Short-read Next-generation Sequencing Data Research Articles

A new method for ecologists to estimate heterozygote excess and deficit for multi-locus gene families.

The fixation index, F IS, has been a staple measure to detect selection, or departures from random mating in populations. However, current Next Generation Sequencing (NGS) cannot easily estimate F IS, in multi-locus gene families that contain multiple loci having similar or identical arrays of variant sequences of ≥1 kilobase (kb), which differ at multiple positions. In these families, high-quality short-read NGS data typically identify variants, but not the genomic location, which is required to calculate F IS (based on locus-specific observed and expected heterozygosity). Thus, to assess assortative mating, or selection on heterozygotes, from NGS of multi-locus gene families, we need a method that does not require knowledge of which variants are alleles at which locus in the genome. We developed such a method. Like F IS, our novel measure, 1 H IS, is based on the principle that positive assortative mating, or selection against heterozygotes, and some other processes reduce within-individual variability relative to the population. We demonstrate high accuracy of 1 H IS on a wide range of simulated scenarios and two datasets from natural populations of penguins and dolphins. 1 H IS is important because multi-locus gene families are often involved in assortative mating or selection on heterozygotes. 1 H IS is particularly useful for multi-locus gene families, such as toll-like receptors, the major histocompatibility complex in animals, homeobox genes in fungi and self-incompatibility genes in plants.

An assessment of bioinformatics tools for the detection of human endogenous retroviral insertions in short-read genome sequencing data.

There is a growing interest in the study of human endogenous retroviruses (HERVs) given the substantial body of evidence that implicates them in many human diseases. Although their genomic characterization presents numerous technical challenges, next-generation sequencing (NGS) has shown potential to detect HERV insertions and their polymorphisms in humans. Currently, a number of computational tools to detect them in short-read NGS data exist. In order to design optimal analysis pipelines, an independent evaluation of the available tools is required. We evaluated the performance of a set of such tools using a variety of experimental designs and datasets. These included 50 human short-read whole-genome sequencing samples, matching long and short-read sequencing data, and simulated short-read NGS data. Our results highlight a great performance variability of the tools across the datasets and suggest that different tools might be suitable for different study designs. However, specialized tools designed to detect exclusively human endogenous retroviruses consistently outperformed generalist tools that detect a wider range of transposable elements. We suggest that, if sufficient computing resources are available, using multiple HERV detection tools to obtain a consensus set of insertion loci may be ideal. Furthermore, given that the false positive discovery rate of the tools varied between 8% and 55% across tools and datasets, we recommend the wet lab validation of predicted insertions if DNA samples are available.

Improved Assembly of Metagenome-Assembled Genomes and Viruses in Tibetan Saline Lake Sediment by HiFi Metagenomic Sequencing.

With the development and reduced costs of high-throughput sequencing technology, environmental dark matter, such as novel metagenome-assembled genomes (MAGs) and viruses, is now being discovered easily. However, due to read length limitations, MAGs and viromes often suffer from genome discontinuity and deficiencies in key functional elements. Here, by applying long-read sequencing technology to sediment samples from a Tibetan saline lake, we comprehensively analyzed the performance of high-fidelity (HiFi) reads and the possibility of integration with short-read next-generation sequencing (NGS) data. In total, 207 full-length nonredundant 16S rRNA gene sequences and 19 full-length nonredundant 18S rRNA genes were directly obtained from HiFi reads, which greatly surpassed the retrieval performance of NGS technology. We carried out a cross-sectional comparison among multiple assembly strategies, referred to as 'NGS', 'Hybrid (NGS+HiFi)', and 'HiFi'. Two MAGs and 29 viruses with circular genomes were reconstructed using HiFi reads alone, indicating the great power of the 'HiFi' approach to assemble high-quality microbial genomes. Among the 3 strategies, the 'Hybrid' approach produced the highest number of medium/high-quality MAGs and viral genomes, while the ratio of MAGs containing 16S rRNA genes was significantly improved in the 'HiFi' assembly results. Overall, our study provides a practical metagenomic resolution for analyzing complex environmental samples by taking advantage of both the short-read and HiFi long-read sequencing methods to extract the maximum amount of information, including data on prokaryotes, eukaryotes, and viruses, via the 'Hybrid' approach. IMPORTANCE To expand the understanding of microbial dark matter in the environment, we did the first comparative evaluation of multiple assembly strategies based on high-throughput short-read and HiFi data from lake sediments metagenomic sequencing. The results demonstrated great improvement of the 'Hybrid' assembly method (short-read next-generation sequencing data plus HiFi data) in the recovery of medium/high-quality MAGs and viral genomes. Further analysis showed that HiFi data is important to retrieve the complete circular prokaryotic and viral genomes. Meanwhile, hundreds of full-length 16S/18S rRNA genes were assembled directly from HiFi data, which facilitated the species composition studies of complex environmental samples, especially for understanding micro-eukaryotes. Therefore, the application of the latest HiFi long-read sequencing could greatly improve the metagenomic assembly integrity and promote environmental microbiome research.

Overcoming the Pitfalls of Next-Generation Sequencing–Based Molecular Diagnosis of Shwachman-Diamond Syndrome

Shwachman-Diamond syndrome (SDS) is the second most common cause of exocrine pancreatic insufficiency, and 90% of patients carry mutations in the SBDS gene, the most common being the c.183_184delinsCT and c.258+2T>C variants. However, precise detection of these most contributory variants by conventional short-read next-generation sequencing data analysis is limited because of the SBDS/SBDSP1 highly homologous sequences. In this study, an efficient approach was established to infer the haplotype of SBDS based on the expectation-maximization algorithm. The workflow was retrospectively applied to detect the two most common SBDS variants in a Chinese SDS high-risk cohort, and a systematic comparison of variant detection results was performed between the workflow and conventional next-generation sequencing analysis based on Sanger sequencing validation. Among the Chinese SDS high-risk cohort (n = 47) and their available parents (n = 64), the established workflow improved the diagnostic rate for these two variants by 27.7% (95% CI, 15.6%-42.6%) compared with conventional analysis. For overall variant detection, the established workflow achieved 100% (95% CI, 92.5%-100%) concordance with Sanger sequencing, whereas conventional analysis showed only 65.8% accuracy; these results included 25.2% with missed variant calls, 7.2% with diagnosed but inaccurate variant calls, and 1.8% with false-positive calls. With its favorable result in both SDS patient diagnosis and carrier detection performance, the provided workflow showed its potential in clinical application for SDS molecular diagnosis.

Detection of somatic structural variants from short-read next-generation sequencing data.

Somatic structural variants (SVs), which are variants that typically impact >50 nucleotides, play a significant role in cancer development and evolution but are notoriously more difficult to detect than small variants from short-read next-generation sequencing (NGS) data. This is due to a combination of challenges attributed to the purity of tumour samples, tumour heterogeneity, limitations of short-read information from NGS and sequence alignment ambiguities. In spite of active development of SV detection tools (callers) over the past few years, each method has inherent advantages and limitations. In this review, we highlight some of the important factors affecting somatic SV detection and compared the performance of seven commonly used SV callers. In particular, we focus on the extent of change in sensitivity and precision for detecting different SV types and size ranges from samples with differing variant allele frequencies and sequencing depths of coverage. We highlight the reasons for why some SV callers perform well in some settings but not others, allowing our evaluation findings to be extended beyond the seven SV callers examined in this paper. As the importance of large SVs become increasingly recognized in cancer genomics, this paper provides a timely review on some of the most impactful factors influencing somatic SV detection that should be considered when choosing SV callers.

Software Dedicated to Virus Sequence Analysis "Bioinformatics Goes Viral".

Computer-assisted technologies of the genomic structure, biological function, and evolution of viruses remain a largely neglected area of research. The attention of bioinformaticians to this challenging field is currently unsatisfying in respect to its medical and biological importance. The power of new genome sequencing technologies, associated with new tools to handle “big data”, provides unprecedented opportunities to address fundamental questions in virology. Here, we present an overview of the current technologies, challenges, and advantages of Next-Generation Sequencing (NGS) in relation to the field of virology. We present how viral sequences can be detected de novo out of current short-read NGS data. Furthermore, we discuss the challenges and applications of viral quasispecies and how secondary structures, commonly shaped by RNA viruses, can be computationally predicted. The phylogenetic analysis of viruses, as another ubiquitous field in virology, forms an essential element of describing viral epidemics and challenges current algorithms. Recently, the first specialized virus-bioinformatic organizations have been established. We need to bring together virologists and bioinformaticians and provide a platform for the implementation of interdisciplinary collaborative projects at local and international scales. Above all, there is an urgent need for dedicated software tools to tackle various challenges in virology.

Next-Generation Anchor Based Phylogeny (NexABP): Constructing phylogeny from Next-generation sequencing data

Whole genome sequences are ideally suited for deriving evolutionary relationship among organisms. With the availability of Next Generation sequencing (NGS) datasets in an unprecedented scale, it will be highly desirable if phylogenetic analysis can be carried out using short read NGS data. We described here an anchor based approach NexABP for phylogenetic construction of closely related strains/isolates from NGS data. This approach can be used even in the absence of a fully assembled reference genome and works by reducing the complexity of the datasets without compromising results. NexABP was used for constructing phylogeny of different strains of some of the common pathogens, such as Mycobacterium tuberculosis, Vibrio cholera and Escherichia coli. In addition to classification into distinct lineages, NexABP could resolve inner branches and also allow statistical testing using bootstrap analysis. We believe that there are some clear advantages of using NexABP based phylogenetic analysis as compared to other methods.

Characterisation of the wheat (Triticum aestivum L.) transcriptome by de novo assembly for the discovery of phosphate starvation-responsive genes: gene expression in Pi-stressed wheat.

BackgroundPhosphorus (P) is an essential macronutrient for plant growth and development. To modulate their P homeostasis, plants must balance P uptake, mobilisation, and partitioning to various organs. Despite the worldwide importance of wheat as a cultivated food crop, molecular mechanisms associated with phosphate (Pi) starvation in wheat remain unclear. To elucidate these mechanisms, we used RNA-Seq methods to generate transcriptome profiles of the wheat variety ‘Chinese Spring’ responding to 10 days of Pi starvation.ResultsWe carried out de novo assembly on 73.8 million high-quality reads generated from RNA-Seq libraries. We then constructed a transcript dataset containing 29,617 non-redundant wheat transcripts, comprising 15,047 contigs and 14,570 non-redundant full-length cDNAs from the TriFLDB database. When compared with barley full-length cDNAs, 10,656 of the 15,047 contigs were unalignable, suggesting that many might be distinct from barley transcripts. The average expression level of the contigs was lower than that of the known cDNAs, implying that these contigs included transcripts that were rarely represented in the full-length cDNA library. Within the non-redundant transcript set, we identified 892–2,833 responsive transcripts in roots and shoots, corresponding on average to 23.4% of the contigs not covered by cDNAs in TriFLDB under Pi starvation. The relative expression level of the wheat IPS1 (Induced by Phosphate Starvation 1) homologue, TaIPS1, was 341-fold higher in roots and 13-fold higher in shoots; this finding was further confirmed by qRT-PCR analysis. A comparative analysis of the wheat- and rice-responsive transcripts for orthologous genes under Pi-starvation revealed commonly upregulated transcripts, most of which appeared to be involved in a general response to Pi starvation, namely, an IPS1-mediated signalling cascade and its downstream functions such as Pi remobilisation, Pi uptake, and changes in Pi metabolism.ConclusionsOur transcriptome profiles demonstrated the impact of Pi starvation on global gene expression in wheat. This study revealed that enhancement of the Pi-mediated signalling cascade using IPS1 is a potent adaptation mechanism to Pi starvation that is conserved in both wheat and rice and validated the effectiveness of using short-read next-generation sequencing data for wheat transcriptome analysis in the absence of reference genome information.

Next-generation sequencing and large genome assemblies.

The next-generation sequencing (NGS) revolution has drastically reduced time and cost requirements for sequencing of large genomes, and also qualitatively changed the problem of assembly. This article reviews the state of the art in de novo genome assembly, paying particular attention to mammalian-sized genomes. The strengths and weaknesses of the main sequencing platforms are highlighted, leading to a discussion of assembly and the new challenges associated with NGS data. Current approaches to assembly are outlined and the various software packages available are introduced and compared. The question of whether quality assemblies can be produced using short-read NGS data alone, or whether it must be combined with more expensive sequencing techniques, is considered. Prospects for future assemblers and tests of assembly performance are also discussed.

Improved variant discovery through local re-alignment of short-read next-generation sequencing data using SRMA

A primary component of next-generation sequencing analysis is to align short reads to a reference genome, with each read aligned independently. However, reads that observe the same non-reference DNA sequence are highly correlated and can be used to better model the true variation in the target genome. A novel short-read micro re-aligner, SRMA, that leverages this correlation to better resolve a consensus of the underlying DNA sequence of the targeted genome is described here.