Systems and Methods for Multi-Scale, Annotation-Independent Detection of Functionally-Diverse Units of Recurrent Genomic Alteration

摘要

The functional interpretation of somatic mutations remains a persistent challenge in the interpretation of human genome data. Systems and methods for detecting significantly mutated regions (SMRs) in the human genome permit the discovery and identification of multi-scale cancer-driving mutational hotspot clusters. Systems and methods of SMR detection reveal differentially mutated genetic regions across various cancer types. SMR detection and annotation reveals a diverse spectrum of functional elements in the genome, including at least single amino acids, compete coding exons and protein domains, microRNAs, transcription factor binding sites, splice sites, and untranslated regions. Systems and methods of SMR detection optionally including protein structure mapping uncover recurrent somatic alterations within proteins. Systems and methods of SMR detection optionally including differential expression analysis reveal previously unappreciated connections between recurrent and somatic mutations and molecular signatures.

主权项

1. A method of detecting significantly mutated regions in a genome using a SMR detection system, the method comprising:
receiving exome data describing information regarding whole exome sequences and gene-level features for a plurality of samples using a SMR detection system; receiving whole genome data describing information regarding whole genome sequences for a population using the SMR detection system; for each gene in the whole exome sequences, identifying mutations in the plurality of samples based on a mutation probability model using the SMR detection system, wherein the mutation probability model describes gene level features and background mutation probabilities in the whole genome sequences; detecting at least one mutation cluster in the plurality of samples using a spatial clustering technique using the SMR detection system, wherein the detected mutation clusters comprise spatially-proximal sets of mutations within domains; detecting at least one significantly mutated region by filtering the detected mutation clusters based on a false discovery rate threshold using the SMR detection system; annotating the detected at least one significantly mutated region in the exome data using the SMR detection system.