title: Statistical Methods for Identifying Sequence Motifs Affecting Point Mutations.
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[Formula: see text]G mutations. We show that major effects of neighbors on germline mutation lie within [Formula: see text] 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[Formula: see text]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.
authors.affiliation: Research School of Biology, The Australian National University, Canberra, Australian Capital Territory 2601, Australia yicheng.zhu@anu.edu.au gavin.huttley@anu.edu.au.
authors.forename: Yicheng
authors.initials: Y
authors.lastname: Zhu
authors.affiliation: Statistical Consulting Unit, The Australian National University, Canberra, Australian Capital Territory 2601, Australia.
authors.forename: Teresa
authors.initials: T
authors.lastname: Neeman
authors.affiliation: ‡Department of Statistics and Applied Probability, National University of Singapore, Singapore 117546, Singapore.
authors.forename: Von Bing
authors.identifier: 0000-0002-5272-5543
authors.initials: VB
authors.lastname: Yap
authors.affiliation: Research School of Biology, The Australian National University, Canberra, Australian Capital Territory 2601, Australia yicheng.zhu@anu.edu.au gavin.huttley@anu.edu.au.
authors.forename: Gavin A
authors.identifier: 0000-0001-7224-2074
authors.initials: GA
authors.lastname: Huttley
mesh_terms: D000818:Animals; D018899:CpG Islands; D003627:Data Interpretation, Statistical; D006801:Humans; D059372:Nucleotide Motifs; D017354:Point Mutation; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software
keywords: 5-methyl-cytosine; bioinformatics; context dependent mutation; germline mutation; log-linear model; mutation spectrum; sequence motif analysis; somatic mutation