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Durbin, D.

Publications and source records attributed to Durbin, D..

2 recordsLinked to original sources

Genomic sequence characteristics and the empiric accuracy of short-read sequencing

BackgroundShort-read whole genome sequencing (WGS) is a vital tool for clinical applications and basic research. Genetic divergence from the reference genome, repetitive sequences, and sequencing bias, reduce the performance of variant calling using short-read alignment, but the loss in recall and specificity has not been adequately characterized. For the clonal pathogen Mycobacterium tuberculosis (Mtb), researchers frequently exclude 10.7% of the genome believed to be repetitive and prone to erroneous variant calls. To benchmark short-read variant calling, we used 36 diverse clinical Mtb isolates dually sequenced with Illumina short-reads and PacBio long-reads. We systematically study the short-read variant calling accuracy and the influence of sequence uniqueness, reference bias, and GC content. [a] ResultsReference based Illumina variant calling had a recall [≥]89.0% and precision [≥]98.5% across parameters evaluated. The best balance between precision and recall was achieved by tuning the mapping quality (MQ) threshold, i.e. confidence of the read mapping (recall 85.8%, precision 99.1% at MQ [≥] 40). Masking repetitive sequence content is an alternative conservative approach to variant calling that maintains high precision (recall 70.2%, precision 99.6% at MQ[≥]40). Of the genomic positions typically excluded for Mtb, 68% are accurately called using Illumina WGS including 52 of the 168 PE/PPE genes (34.5%). We present a refined list of low confidence regions and examine the largest sources of variant calling error. ConclusionsOur improved approach to variant calling has broad implications for the use of WGS in the study of Mtb biology, inference of transmission in public health surveillance systems, and more generally for WGS applications in other organisms.

bioinformatics↗

In-host population dynamics of M. tuberculosis during treatment failure

BackgroundTuberculosis (TB) is a leading cause of death globally from an infectious agent. Understanding the population dynamics of TBs causative agent Mycobacterium tuberculosis (Mtb) in-host is vital for understanding the efficacy of antibiotic treatment. Here we use longitudinally collected clinical Mtb isolates that underwent Whole-Genome Sequencing (WGS) from the sputa of 307 subjects to investigate Mtb diversity during the course of active TB disease. Methods and findingsWe excluded cases suspected of reinfection or contamination to analyze data from 200 subjects, 167 of which met microbiological criteria for delayed culture conversion, treatment failure or relapse. Using technical and biological replicate samples, we defined an allele frequency threshold attributable to in-host evolution. Of the 167 subjects with unsuccessful treatment outcome, 27 (16%) developed new resistance mutations between sampling with 20/27 (74%) occurring in patients with pre-existing antibiotic resistance. Low abundance resistance variants at a purity of [≥]19% in the first isolate predicts fixation of these variants in the subsequent sample with 27.0% sensitivity and 95.8% specificity. We identify significant in-host variation in seven genes associated with antibiotic resistance and twenty other genes, including metabolic genes and genes known to modulate host innate immunity by interacting with TLR2. We confirm Rv0095c, Rv1944c, PPE18, PPE54 and PPE60 to be under positive selection by assessing phylogenetic convergence across a global and genetically diverse independent sample of 20,352 isolates. ConclusionsOur large sample provides a comprehensive picture of the mutational dynamics in-host during active TB disease. We demonstrate a framework to study temporal changes in Mtb population diversity using average depth WGS data. We show that minor variants can be used to inform antibiotic treatment regimens in patients with TB. Furthermore, we detect a signature of positive selection in-host, possibly stemming from innate immune pressure and informing our understanding of host-pathogen interactions.

genomics↗