Sprites: detection of deletions from sequencing data by re-aligning split reads

Performance evaluation of structural variation detection using DNBSEQ whole-genome sequencing

BACKGROUND

DNBSEQ platforms have been widely used for variation detection, including single-nucleotide variants (SNVs) and short insertions and deletions (INDELs), which is comparable to Illumina. However, the performance and even characteristics of structural variations (SVs) detection using DNBSEQ platforms are still unclear.

RESULTS

In this study, we assessed the detection of SVs using 40 tools on eight DNBSEQ whole-genome sequencing (WGS) datasets and two Illumina WGS datasets of NA12878. Our findings confirmed that the performance of SVs detection using the same tool on DNBSEQ and Illumina datasets was highly consistent, with correlations greater than 0.80 on metrics of number, size, precision and sensitivity, respectively. Furthermore, we constructed a "DNBSEQ" SV set (4,785 SVs) from the DNBSEQ datasets and an "Illumina" SV set (6,797 SVs) from the Illumina datasets. We found that these two SV sets were highly consistent of SV sites and genomic characteristics, including repetitive regions, GC distribution, difficult-to-sequence regions, and gene features, indicating the robustness of our comparative analysis and highlights the value of both platforms in understanding the genomic context of SVs.

CONCLUSIONS

Our study systematically analyzed and characterized germline SVs detected on WGS datasets sequenced from DNBSEQ platforms, providing a benchmark resource for further studies of SVs using DNBSEQ platforms.

Detection of trait-associated structural variations using short-read sequencing

Genomic structural variation (SV) affects genetic and phenotypic characteristics in diverse organisms, but the lack of reliable methods to detect SV has hindered genetic analysis. We developed a computational algorithm (MOPline) that includes missing call recovery combined with high-confidence SV call selection and genotyping using short-read whole-genome sequencing (WGS) data. Using 3,672 high-coverage WGS datasets, MOPline stably detected ∼16,000 SVs per individual, which is over ∼1.7-3.3-fold higher than previous large-scale projects while exhibiting a comparable level of statistical quality metrics. We imputed SVs from 181,622 Japanese individuals for 42 diseases and 60 quantitative traits. A genome-wide association study with the imputed SVs revealed 41 top-ranked or nearly top-ranked genome-wide significant SVs, including 8 exonic SVs with 5 novel associations and enriched mobile element insertions. This study demonstrates that short-read WGS data can be used to identify rare and common SVs associated with a variety of traits.

Deletion Detection Method Using the Distribution of Insert Size and a Precise Alignment Strategy

Homozygous and heterozygous deletions commonly exist in the human genome. For current structural variation detection tools, it is significant to determine whether a deletion is homozygous or heterozygous. However, the problems of sequencing errors, micro-homologies, and micro-insertions prohibit common alignment tools from identifying accurate breakpoint locations, and often result in detecting false structural variations. In this study, we present a novel deletion detection tool called Sprites2. Comparing with Sprites, Sprites2 makes the following modifications: (1) The distribution of insert size is used in Sprites2, which can identify the type of deletions and improve the accuracy of deletion calls. (2) A precise alignment method based on AGE (one algorithm simultaneously aligning 5' and 3' ends between two sequences) is adopted in Sprites2 to identify breakpoints, which is helpful to resolve the problems introduced by sequencing errors, micro-homologies, and micro-insertions. In order to test and verify the performance of Sprites2, some simulated and real datasets are adopted in our experiments, and Sprites2 is compared with five popular tools. The experimental results show that Sprites2 can improve the performance of deletion detection. Sprites2 can be downloaded from https://github.com/zhangzhen/sprites2.

Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software

In recent years, many software packages for identifying structural variants (SVs) using whole-genome sequencing data have been released. When published, a new method is commonly compared with those already available, but this tends to be selective and incomplete. The lack of comprehensive benchmarking of methods presents challenges for users in selecting methods and for developers in understanding algorithm behaviours and limitations. Here we report the comprehensive evaluation of 10 SV callers, selected following a rigorous process and spanning the breadth of detection approaches, using high-quality reference cell lines, as well as simulations. Due to the nature of available truth sets, our focus is on general-purpose rather than somatic callers. We characterise the impact on performance of event size and type, sequencing characteristics, and genomic context, and analyse the efficacy of ensemble calling and calibration of variant quality scores. Finally, we provide recommendations for both users and methods developers.

A number of computational methods have been developed for calling structural variants (SVs) using short read sequencing data. Here, the authors perform a comprehensive benchmarking analysis comparing 10 general-purpose callers and provide recommendations for both users and methods developers.

Removal of a Subset of Non-essential Genes Fully Attenuates a Highly Virulent Mycoplasma Strain

Mycoplasmas are the smallest free-living organisms and cause a number of economically important diseases affecting humans, animals, insects, and plants. Here, we demonstrate that highly virulent Mycoplasma mycoides subspecies capri (Mmc) can be fully attenuated via targeted deletion of non-essential genes encoding, among others, potential virulence traits. Five genomic regions, representing approximately 10% of the original Mmc genome, were successively deleted using Saccharomyces cerevisiae as an engineering platform. Specifically, a total of 68 genes out of the 432 genes verified to be individually non-essential in the JCVI-Syn3.0 minimal cell, were excised from the genome. In vitro characterization showed that this mutant was similar to its parental strain in terms of its doubling time, even though 10% of the genome content were removed. A novel in vivo challenge model in goats revealed that the wild-type parental strain caused marked necrotizing inflammation at the site of inoculation, septicemia and all animals reached endpoint criteria within 6 days after experimental infection. This is in contrast to the mutant strain, which caused no clinical signs nor pathomorphological lesions. These results highlight, for the first time, the rational design, construction and complete attenuation of a Mycoplasma strain via synthetic genomics tools. Trait addition using the yeast-based genome engineering platform and subsequent in vitro or in vivo trials employing the Mycoplasma chassis will allow us to dissect the role of individual candidate Mycoplasma virulence factors and lead the way for the development of an attenuated designer vaccine.

Streptococcus agalactiae Strains with Chromosomal Deletions Evade Detection with Molecular Methods

Surveillance of circulating microbial populations is critical for monitoring the performance of a molecular diagnostic test. In this study, we characterized 31 isolates of Streptococcus agalactiae (group B Streptococcus [GBS]) from several geographic locations in the United States and Ireland that contain deletions in or adjacent to the region of the chromosome that encodes the hemolysin gene cfb, the region targeted by the Xpert GBS and GBS LB assays. PCR-negative, culture-positive isolates were recognized during verification studies of the Xpert GBS assay in 12 laboratories between 2012 and 2018. Whole-genome sequencing of 15 GBS isolates from 11 laboratories revealed four unique deletions of chromosomal DNA ranging from 181 bp to 49 kb. Prospective surveillance studies demonstrated that the prevalence of GBS isolates containing deletions in the convenience sample was <1% in three geographic locations but 7% in a fourth location. Among the 15 isolates with chromosomal deletions, multiple pulsed-field gel electrophoresis types were identified, one of which appears to be broadly dispersed across the United States.

An Exome-seq Based Tool for Mapping and Selection of Candidate Genes in Maize Deletion Mutants

Despite the large number of genomic and transcriptomic resources in maize, there is still much to learn about the function of genes in developmental and biochemical processes. Some maize mutants that were generated by gamma-irradiation showed clear segregation for the kernel phenotypes in B73 × Mo17 F2 ears. To better understand the functional genomics of kernel development, we developed a mapping and gene identification pipeline, bulked segregant exome sequencing (BSEx-seq), to map mutants with kernel phenotypes including opaque endosperm and reduced kernel size. BSEx-seq generates and compares the sequence of the exon fraction from mutant and normal plant F2 DNA pools. The comparison can derive mapping peaks, identify deletions within the mapping peak, and suggest candidate genes within the deleted regions. We then used the public kernel-specific expression data to narrow down the list of candidate genes/mutations and identified deletions ranging from several kb to more than 1 Mb. A full deletion allele of the Opaque-2 gene was identified in mutant 531, which occurs within a ∼200-kb deletion. Opaque mutant 1486 has a 6248-bp deletion in the mapping interval containing two candidate genes encoding RNA-directed DNA methylation 4 (RdDM4) and AMP-binding protein, respectively. This study demonstrates the efficiency and cost-effectiveness of BSEx-seq for causal mutation mapping and candidate gene selection, providing a new option in mapping-by-sequencing for maize functional genomics studies.

A Sequence-Based Novel Approach for Quality Evaluation of Third-Generation Sequencing Reads

The advent of third-generation sequencing (TGS) technologies, such as the Pacific Biosciences (PacBio) and Oxford Nanopore machines, provides new possibilities for contig assembly, scaffolding, and high-performance computing in bioinformatics due to its long reads. However, the high error rate and poor quality of TGS reads provide new challenges for accurate genome assembly and long-read alignment. Efficient processing methods are in need to prioritize high-quality reads for improving the results of error correction and assembly. In this study, we proposed a novel Read Quality Evaluation and Selection Tool (REQUEST) for evaluating the quality of third-generation long reads. REQUEST generates training data of high-quality and low-quality reads which are characterized by their nucleotide combinations. A linear regression model was built to score the quality of reads. The method was tested on three datasets of different species. The results showed that the top-scored reads prioritized by REQUEST achieved higher alignment accuracies. The contig assembly results based on the top-scored reads also outperformed conventional approaches that use all reads. REQUEST is able to distinguish high-quality reads from low-quality ones without using reference genomes, making it a promising alternative sequence-quality evaluation method to alignment-based algorithms.

FNBtools: A Software to Identify Homozygous Lesions in Deletion Mutant Populations

Deletion mutagenesis such as fast neutron bombardment (FNB) has been widely used for forward and reverse genetics studies in functional genomics. Traditionally, the time-consuming map-based cloning is used to locate causal deletions in deletion mutants. In recent years, comparative genomic hybridization (CGH) has been used to speed up and scale up the lesion identification process in deletion mutants. However, limitations of low accuracy and sensitivity for small deletions in the CGH approach are apparent. With the next generation sequencing (NGS) becoming affordable for most users, NGS-based bioinformatics tools are more appealing. Although several deletion callers are available, these tools are not efficient in detecting small deletions. Population-scale deletion callers that can identify both small and large deletions are rare. We were motivated to create a population-scale deletion detection tool, called FNBtools, to identify homozygous causal deletions in mutant populations by using NGS data. FNBtools is a tool to call deletions at a population-scale and to achieve high accuracy at different levels of coverage. In addition, FNBtools can detect both small and large deletions with the ability to identify unique deletions in a mutant pool by filtering deletions that exist in a wild-type or control pool. Furthermore, FNBtools is also able to visualize all identified deletions in a genome-wide scope by using Circos. From simulated data analysis, FNBtools outperforms four existing popular deletion callers in detecting small deletions at different coverage levels. To test the usefulness of FNBtools in real biological applications, we used it to analyze a salt-tolerant mutant in Medicago truncatula and identified the unique deletion locus that is tightly linked with this trait. The causal deletion in the mutant was confirmed by PCR amplification, sequencing and genetic linkage analyses. FNBtools can be used for homozygous deletion identification in any species with reference genome sequences. FNBtools is publicly available at: https://github.com/noble-research-institute/fnbtools.

VAliBS: a visual aligner for bisulfite sequences

Background

Methylation is a common modification of DNA. It has been a very important and hot topic to study the correlation between methylation and diseases in medical science. Because of the special process with bisulfite treatment, traditional mapping tools do not work well with such methylation experimental reads. Traditional aligners are not designed for mapping bisulfite-treated reads, where the un-methylated ‘C’s are converted to ‘T’s.

Results

In this paper, we develop a reliable and visual tool, named VAliBS, for mapping bisulfate sequences to a genome reference. VAliBS works well even on large scale data or high noise data. By comparing with other state-of-the-art tools (BisMark, BSMAP, BS-Seeker2), VAliBS can improve the accuracy of bisulfite mapping. Moreover, VAliBS is a visual tool which makes its operations more easily and the alignment results are shown with colored marks which makes it easier to be read. VAliBS provides fast and accurate mapping of bisulfite-converted reads, and a friendly window system to visualize the detail of mapping of each read.

Conclusions

VAliBS works well on both simulated data and real data. It can be useful in DNA methylation research. VALiBS implements an X-Window user interface where the methylation positions are visual and the operations are friendly.

A Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing Data

Whole Exome Sequencing (WES) is the application of the next-generation technology to determine the variations in the exome and is becoming a standard approach in studying genetic variants in diseases. Understanding the exomes of individuals at single base resolution allows the identification of actionable mutations for disease treatment and management. WES technologies have shifted the bottleneck in experimental data production to computationally intensive informatics-based data analysis. Novel computational tools and methods have been developed to analyze and interpret WES data. Here, we review some of the current tools that are being used to analyze WES data. These tools range from the alignment of raw sequencing reads all the way to linking variants to actionable therapeutics. Strengths and weaknesses of each tool are discussed for the purpose of helping researchers make more informative decisions on selecting the best tools to analyze their WES data.