Extraction of Mitochondrial Genome from Whole Genome Next Generation Sequencing Data and Unveiling of Forensically Relevant Markers

Abstract RHD and RHCE genes encode Rh blood group antigens and exhibit extensive single-nucleotide polymorphisms and chromosome structural changes in patients with sickle cell disease (SCD). RH variation can drive loss of antigen epitopes or expression of new epitopes, predisposing patients with SCD to Rh alloimmunization. Serologic antigen typing is limited to common Rh antigens, necessitating a genetic approach to detect variant antigen expression. We developed a novel algorithm termed RHtyper for RH genotyping from existing whole-genome sequencing (WGS) data. RHtyper determined RH genotypes in an average of 3.4 and 3.3 minutes per sample for RHD and RHCE, respectively. In a validation cohort consisting of 57 patients with SCD, RHtyper achieved 100% accuracy for RHD and 98.2% accuracy for RHCE, when compared with genotypes obtained by RH BeadChip and targeted molecular assays and after verification by Sanger sequencing and independent next-generation sequencing assays. RHtyper was next applied to WGS data from an additional 827 patients with SCD. In the total cohort of 884 patients, RHtyper identified 38 RHD and 28 RHCE distinct alleles, including a novel RHD DAU allele, RHD* 602G, 733C, 744T 1136T. RHtyper provides comprehensive and high-throughput RH genotyping from WGS data, facilitating deconvolution of the extensive RH genetic variation among patients with SCD. We have implemented RHtyper as a cloud-based public access application in DNAnexus (https://platform.dnanexus.com/app/RHtyper), enabling clinicians and researchers to perform RH genotyping with next-generation sequencing data.

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Computel: Computation of Mean Telomere Length from Whole-Genome Next-Generation Sequencing Data

PLoS ONE ◽

10.1371/journal.pone.0125201 ◽

2015 ◽

Vol 10 (4) ◽

pp. e0125201 ◽

Cited By ~ 24

Author(s):

Lilit Nersisyan ◽

Arsen Arakelyan

Keyword(s):

Next Generation Sequencing ◽

Telomere Length ◽

Next Generation Sequencing Data ◽

Whole Genome ◽

Next Generation ◽

Sequencing Data ◽

Generation Sequencing

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NPSV: A simulation-driven approach to genotyping structural variants in whole-genome sequencing data

GigaScience ◽

10.1093/gigascience/giab046 ◽

2021 ◽

Vol 10 (7) ◽

Author(s):

Michael D Linderman ◽

Crystal Paudyal ◽

Musab Shakeel ◽

William Kelley ◽

Ali Bashir ◽

...

Keyword(s):

Next Generation Sequencing ◽

De Novo ◽

Training Data ◽

Next Generation Sequencing Data ◽

Whole Genome Sequencing Data ◽

Whole Genome ◽

Next Generation ◽

Structural Variants ◽

Sequencing Data ◽

Generation Sequencing

Abstract Background Structural variants (SVs) play a causal role in numerous diseases but are difficult to detect and accurately genotype (determine zygosity) in whole-genome next-generation sequencing data. SV genotypers that assume that the aligned sequencing data uniformly reflect the underlying SV or use existing SV call sets as training data can only partially account for variant and sample-specific biases. Results We introduce NPSV, a machine learning–based approach for genotyping previously discovered SVs that uses next-generation sequencing simulation to model the combined effects of the genomic region, sequencer, and alignment pipeline on the observed SV evidence. We evaluate NPSV alongside existing SV genotypers on multiple benchmark call sets. We show that NPSV consistently achieves or exceeds state-of-the-art genotyping accuracy across SV call sets, samples, and variant types. NPSV can specifically identify putative de novo SVs in a trio context and is robust to offset SV breakpoints. Conclusions Growing SV databases and the increasing availability of SV calls from long-read sequencing make stand-alone genotyping of previously identified SVs an increasingly important component of genome analyses. By treating potential biases as a “black box” that can be simulated, NPSV provides a framework for accurately genotyping a broad range of SVs in both targeted and genome-scale applications.

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