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Nicole Deflaux

Publications and source records attributed to Nicole Deflaux.

2 recordsLinked to original sources

Interactive Analytics for Very Large Scale Genomic Data

Large scale genomic sequencing is now widely used to decipher questions in diverse realms such as biological function, human diseases, evolution, ecosystems, and agriculture. With the quantity and diversity these data harbor, a robust and scalable data handling and analysis solution is desired. Here we present interactive analytics using public cloud infrastructure and distributed computing database Dremel and developed according to the standards of Global Alliance for Genomics and Health, to perform information compression, comprehensive quality controls, and biological information retrieval in large volumes of genomic data. We demonstrate that such computing paradigms can provide orders of magnitude faster turnaround for common analyses, transforming long-running batch jobs submitted via a Linux shell into questions that can be asked from a web browser in seconds.

Preprint

Analysis of protein-coding genetic variation in 60,706 humans

Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. Here we describe the aggregation and analysis of high-quality exome (protein-coding region) sequence data for 60,706 individuals of diverse ethnicities generated as part of the Exome Aggregation Consortium (ExAC). The resulting catalogue of human genetic diversity contains an average of one variant every eight bases of the exome, and provides direct evidence for the presence of widespread mutational recurrence. We show that this catalogue can be used to calculate objective metrics of pathogenicity for sequence variants, and to identify genes subject to strong selection against various classes of mutation; we identify 3,230 genes with near-complete depletion of truncating variants, 72% of which have no currently established human disease phenotype. Finally, we demonstrate that these data can be used for the efficient filtering of candidate disease-causing variants, and for the discovery of human \"knockout\" variants in protein-coding genes.

Genomics