Biology subjects

Aaron R Quinlan

Publications and source records attributed to Aaron R Quinlan.

3 recordsLinked to original sources

bioRxiv2016

Who’s who? Detecting and resolving sample anomalies in human DNA sequencing studies with peddy

The potential for genetic discovery in human DNA sequencing studies is greatly diminished if DNA samples from the cohort are mislabelled, swapped, contaminated, or include unintended individuals. Unfortunately, the potential for such errors is significant since DNA samples are often manipulated by several protocols, labs or scientists in the process of sequencing. We have developed peddy to identify and facilitate the remediation of such errors via interactive visualizations and reports comparing the stated sex, relatedness, and ancestry to what is inferred from each individuals genotypes. Peddy predicts a samples ancestry using a machine learning model trained on individuals of diverse ancestries from the 1000 Genomes Project reference panel. Peddys speed, text reports and web interface facilitate both automated and visual detection of sample swaps, poor sequencing quality and other indicators of sample problems that, were they left undetected, would inhibit discovery.\n\nSoftware Availabilityhttps://github.com/brentp/peddy\n\nDemonstration (Chrome suggested)http://home.chpc.utah.edu/[~]u6000771//plots/ceph1463.html

Genomics↗

bioRxiv2015

Efficient genotype compression and analysis of large genetic variation datasets

The economy of human genome sequencing has catalyzed ambitious efforts to interrogate the genomes of large cohorts in search of new insight into the genetic basis of disease. This manuscript introduces Genotype Query Tools (GQT) as a new indexing strategy and toolset that addresses an analytical bottleneck by enabling interactive analyses based on genotypes, phenotypes and sample relationships. Speed improvements are achieved by operating directly on a compressed genotype index without decompression. GQTs data compression ratios increase favorably with cohort size and relative analysis performance improves in kind. We demonstrate substantial performance improvements over state-of-the-art tools using datasets from the 1000 Genomes Project (46 fold), the Exome Aggregation Consortium (443 fold), and simulated datasets of up to 100,000 genomes (218 fold). Furthermore, we show that this indexing strategy facilitates population and statistical genetics measures such as principal component analysis and burden tests. Based on its computational efficiency and by complementing existing toolsets, GQT provides a flexible framework for current and future analyses of massive genome datasets.

Genomics↗

bioRxiv2014

SpeedSeq: Ultra-fast personal genome analysis and interpretation

Comprehensive interpretation of human genome sequencing data is a challenging bioinformatic problem that typically requires weeks of analysis, with extensive hands-on expert involvement. This informatics bottleneck inflates genome sequencing costs, poses a computational burden for large-scale projects, and impedes the adoption of time-critical clinical applications such as personalized cancer profiling and newborn disease diagnosis, where the actionable timeframe can measure in hours or days. We developed SpeedSeq, an open-source genome analysis platform that vastly reduces computing time. SpeedSeq accomplishes read alignment, duplicate removal, variant detection and functional annotation of a 50X human genome in <24 hours, even using one low-cost server. SpeedSeq offers competitive or superior performance to current methods for detecting germline and somatic single nucleotide variants (SNVs), indels, and structural variants (SVs) and includes novel functionality for SV genotyping, SV annotation, fusion gene detection, and rapid identification of actionable mutations. SpeedSeq will help bring timely genome analysis into the clinical realm.\n\nAvailability: SpeedSeq is available at https://github.com/cc2qe/speedseq.

Bioinformatics↗