Title: HQAlign: aligning nanopore reads for SV detection using current-level modeling
Abstract MotivationDetection of structural variants (SVs) from the alignment of sample DNA reads to the reference genome is an important problem in understanding human diseases. Long reads that can span repeat regions, along with an accurate alignment of these long reads play an important role in identifying novel SVs. Long-read sequencers, such as nanopore sequencing, can address this problem by providing very long reads but with high error rates, making accurate alignment challenging. Many errors induced by nanopore sequencing have a bias because of the physics of the sequencing process and proper utilization of these error characteristics can play an important role in designing a robust aligner for SV detection problems. In this article, we design and evaluate HQAlign, an aligner for SV detection using nanopore sequenced reads. The key ideas of HQAlign include (i) using base-called nanopore reads along with the nanopore physics to improve alignments for SVs, (ii) incorporating SV-specific changes to the alignment pipeline, and (iii) adapting these into existing state-of-the-art long-read aligner pipeline, minimap2 (v2.24), for efficient alignments. ResultsWe show that HQAlign captures about 4%–6% complementary SVs across different datasets, which are missed by minimap2 alignments while having a standalone performance at par with minimap2 for real nanopore reads data. For the common SV calls between HQAlign and minimap2, HQAlign improves the start and the end breakpoint accuracy by about 10%–50% for SVs across different datasets. Moreover, HQAlign improves the alignment rate to 89.35% from minimap2 85.64% for nanopore reads alignment to recent telomere-to-telomere CHM13 assembly, and it improves to 86.65% from 83.48% for nanopore reads alignment to GRCh37 human genome. Availability and implementationhttps://github.com/joshidhaivat/HQAlign.git. more »« less
Efficient and accurate alignment of DNA/RNA sequence reads to each other or to a reference genome/transcriptome is an important problem in genomic analysis. Nanopore sequencing has emerged as a major sequencing technology and many long-read aligners have been designed for aligning nanopore reads. However, the high error rate makes accurate and efficient alignment difficult. Utilizing the noise and error characteristics inherent in the sequencing process properly can play a vital role in constructing a robust aligner. In this article, we design QAlign, a pre-processor that can be used with any long-read aligner for aligning long reads to a genome/transcriptome or to other long reads. The key idea in QAlign is to convert the nucleotide reads into discretized current levels that capture the error modes of the nanopore sequencer before running it through a sequence aligner.We show that QAlign is able to improve alignment rates from around 80\% up to 90\% with nanopore reads when aligning to the genome. We also show that QAlign improves the average overlap quality by 9.2, 2.5 and 10.8\% in three real datasets for read-to-read alignment. Read-to-transcriptome alignment rates are improved from 51.6\% to 75.4\% and 82.6\% to 90\% in two real datasets.https://github.com/joshidhaivat/QAlign.git.Supplementary data are available at Bioinformatics online.
Linderman, Michael D; Paudyal, Crystal; Shakeel, Musab; Kelley, William; Bashir, Ali; Gelb, Bruce D
(, GigaScience)
Abstract Background Structural variants (SVs) play a causal role in numerous diseases but are difficult to detect and accurately genotype (determine zygosity) in whole-genome next-generation sequencing data. SV genotypers that assume that the aligned sequencing data uniformly reflect the underlying SV or use existing SV call sets as training data can only partially account for variant and sample-specific biases. Results We introduce NPSV, a machine learning–based approach for genotyping previously discovered SVs that uses next-generation sequencing simulation to model the combined effects of the genomic region, sequencer, and alignment pipeline on the observed SV evidence. We evaluate NPSV alongside existing SV genotypers on multiple benchmark call sets. We show that NPSV consistently achieves or exceeds state-of-the-art genotyping accuracy across SV call sets, samples, and variant types. NPSV can specifically identify putative de novo SVs in a trio context and is robust to offset SV breakpoints. Conclusions Growing SV databases and the increasing availability of SV calls from long-read sequencing make stand-alone genotyping of previously identified SVs an increasingly important component of genome analyses. By treating potential biases as a “black box” that can be simulated, NPSV provides a framework for accurately genotyping a broad range of SVs in both targeted and genome-scale applications.
Structural variants (SVs) account for a large amount of sequence variability across genomes and play an important role in human genomics and precision medicine. Despite intense efforts over the years, the discovery of SVs in individuals remains challenging due to the diploid and highly repetitive structure of the human genome, and by the presence of SVs that vastly exceed sequencing read lengths. However, the recent introduction of low-error long-read sequencing technologies such as PacBio HiFi may finally enable these barriers to be overcome. Here we present SV discovery with sample-specific strings (SVDSS)—a method for discovery of SVs from long-read sequencing technologies (for example, PacBio HiFi) that combines and effectively leverages mapping-free, mapping-based and assembly-based methodologies for overall superior SV discovery performance. Our experiments on several human samples show that SVDSS outperforms state-of-the-art mapping-based methods for discovery of insertion and deletion SVs in PacBio HiFi reads and achieves notable improvements in calling SVs in repetitive regions of the genome.
Abstract: Structural variants (SVs) can promote speciation by directly causing reproductive isolation or by suppressing recombination across large genomic regions. Whereas examples of each mechanism have been documented, systematic tests of the role of SVs in speciation are lacking. Here, we take advantage of long-read (Oxford nanopore) whole-genome sequencing and a hybrid zone between two Lycaeides butterfly taxa (L. melissa and Jackson Hole Lycaeides) to comprehensively evaluate genome-wide patterns of introgression for SVs and relate these patterns to hypotheses about speciation. We found 100,000 SVs segregating within or between the two hybridizing species. SVs and SNPs exhibited similar levels of genetic differentiation between species, with the exception of inversions, which were more differentiated. We detected credible variation in patterns of introgression among SV loci in the hybrid zone, with 562 of 1419 ancestry-informative SVs exhibiting genomic clines that deviated from null expectations based on genome-average ancestry. Overall, hybrids exhibited a directional shift towards Jackson Hole Lycaeides ancestry at SV loci, consistent with the hypothesis that these loci experienced more selection on average than SNP loci. Surprisingly, we found that deletions, rather than inversions, showed the highest skew towards excess ancestry from Jackson Hole Lycaeides. Excess Jackson Hole Lycaeides ancestry in hybrids was also especially pronounced for Z-linked SVs and inversions containing many genes. In conclusion, our results show that SVs are ubiquitous and suggest that SVs in general, but especially deletions, might disproportionately affect hybrid fitness and thus contribute to reproductive isolation. Methods: This data set includes a new, whole genome reference sequence for Lycaeides melissa. We used PacBio SMRT sequencing to improve our existing L. melissa reference genome, which was assembled from Chicago and Hi-C genomic libraries and comprised 24 large scaffolds corresponding to the 23 L. melissa linkage groups (22 autosomes and the Z sex chromosome, with a all but one chromosome represented by a single scaffold). High molecular weight DNA was extracted from a single female L. melissa. Following quality control, a HiFi library was constructed from purified DNA and sequenced using a PacBio Sequell 2 with a 30-hour HiFi SMRT cell. This generated 978,737 reads with a mean length of 18,683 bps. The BYU DNA Sequencing Center bioinformatics team then used these data to improve (extend and replace Ns with known bases) our existing genome. Gaps were filled in the assembly from Dovetail Genomics using PacBio HiFi reads using TGS-GapCloser v1.0.1. Given that the reads were Q20 or higher, the skip error correction option was used. Other than required parameters (e.g., to provide the input reads and assembly), the only other non-default parameter was ‑‑tgstype to specify the read type. Our input reference genome comprised 362 Mbps with 56% Ns, whereas after the gap-filling process our new reference genome spanned 521 Mbps with only 6.8% Ns.The data set also includes a vcf file with structural variant calls for Lycaeides. These variants were identified from Oxford nanopore DNA sequence data generated with a MinION. Genomic libraries for 37 butterflies were loaded onto R9.4 flow cells (FLO-Min106, ONT) for sequencing for full 72 hour runs on a MinION. This generated an average of 7 GB of sequence data per library, with a N50 read length of 1114 to 20,944 bps (mean read length 9073 bp; maximum read length of 342,840 bp). Base calling was performed using the ont-guppy algorithm from guppy_basecaller (version 4.2.2). We then used guppy_barcoder (version 4.2.2) to demultiplex the nanopore sequence reads and subsequently remove the barcode sequences. We used Minimap2 (version 2.17) to align the nanopore reads to the reference genome, and we used Sniffles (version 1.0.12) and SURVIVOR (version 1.03) for structural variant calling. We then discarded all structural variants less than 1000 bp in length. Other: Lmel_dovetailPacBio_genome.fasta genome assembly for Lycaeides melissa in fasta format. merged_SURVIVOR_1kbpdist_typesave_new.vcf structural variant file in vcf format.
Lötter, Anneri; Duong, Tuan A; Candotti, Julia; Mizrachi, Eshchar; Wegrzyn, Jill L; Myburg, Alexander A
(, GigaScience)
Abstract BackgroundDe novo phased (haplo)genome assembly using long-read DNA sequencing data has improved the detection and characterization of structural variants (SVs) in plant and animal genomes. Able to span across haplotypes, long reads allow phased, haplogenome assembly in highly outbred organisms such as forest trees. Eucalyptus tree species and interspecific hybrids are the most widely planted hardwood trees with F1 hybrids of Eucalyptus grandis and E. urophylla forming the bulk of fast-growing pulpwood plantations in subtropical regions. The extent of structural variation and its effect on interspecific hybridization is unknown in these trees. As a first step towards elucidating the extent of structural variation between the genomes of E. grandis and E. urophylla, we sequenced and assembled the haplogenomes contained in an F1 hybrid of the two species. FindingsUsing Nanopore sequencing and a trio-binning approach, we assembled the separate haplogenomes (566.7 Mb and 544.5 Mb) to 98.0% BUSCO completion. High-density SNP genetic linkage maps of both parents allowed scaffolding of 88.0% of the haplogenome contigs into 11 pseudo-chromosomes (scaffold N50 of 43.8 Mb and 42.5 Mb for the E. grandis and E. urophylla haplogenomes, respectively). We identify 48,729 SVs between the two haplogenomes providing the first detailed insight into genome structural rearrangement in these species. The two haplogenomes have similar gene content, 35,572 and 33,915 functionally annotated genes, of which 34.7% are contained in genome rearrangements. ConclusionsKnowledge of SV and haplotype diversity in the two species will form the basis for understanding the genetic basis of hybrid superiority in these trees.
Joshi, Dhaivat, Diggavi, Suhas, Chaisson, Mark J, and Kannan, Sreeram.
"HQAlign: aligning nanopore reads for SV detection using current-level modeling". Bioinformatics 39 (10). Country unknown/Code not available: Bioinformatics, Oxford University Press. https://doi.org/10.1093/bioinformatics/btad580.https://par.nsf.gov/biblio/10474610.
@article{osti_10474610,
place = {Country unknown/Code not available},
title = {HQAlign: aligning nanopore reads for SV detection using current-level modeling},
url = {https://par.nsf.gov/biblio/10474610},
DOI = {10.1093/bioinformatics/btad580},
abstractNote = {Abstract MotivationDetection of structural variants (SVs) from the alignment of sample DNA reads to the reference genome is an important problem in understanding human diseases. Long reads that can span repeat regions, along with an accurate alignment of these long reads play an important role in identifying novel SVs. Long-read sequencers, such as nanopore sequencing, can address this problem by providing very long reads but with high error rates, making accurate alignment challenging. Many errors induced by nanopore sequencing have a bias because of the physics of the sequencing process and proper utilization of these error characteristics can play an important role in designing a robust aligner for SV detection problems. In this article, we design and evaluate HQAlign, an aligner for SV detection using nanopore sequenced reads. The key ideas of HQAlign include (i) using base-called nanopore reads along with the nanopore physics to improve alignments for SVs, (ii) incorporating SV-specific changes to the alignment pipeline, and (iii) adapting these into existing state-of-the-art long-read aligner pipeline, minimap2 (v2.24), for efficient alignments. ResultsWe show that HQAlign captures about 4%–6% complementary SVs across different datasets, which are missed by minimap2 alignments while having a standalone performance at par with minimap2 for real nanopore reads data. For the common SV calls between HQAlign and minimap2, HQAlign improves the start and the end breakpoint accuracy by about 10%–50% for SVs across different datasets. Moreover, HQAlign improves the alignment rate to 89.35% from minimap2 85.64% for nanopore reads alignment to recent telomere-to-telomere CHM13 assembly, and it improves to 86.65% from 83.48% for nanopore reads alignment to GRCh37 human genome. Availability and implementationhttps://github.com/joshidhaivat/HQAlign.git.},
journal = {Bioinformatics},
volume = {39},
number = {10},
publisher = {Bioinformatics, Oxford University Press},
author = {Joshi, Dhaivat and Diggavi, Suhas and Chaisson, Mark J and Kannan, Sreeram},
editor = {Alkan, Can}
}
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