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Expectations and blind spots for structural variation detection from long-read assemblies and short-read genome sequencing technologies.

ABSTRACT: Virtually all genome sequencing efforts in national biobanks, complex and Mendelian disease programs, and medical genetic initiatives are reliant upon short-read whole-genome sequencing (srWGS), which presents challenges for the detection of structural variants (SVs) relative to emerging long-read WGS (lrWGS) technologies. Given this ubiquity of srWGS in large-scale genomics initiatives, we sought to establish expectations for routine SV detection from this data type by comparison with lrWGS assembly, as well as to quantify the genomic properties and added value of SVs uniquely accessible to each technology. Analyses from the Human Genome Structural Variation Consortium (HGSVC) of three families captured ~11,000 SVs per genome from srWGS and ~25,000 SVs per genome from lrWGS assembly. Detection power and precision for SV discovery varied dramatically by genomic context and variant class: 9.7% of the current GRCh38 reference is defined by segmental duplication (SD) and simple repeat (SR), yet 91.4% of deletions that were specifically discovered by lrWGS localized to these regions. Across the remaining 90.3% of reference sequence, we observed extremely high (93.8%) concordance between technologies for deletions in these datasets. In contrast, lrWGS was superior for detection of insertions across all genomic contexts. Given that non-SD/SR sequences encompass 95.9% of currently annotated disease-associated exons, improved sensitivity from lrWGS to discover novel pathogenic deletions in these currently interpretable genomic regions is likely to be incremental. However, these analyses highlight the considerable added value of assembly-based lrWGS to create new catalogs of insertions and transposable elements, as well as disease-associated repeat expansions in genomic sequences that were previously recalcitrant to routine assessment.

SUBMITTER: Zhao X

PROVIDER: S-EPMC8206509 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundStructural variations play an important role in bacterial genomes. They can mediate genome adaptation quickly in response to the external environment and thus can also play a role in antibiotic resistance. The detection of structural variations in bacteria is challenging, and the recognition of even small rearrangements can be important. Even though most detection tools are aimed at and benchmarked on eukaryotic genomes, they can also be used on prokaryotic genomes. The key features of detection are the ability to detect small rearrangements and support haploid genomes. Because of the limiting performance of a single detection tool, combining the detection abilities of multiple tools can lead to more robust results. There are already available workflows for structural variation detection for long-reads technologies and for the detection of single-nucleotide variation and indels, both aimed at bacteria. Yet we are unaware of structural variations detection workflows for the short-reads sequencing platform. Motivated by this gap we created our workflow. Further, we were interested in increasing the detection performance and providing more robust results.ResultsWe developed an open-source bioinformatics pipeline, ProcaryaSV, for the detection of structural variations in bacterial isolates from paired-end short sequencing reads. Multiple tools, starting with quality control and trimming of sequencing data, alignment to the reference genome, and multiple structural variation detection tools, are integrated. All the partial results are then processed and merged with an in-house merging algorithm. Compared with a single detection approach, ProcaryaSV has improved detection performance and is a reproducible easy-to-use tool.ConclusionsThe ProcaryaSV pipeline provides an integrative approach to structural variation detection from paired-end next-generation sequencing of bacterial samples. It can be easily installed and used on Linux machines. It is publicly available on GitHub at https://github.com/robinjugas/ProcaryaSV .

Project description:BackgroundMutations that alter chromosomal structure play critical roles in evolution and disease, including in the origin of new lifestyles and pathogenic traits in microbes. Large-scale rearrangements in genomes are often mediated by recombination events involving new or existing copies of mobile genetic elements, recently duplicated genes, or other repetitive sequences. Most current software programs for predicting structural variation from short-read DNA resequencing data are intended primarily for use on human genomes. They typically disregard information in reads mapping to repeat sequences, and significant post-processing and manual examination of their output is often required to rule out false-positive predictions and precisely describe mutational events.ResultsWe have implemented an algorithm for identifying structural variation from DNA resequencing data as part of the breseq computational pipeline for predicting mutations in haploid microbial genomes. Our method evaluates the support for new sequence junctions present in a clonal sample from split-read alignments to a reference genome, including matches to repeat sequences. Then, it uses a statistical model of read coverage evenness to accept or reject these predictions. Finally, breseq combines predictions of new junctions and deleted chromosomal regions to output biologically relevant descriptions of mutations and their effects on genes. We demonstrate the performance of breseq on simulated Escherichia coli genomes with deletions generating unique breakpoint sequences, new insertions of mobile genetic elements, and deletions mediated by mobile elements. Then, we reanalyze data from an E. coli K-12 mutation accumulation evolution experiment in which structural variation was not previously identified. Transposon insertions and large-scale chromosomal changes detected by breseq account for ~25% of spontaneous mutations in this strain. In all cases, we find that breseq is able to reliably predict structural variation with modest read-depth coverage of the reference genome (>40-fold).ConclusionsUsing breseq to predict structural variation should be useful for studies of microbial epidemiology, experimental evolution, synthetic biology, and genetics when a reference genome for a closely related strain is available. In these cases, breseq can discover mutations that may be responsible for important or unintended changes in genomes that might otherwise go undetected.

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Expectations and blind spots for structural variation detection from long-read assemblies and short-read genome sequencing technologies.

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