Lawrence, M. — Page 2 | bioRxiv Science

bioRxiv2018

plyranges: A grammar of genomic data transformation

The Bioconductor project provides many interoperable data abstractions for analyzing high-throughput genomics experiments; however implementing a typical genomic workflow with Bioconductor requires learning these abstractions and understanding them at an integrative level. This places a large cognitive burden on the user, especially for non-programmers. To reduce this burden we have created a grammar of genomic data transformation that operates on a single, central Bioconductor data structure, GRanges, which naturally represents genomic intervals and their associated measurements. The grammar defines verbs for performing actions on and between genomic interval data through a simplified, coherent interface to existing Bioconductor infrastructure, resulting in fluent analysis workflows. We have implemented this grammar as an R/Bioconductor package called plyranges.

bioinformatics↗

bioRxiv2017

Detecting cancer vulnerabilities through gene networks under purifying selection in 4,700 cancer genomes

Large-scale cancer sequencing studies have uncovered dozens of mutations critical to cancer initiation and progression. However, a significant proportion of genes linked to tumor propagation remain hidden, often due to noise in sequencing data confounding low frequency alterations. Further, genes in networks under purifying selection (NPS), or those that are mutated in cancers less frequently than would be expected by chance, may play crucial roles in sustaining cancers but have largely been overlooked. We describe here a statistical framework that identifies genes that have a first order protein interaction network significantly depleted for mutations, to elucidate key genetic contributors to cancers. Not reliant on and thus, unbiased by, the gene of interests mutation rate, our approach has identified 685 putative genes linked to cancer development. Comparative analysis indicates statistically significant enrichment of NPS genes in previously validated cancer vulnerability gene sets, while further identifying novel cancer-specific candidate gene targets. As more tumor genomes are sequenced, integrating systems level mutation data through this network approach should become increasingly useful in pinpointing gene targets for cancer diagnosis and treatment.

bioinformatics↗