Statistical methods for identifying sequence motifs affecting point mutations

Yicheng Zhu*, Teresa Neeman, Von Bing Yap, Gavin A. Huttley

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)

Abstract

Mutation processes differ between types of point mutation, genomic locations, cells, and biological species. For some point mutations, specific neighboring bases are known to be mechanistically influential. Beyond these cases, numerous questions remain unresolved, including: what are the sequence motifs that affect point mutations? How large are the motifs? Are they strand symmetric? And, do they vary between samples? 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→G mutations. We show that major effects of neighbors on germline mutation lie within ±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→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.

Original languageEnglish
Pages (from-to)843-856
Number of pages14
JournalGenetics
Volume205
Issue number2
DOIs
Publication statusPublished - Feb 2017

Fingerprint

Dive into the research topics of 'Statistical methods for identifying sequence motifs affecting point mutations'. Together they form a unique fingerprint.

Cite this