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Chen, S. L.

Publications and source records attributed to Chen, S. L..

3 recordsLinked to original sources

Long-read whole genome sequencing and comparative analysis of six strains of the human pathogen Orientia tsutsugamushi

BackgroundOrientia tsutsugamushi is a clinically important but neglected obligate intracellular bacterial pathogen of the Rickettsiaceae family that causes the potentially life-threatening human disease scrub typhus. In contrast to the genome reduction seen in many obligate intracellular bacteria, early genetic studies of Orientia have revealed one of the most repetitive bacterial genomes sequenced to date. The dramatic expansion of mobile elements has hampered efforts to generate complete genome sequences using short read sequencing methodologies, and consequently there have been few studies of the comparative genomics of this neglected species.\n\nResultsWe report new high-quality genomes of Orientia tsutsugamushi, generated using PacBio single molecule long read sequencing, for six strains: Karp, Kato, Gilliam, TA686, UT76 and UT176. In comparative genomics analyses of these strains together with existing reference genomes from Ikeda and Boryong strains, we identify a relatively small core genome of 657 genes, grouped into core gene islands and separated by repeat regions, and use the core genes to infer the first whole-genome phylogeny of Orientia.\n\nConclusionsComplete assemblies of multiple Orientia genomes verify initial suggestions that these are remarkable organisms. They have large genomes with widespread amplification of repeat elements and massive chromosomal rearrangements between strains. At the gene level, Orientia has a relatively small set of universally conserved genes, similar to other obligate intracellular bacteria, and the relative expansion in genome size can be accounted for by gene duplication and repeat amplification. Our study demonstrates the utility of long read sequencing to investigate complex bacterial genomes and characterise genomic variation.

genomics

Lacer: Accurate Base Quality Score Recalibration For Improving Variant Calling From Next-Generation Sequencing Data In Any Organism

Next-generation sequencing data is accompanied by quality scores that quantify sequencing error. Inaccuracies in these quality scores propagate through all subsequent analyses; thus base quality score recalibration is a standard step in many next-generation sequencing workflows, resulting in improved variant calls. Current base quality score recalibration algorithms rely on the assumption that sequencing errors are already known; for human resequencing data, relatively complete variant databases facilitate this. However, because existing databases are still incomplete, recalibration is still inaccurate; and most organisms do not have variant databases, exacerbating inaccuracy for non-human data. To overcome these logical and practical problems, we introduce Lacer, which recalibrates base quality scores without assuming knowledge of correct and incorrect bases and without requiring knowledge of common variants. Lacer is the first logically sound, fully general, and truly accurate base recalibrator. Lacer enhances variant identification accuracy for resequencing data of human as well as other organisms (which are not accessible to current recalibrators), simultaneously improving and extending the benefits of base quality score recalibration to nearly all ongoing sequencing projects. Lacer is available at: https://github.com/swainechen/lacer.

genomics

Systematic integration of biomedical knowledge prioritizes drugs for repurposing

The ability to computationally predict whether a compound treats a disease would improve the economy and success rate of drug approval. This study describes Project Rephetio to systematically model drug efficacy based on 755 existing treatments. First, we constructed Hetionet (neo4j.het.io), an integrative network encoding knowledge from millions of biomedical studies. Hetionet v1.0 consists of 47,031 nodes of 11 types and 2,250,197 relationships of 24 types. Data was integrated from 29 public resources to connect compounds, diseases, genes, anatomies, pathways, biological processes, molecular functions, cellular components, pharmacologic classes, side effects, and symptoms. Next, we identified network patterns that distinguish treatments from non-treatments. Then we predicted the probability of treatment for 209,168 compound-disease pairs (het.io/repurpose). Our predictions validated on two external sets of treatment and provided pharmacological insights on epilepsy, suggesting they will help prioritize drug repurposing candidates. This study was entirely open and received realtime feedback from 40 community members.

bioinformatics