cancer, including statistically significant strand asymmetry, and markedly different neighboring influences. The methods we present are made freely available as a Python library https://bitbucket.org/pycogent3/mutationmotif.
Source: Genetics - Category: Genetics & Stem Cells Authors: Tags: Population and Evolutionary Genetics Source Type: research