Enhancing breakpoint resolution with deep segmentation model: A general refinement method for read-depth based structural variant callers.
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ABSTRACT:
SUBMITTER: Zhang YZ
PROVIDER: S-EPMC8504719 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Long-read RNA sequencing (lrRNA-seq) produces detailed information about full-length transcripts, including novel and sample-specific isoforms. Furthermore, there is an opportunity to call variants directly from lrRNA-seq data. However, most state-of-the-art variant callers have been developed for genomic DNA. Here, there are two objectives: first, we perform a mini-benchmark on GATK, DeepVariant, Clair3, and NanoCaller primarily on PacBio Iso-Seq, data, but also on Nanopore and Illumina RNA-seq data; second, we propose a pipeline to process spliced-alignment files, making them suitable for variant calling with DNA-based callers. With such manipulations, high calling performance can be achieved using DeepVariant on Iso-seq data.
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Project description:Structural variation is an important class of genetic variation in mammals. High-throughput sequencing (HTS) technologies promise to revolutionize copy-number variation (CNV) detection but present substantial analytic challenges. Converging evidence suggests that multiple types of CNV-informative data (e.g. read-depth, read-pair, split-read) need be considered, and that sophisticated methods are needed for more accurate CNV detection. We observed that various sources of experimental biases in HTS confound read-depth estimation, and note that bias correction has not been adequately addressed by existing methods. We present a novel read-depth-based method, GENSENG, which uses a hidden Markov model and negative binomial regression framework to identify regions of discrete copy-number changes while simultaneously accounting for the effects of multiple confounders. Based on extensive calibration using multiple HTS data sets, we conclude that our method outperforms existing read-depth-based CNV detection algorithms. The concept of simultaneous bias correction and CNV detection can serve as a basis for combining read-depth with other types of information such as read-pair or split-read in a single analysis. A user-friendly and computationally efficient implementation of our method is freely available.
| S-EPMC3561969 | biostudies-literature
Project description:BACKGROUND:Since 2009, numerous tools have been developed to detect structural variants using short read technologies. Insertions >50 bp are one of the hardest type to discover and are drastically underrepresented in gold standard variant callsets. The advent of long read technologies has completely changed the situation. In 2019, two independent cross technologies studies have published the most complete variant callsets with sequence resolved insertions in human individuals. Among the reported insertions, only 17 to 28% could be discovered with short-read based tools. RESULTS:In this work, we performed an in-depth analysis of these unprecedented insertion callsets in order to investigate the causes of such failures. We have first established a precise classification of insertion variants according to four layers of characterization: the nature and size of the inserted sequence, the genomic context of the insertion site and the breakpoint junction complexity. Because these levels are intertwined, we then used simulations to characterize the impact of each complexity factor on the recall of several structural variant callers. We showed that most reported insertions exhibited characteristics that may interfere with their discovery: 63% were tandem repeat expansions, 38% contained homology larger than 10 bp within their breakpoint junctions and 70% were located in simple repeats. Consequently, the recall of short-read based variant callers was significantly lower for such insertions (6% for tandem repeats vs 56% for mobile element insertions). Simulations showed that the most impacting factor was the insertion type rather than the genomic context, with various difficulties being handled differently among the tested structural variant callers, and they highlighted the lack of sequence resolution for most insertion calls. CONCLUSIONS:Our results explain the low recall by pointing out several difficulty factors among the observed insertion features and provide avenues for improving SV caller algorithms and their combinations.
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Project description:BACKGROUND:Studies have shown that complex structural variants (cxSVs) contribute to human genomic variation and can cause Mendelian disease. We aimed to identify cxSVs relevant to Mendelian disease using short-read whole-genome sequencing (WGS), resolve the precise variant configuration and investigate possible mechanisms of cxSV formation. METHODS:We performed short-read WGS and analysis of breakpoint junctions to identify cxSVs in a cohort of 1324 undiagnosed rare disease patients. Long-read WGS and gene expression analysis were used to resolve one case. RESULTS:We identified three pathogenic cxSVs: a de novo duplication-inversion-inversion-deletion affecting ARID1B, a de novo deletion-inversion-duplication affecting HNRNPU and a homozygous deletion-inversion-deletion affecting CEP78. Additionally, a de novo duplication-inversion-duplication overlapping CDKL5 was resolved by long-read WGS demonstrating the presence of both a disrupted and an intact copy of CDKL5 on the same allele, and gene expression analysis showed both parental alleles of CDKL5 were expressed. Breakpoint analysis in all the cxSVs revealed both microhomology and longer repetitive elements. CONCLUSIONS:Our results corroborate that cxSVs cause Mendelian disease, and we recommend their consideration during clinical investigations. We show that resolution of breakpoints can be critical to interpret pathogenicity and present evidence of replication-based mechanisms in cxSV formation.
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Project description: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.
| S-EPMC6642177 | biostudies-literature
Project description:Four popular somatic single nucleotide variant (SNV) calling methods (Varscan, SomaticSniper, Strelka and MuTect2) were carefully evaluated on the real whole exome sequencing (WES, depth of ~50X) and ultra-deep targeted sequencing (UDT-Seq, depth of ~370X) data. The four tools returned poor consensus on candidates (only 20% of calls were with multiple hits by the callers). For both WES and UDT-Seq, MuTect2 and Strelka obtained the largest proportion of COSMIC entries as well as the lowest rate of dbSNP presence and high-alternative-alleles-in-control calls, demonstrating their superior sensitivity and accuracy. Combining different callers does increase reliability of candidates, but narrows the list down to very limited range of tumor read depth and variant allele frequency. Calling SNV on UDT-Seq data, which were of much higher read-depth, discovered additional true-positive variations, despite an even more tremendous growth in false positive predictions. Our findings not only provide valuable benchmark for state-of-the-art SNV calling methods, but also shed light on the access to more accurate SNV identification in the future.
| S-EPMC5118795 | biostudies-literature
Project description:Clinically translatable large animal models have become indispensable for cardiovascular research, clinically relevant proof of concept studies and for novel diagnostic and therapeutic interventions. In particular, the pig as emerged as an essential cardiovascular disease model, because its heart, circulatory system, and blood supply are anatomically and functionally similar to that of humans. Unfortunately, molecular and omics-based studies in the pig are hampered by the incompleteness of the genome and the lack of diversity of the corresponding transcriptome annotation. Here, we employed Nanopore long-read sequencing and in-depth proteomics on top of Illumina RNA-seq to enhance the pig cardiac transcriptome annotation. We assembled 15,926 transcripts, stratified into coding and non-coding, and validated our results by complementary mass spectrometry. A manual review of several gene loci, which are associated with cardiac function, corroborated the utility of our enhanced annotation. All our data are available for download and is also provided as tracks for integration in genome browsers. We deem this resource as highly valuable for molecular research in an increasingly relevant large animal model.
2020-10-29 | PXD018985 | Pride
Project description:BackgroundImage segmentation is a difficult and classic problem. It has a wide range of applications, one of which is skin lesion segmentation. Numerous researchers have made great efforts to tackle the problem, yet there is still no universal method in various application domains.ResultsWe propose a novel approach that combines a deep convolutional neural network with a grabcut-like user interaction to tackle the interactive skin lesion segmentation problem. Slightly deviating from grabcut user interaction, our method uses boxes and clicks. In addition, contrary to existing interactive segmentation algorithms that combine the initial segmentation task with the following refinement task, we explicitly separate these tasks by designing individual sub-networks. One network is SBox-Net, and the other is Click-Net. SBox-Net is a full-fledged segmentation network that is built upon a pre-trained, state-of-the-art segmentation model, while Click-Net is a simple yet powerful network that combines feature maps extracted from SBox-Net and user clicks to residually refine the mistakes made by SBox-Net. Extensive experiments on two public datasets, PH2 and ISIC, confirm the effectiveness of our approach.ConclusionsWe present an interactive two-stage pipeline method for skin lesion segmentation, which was demonstrated to be effective in comprehensive experiments.
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Project description:Pericentric inversions are the most common euchromatic chromosomal differences among humans and the great apes. The human and chimpanzee karyotype differs by nine such events, in addition to several constitutive heterochromatic increases and one chromosomal fusion event. Reproductive isolation and subsequent speciation are thought to be the potential result of pericentric inversions, as reproductive boundaries form as a result of hybrid sterility.Here we employed a comparative fluorescence in situ hybridization approach, using probes selected from a combination of physical mapping, genomic sequence, and segmental duplication analyses to narrow the breakpoint interval of a pericentric inversion in chimpanzee involving the orthologous human 15q11-q13 region. We have refined the inversion breakpoint of this chimpanzee-specific rearrangement to a 600 kilobase (kb) interval of the human genome consisting of entirely duplicated material. Detailed analysis of the underlying sequence indicated that this region comprises multiple segmental duplications, including a previously characterized duplication of the alpha7 neuronal nicotinic acetylcholine receptor subunit gene (CHRNA7) in 15q13.3 and several Golgin-linked-to-PML, or LCR15, duplications.We conclude that, on the basis of experimental data excluding the CHRNA7 duplicon as the site of inversion, and sequence analysis of regional duplications, the most likely rearrangement site is within a GLP/LCR15 duplicon. This study further exemplifies the genomic plasticity due to the presence of segmental duplications and highlights their importance for a complete understanding of genome evolution.
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Project description:MOTIVATION:Genetic variant calling with high-throughput sequencing data has been recognized as a useful tool for better understanding of disease mechanism and detection of potential off-target sites in genome editing. Since most of the variant calling algorithms rely on initial mapping onto a reference genome and tend to predict many variant candidates, variant calling remains challenging in terms of predicting variants with low false positives. RESULTS:Here we present Bivartect, a simple yet versatile variant caller based on direct comparison of short sequence reads between normal and mutated samples. Bivartect can detect not only single nucleotide variants but also insertions/deletions, inversions and their complexes. Bivartect achieves high predictive performance with an elaborate memory-saving mechanism, which allows Bivartect to run on a computer with a single node for analyzing small omics data. Tests with simulated benchmark and real genome-editing data indicate that Bivartect was comparable to state-of-the-art variant callers in positive predictive value for detection of single nucleotide variants, even though it yielded a substantially small number of candidates. These results suggest that Bivartect, a reference-free approach, will contribute to the identification of germline mutations as well as off-target sites introduced during genome editing with high accuracy. AVAILABILITY AND IMPLEMENTATION:Bivartect is implemented in C++ and available along with in silico simulated data at https://github.com/ykat0/bivartect. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.
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