Whole-genome Next-generation Sequencing Data Research Articles

The complete chloroplast genome of Rhododendron shanii W.P. Fang (Ericaceae), a endemic plant from the Southern Dabie Mountains of China

Rhododendron shanii W.P. Fang 1983 (Ericaceae) is woody plant naturally distributed in the southwest of Anhui, China. The complete chloroplast genome sequence of R. shanii was generated by whole-genome next-generation sequencing data and assembled based on three Rhododendron species chloroplast genome. The complete chloroplast genome sequence of R. shanii was 204,170 bp and divided into four distinct regions: small single-copy region (2615 bp), large single-copy region (107,189 bp), and a pair of inverted repeat regions (47,183 bp). The genome annotation displayed 150 genes, including 95 protein-coding genes, 47 tRNA genes, and eight rRNA genes. Phylogenetic analysis with the Ericaceae reported chloroplast genomes revealed that R. shanii is sister to the clade comprising R. delavayi, R. griersonianum and R. platypodum.

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

BackgroundStructural 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.ResultsWe 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.ConclusionsGrowing 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.

Articles of Significant Interest in This Issue

RNA viruses, such as Venezuelan equine encephalitis virus (VEEV), have high mutation rates that allow for rapid adaptation to selective pressures in their environment. Antiviral compounds exert one such pressure on virus populations during replication. Lee et al. (e00317-20) assessed single-nucleotide polymorphisms and genome network analyses of whole-genome next-generation sequencing data of VEEV while increasing antiviral concentration in two cell types. The trajectory of resistance differed between Vero and SVGA cell types, and the penetration of single-nucleotide polymorphisms was reduced in the SVGA cells. This highlights how population dynamics can be analyzed to understand viral evolution over time.

Evaluation of Phylogenetic Methods for Inferring the Direction of Human Immunodeficiency Virus (HIV) Transmission: HIV Prevention Trials Network (HPTN) 052.

Phylogenetic analysis can be used to assess human immunodeficiency virus (HIV) transmission in populations. We inferred the direction of HIV transmission using whole-genome HIV sequences from couples with known linked infection and known transmission direction. Complete next-generation sequencing (NGS) data were obtained for 105 unique index-partner sample pairs from 32 couples enrolled in the HIV Prevention Trials Network (HPTN) 052 study (up to 2 samples/person). Index samples were obtained up to 5.5 years before partner infection; partner samples were obtained near the time of seroconversion. The bioinformatics method, phyloscanner, was used to infer transmission direction. Analyses were performed using samples from individual sample pairs, samples from all couples (1 sample/person; group analysis), and all available samples (multisample group analysis). Analysis was also performed using NGS data from defined regions of the HIV genome (gag, pol, env). Using whole-genome NGS data, transmission direction was inferred correctly (index to partner) for 98 of 105 (93.3%) of the individual sample pairs, 99 of 105 (94.3%) sample pairs using group analysis, and 31 of the 32 couples (96.9%) using multisample group analysis. There were no cases where the incorrect transmission direction (partner to index) was inferred. The accuracy of the method was higher with greater time between index and partner sample collection. Pol region sequences performed better than env or gag sequences for inferring transmission direction. We demonstrate the potential of a phylogenetic method to infer the direction of HIV transmission between 2 individuals using whole-genome and pol NGS data.

The complete chloroplast genome of Meeboldia yunnanensis (Apiaceae)

Meeboldia yunnanensis Wolff (Apiaceae) is a perennial species naturally distributed in Yunnan and Xizang. The complete chloroplast genome sequence of M. yunnanensis was generated by de novo assembly using whole-genome next-generation sequencing data. The complete chloroplast genome of M. yunnanensis was 154,865 bp in total sequence length and divided into four distinct regions: small single-copy region (17,370 bp), large single-copy region (84,641 bp), and a pair of inverted repeat regions (26,427 bp). The genome annotation displayed a total of 130 genes, including 85 protein-coding genes, 37 tRNA genes, and 8 rRNA genes. Phylogenetic analysis with the reported chloroplast genomes revealed that M. yunnanensis has close relationship to Pterygopleurum neurophyllum.

Evidence of inter-sectional chloroplast capture in Corymbia among sections Torellianae and Maculatae

Chloroplast capture through hybridisation and introgression is well described within Eucalyptus. Despite the propensity of the Corymbia genus (eucalypts) to form hybrids from wide crosses, description of chloroplast capture in Corymbia has, until recently, been limited. In this study our aim was to investigate evidence of intersectional chloroplast capture between sections Torellianae and Maculatae. Using whole-genome next-generation sequencing data, the complete chloroplast genomes were assembled from four Corymbia taxa: Corymbia citriodora subspecies citriodora (Hook.) K.D.Hill & L.A.S.Johnson, Corymbia citriodora subspecies variegata (F.Muell.) A.R.Bean & M.W.McDonald, Corymbia henryi (S.T.Blake) K.D.Hill & L.A.S.Johnson, and Corymbia torelliana (F.Muell.) K.D.Hill & L.A.S.Johnson, represented by eight genotypes. Phylogenetic analysis and comparison among Corymbia chloroplast genomes and nuclear external transcribed spacer (ETS) sequences revealed chloroplast capture among Corymbia species across distinct sections Torellianae and Maculatae within subgenus Blakella. Reticulate evolution, along with Eucalyptus, likely extends into Corymbia as evidenced by incongruent plastid and nuclear phylogenetic trees, suggestive of its importance of hybridisation and introgression during the evolution of eucalypts.

Integrating next-generation sequencing into clinical oncology: strategies, promises and pitfalls

We live in an era of genomic medicine. The past five years brought about many significant achievements in the field of cancer genetics, driven by rapidly evolving technologies and plummeting costs of next-generation sequencing (NGS). The official completion of the Cancer Genome Project in 2014 led many to envision the clinical implementation of cancer genomic data as the next logical step in cancer therapy. Stemming from this vision, the term ‘precision oncology’ was coined to illustrate the novelty of this individualised approach. The basic assumption of precision oncology is that molecular markers detected by NGS will predict response to targeted therapies independently from tumour histology. However, along with a ubiquitous availability of NGS, the complexity and heterogeneity at the individual patient level had to be acknowledged. Not only does the latter present challenges to clinical decision-making based on sequencing data, it is also an obstacle to the rational design of clinical trials. Novel tissue-agnostic trial designs were quickly developed to overcome these challenges. Results from some of these trials have recently demonstrated the feasibility and efficacy of this approach. On the other hand, there is an increasing amount of whole-exome and whole-genome NGS data which allows us to assess ever smaller differences between individual patients with cancer. In this review, we highlight different tumour sequencing strategies currently used for precision oncology, describe their individual strengths and weaknesses, and emphasise their feasibility in different clinical settings. Further, we evaluate the possibility of NGS implementation in current and future clinical trials, and point to the significance of NGS for translational research.

The complete chloroplast genomes of Lilium tsingtauense Gilg (Liliaceae)

Lilium tsingtauense, known as ‘Korean wheel lily’ and ‘Twilight lily’, is a lily species naturally distributed in Korea. The complete chloroplast genome sequences of L. tsingtauense was obtained by de novo assembly using whole-genome next-generation sequencing data. The chloroplast genome of L. tsingtauense was 152,710 bp in length and consisted of four distinct regions, such as large single-copy region (82,059 bp), small single-copy region (17,619 bp) and a pair of inverted repeat regions (26,516 bp). The genome contained a total of 113 genes, including 79 protein-coding genes, 30 tRNA genes and 4 rRNA genes. Phylogenetic analysis with the reported chloroplast genomes revealed that L. tsingtauense is most closely related to Lilium hansonii, a lily species native to Korea.

From FASTQ to Function: In Silico Methods for Processing Next-Generation Sequencing Data.

This chapter presents a method to process C. difficile whole-genome next-generation sequencing data straight from the sequencer. Quality control processing and de novo assembly of these data enable downstream analyses such as gene annotation and in silico multi-locus strain-type identification.

Computel: computation of mean telomere length from whole-genome next-generation sequencing data.

Telomeres are the ends of eukaryotic chromosomes, consisting of consecutive short repeats that protect chromosome ends from degradation. Telomeres shorten with each cell division, leading to replicative cell senescence. Deregulation of telomere length homeostasis is associated with the development of various age-related diseases and cancers. A number of experimental techniques exist for telomere length measurement; however, until recently, the absence of tools for extracting telomere lengths from high-throughput sequencing data has significantly obscured the association of telomere length with molecular processes in normal and diseased conditions. We have developed Computel, a program in R for computing mean telomere length from whole-genome next-generation sequencing data. Computel is open source, and is freely available at https://github.com/lilit-nersisyan/computel. It utilizes a short-read alignment-based approach and integrates various popular tools for sequencing data analysis. We validated it with synthetic and experimental data, and compared its performance with the previously available software. The results have shown that Computel outperforms existing software in accuracy, independence of results from sequencing conditions, stability against inherent sequencing errors, and better ability to distinguish pure telomeric sequences from interstitial telomeric repeats. By providing a highly reliable methodology for determining telomere lengths from whole-genome sequencing data, Computel should help to elucidate the role of telomeres in cellular health and disease.