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There is growing interest in the role of structural variants (SVs) as drivers of local adaptation and speciation. From a biodiversity genomics perspective, the characterization of genome-wide SVs provides an exciting opportunity to complement single nucleotide polymorphisms (SNPs). However, little is known about the impacts of SV discovery and genotyping strategies on the characterization of genome-wide SV diversity within and among populations. Here, we explore a near whole-species resequence data set, and long-read sequence data for a subset of highly represented individuals in the critically endangered kākāpō (Strigops habroptilus). We demonstrate that even when using a highly contiguous reference genome, different discovery and genotyping strategies can significantly impact the type, size and location of SVs characterized genome-wide. Further, we found that the mean number of SVs in each of two kākāpō lineages differed both within and across generations. These combined results suggest that genome-wide characterization of SVs remains challenging at the population-scale. We are optimistic that increased accessibility to long-read sequencing and advancements in bioinformatic approaches including multireference approaches like genome graphs will alleviate at least some of the challenges associated with resolving SV characteristics below the species level. In the meantime, we address caveats, highlight considerations, and provide recommendations for the characterization of genome-wide SVs in biodiversity genomic research.
Pubmed ID: 36916824
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Software package as probabilistic framework for structural variant discovery. Capable of integrating any number of SV detection signals including those generated from read alignments or prior evidence. Simplified wrapper for standard analyses, LUMPY Express, can also be executed.
View all literature mentionsIntegrated structural variant prediction software that can detect deletions, tandem duplications, inversions and translocations at single-nucleotide resolution in short-read massively parallel sequencing data. It uses paired-ends and split-reads to sensitively and accurately delineate genomic rearrangements throughout genome.
View all literature mentionsProvide various utilities for manipulating alignments in the SAM format, including sorting, merging, indexing and generating alignments in a per-position format.
View all literature mentionsSoftware program to remove residual adapter sequences from next generation sequencing reads. Used for cleaning of next-generation sequencing reads. AdapterRemoval v2 introduces improvements in throughput, through use of single instruction, multiple data (SIMD; SSE1 and SSE2) instructions and multi-threading support; handles datasets containing reads or read-pairs with different adapters or adapter pairs; provides simultaneous demultiplexing and adapter trimming; has ability to reconstruct adapter sequences from paired-end reads for poorly documented data sets; provides native gzip and bzip2 support.
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View all literature mentionsData aggregate that compiles results from bioinformatics analyses across multiple samples into a single report. It is written in Python.
View all literature mentionsSoftware for scoring protein sequence conservation using the Jensen-Shannon divergence. It can be used to predict catalytic sites and residues near bound ligands.
View all literature mentionsSoftware tool written in Python to perform its filtering based on mean read quality and GC content and read length. Used for filtering and trimming of long read sequencing data.
View all literature mentionsSoftware tool for finding and removing adapters from Oxford Nanopore reads.
View all literature mentionsSoftware tool for rapid detection of structural variants and indels for germline and cancer sequencing applications. Used to call structural variants and indels from mapped paired end sequencing reads.
View all literature mentionsSoftware package as plotting tool for long read sequencing data and alignments.
View all literature mentionsSoftware tool for long read based human genomic structural variation detection. Collects signatures of various types of SVs and employs clustering-and-refinement method to analyze signatures to implement sensitive SV detection.cuteSV2 is upgraded version of cuteSV.
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