June 1, 2021  |  

Highly accurate read mapping of third generation sequencing reads for improved structural variation analysis

Characterizing genomic structural variations (SV) is vital for understanding how genomes evolve. Furthermore, SVs are known for playing a role in a wide range of diseases including cancer, autism, and schizophrenia. Nevertheless, due to their complexity they remain harder to detect and less understood than single nucleotide variations. Recently, third-generation sequencing has proven to be an invaluable tool for detecting SVs. The markedly higher read length not only allows single reads to span a SV, it also enables reliable mapping to repetitive regions of the genome. These regions often contain SVs and are inaccessible to short-read mapping. However, current sequencing technologies like PacBio show a raw read error rate of 10% or more consisting mostly of insertions and deletions. Especially in repetitive regions the high error rate causes current mapping methods to fail finding exact borders for SVs, to split up large deletions and insertions into several small ones, or in some cases, like inversions, to fail reporting them at all. Furthermore, for complex SVs it is not possible to find one end-to-end alignment for a given read. The decision of when to split a read into two or more separate alignments without knowledge of the underlying SV poses an even bigger challenge to current read mappers. Here we present NextGenMap-LR for long single molecule PacBio reads which addresses these issues. NextGenMap-LR uses a fast k-mer search to quickly find anchor regions between parts of a read and the reference and evaluates them using a vectorized implementation of the Smith-Waterman (SW) algorithm. The resulting high-quality anchors are then used to determine whether a read spans an SV and has to be split or can be aligned contiguously. Finally, NextGenMap-LR uses a banded SW algorithm to compute the final alignment(s). In this last step, to account for both the sequencing error and real genomic variations, we employ a non-affine gap model that penalizes gap extensions for longer gaps less than for shorter ones. Based on simulated as well as verified human breast cancer SV data we show how our approach significantly improves mapping of long reads around SVs. The non-affine gap model is especially effective at more precisely identifying the position of the breakpoint, and the enhanced scoring scheme enables subsequent variation callers to identify SVs that would have been missed otherwise.


June 1, 2021  |  

From RNA to full-length transcripts: The PacBio Iso-Seq method for transcriptome analysis and genome annotation

A single gene may encode a surprising number of proteins, each with a distinct biological function. This is especially true in complex eukaryotes. Short- read RNA sequencing (RNA-seq) works by physically shearing transcript isoforms into smaller pieces and bioinformatically reassembling them, leaving opportunity for misassembly or incomplete capture of the full diversity of isoforms from genes of interest. The PacBio Isoform Sequencing (Iso-Seq™) method employs long reads to sequence transcript isoforms from the 5’ end to their poly-A tails, eliminating the need for transcript reconstruction and inference. These long reads result in complete, unambiguous information about alternatively spliced exons, transcriptional start sites, and poly- adenylation sites. This allows for the characterization of the full complement of isoforms within targeted genes, or across an entire transcriptome. Here we present improved genome annotations for two avian models of vocal learning, Anna’s hummingbird (Calypte anna) and zebra finch (Taeniopygia guttata), using the Iso-Seq method. We present graphical user interface and command line analysis workflows for the data sets. From brain total RNA, we characterize more than 15,000 isoforms in each species, 9% and 5% of which were previously unannotated in hummingbird and zebra finch, respectively. We highlight one example where capturing full-length transcripts identifies additional exons and UTRs.


June 1, 2021  |  

Full-length transcript profiling with the Iso-Seq method for improved genome annotations

Incomplete annotation of genomes represents a major impediment to understanding biological processes, functional differences between species, and evolutionary mechanisms. Often, genes that are large, embedded within duplicated genomic regions, or associated with repeats are difficult to study by short-read expression profiling and assembly. In addition, most genes in eukaryotic organisms produce alternatively spliced isoforms, broadening the diversity of proteins encoded by the genome, which are difficult to resolve with short-read methods. Short-read RNA sequencing (RNA-seq) works by physically shearing transcript isoforms into smaller pieces and bioinformatically reassembling them, leaving opportunity for misassembly or incomplete capture of the full diversity of isoforms from genes of interest. In contrast, Single Molecule, Real-Time (SMRT) Sequencing directly sequences full-length transcripts without the need for assembly and imputation. Here we apply the Iso-Seq method (long-read RNA sequencing) to detect full-length isoforms and the new IsoPhase algorithm to retrieve allele-specific isoform information for two avian models of vocal learning, Anna’s hummingbird (Calypte anna) and zebra finch (Taeniopygia guttata).


June 1, 2021  |  

Comprehensive variant detection in a human genome with PacBio high-fidelity reads

Human genomic variations range in size from single nucleotide substitutions to large chromosomal rearrangements. Sequencing technologies tend to be optimized for detecting particular variant types and sizes. Short reads excel at detecting SNVs and small indels, while long or linked reads are typically used to detect larger structural variants or phase distant loci. Long reads are more easily mapped to repetitive regions, but tend to have lower per-base accuracy, making it difficult to call short variants. The PacBio Sequel System produces two main data types: long continuous reads (up to 100 kbp), generated by single passes over a long template, and Circular Consensus Sequence (CCS) reads, generated by calculating the consensus of many sequencing passes over a single shorter template (500 bp to 20 kbp). The long-range information in continuous reads is useful for genome assembly and structural variant detection. The higher base accuracy of CCS effectively detects and phases short variants in single molecules. Recent improvements in library preparation protocols and sequencing chemistry have increased the length, accuracy, and throughput of CCS reads. For the human sample HG002, we collected 28-fold coverage 15 kbp high-fidelity CCS reads with an average read quality above Q20 (99% accuracy). The length and accuracy of these reads allow us to detect SNVs, indels, and structural variants not only in the Genome in a Bottle (GIAB) high confidence regions, but also in segmental duplications, HLA loci, and clinically relevant “difficult-to-map” genes. As with continuous long reads, we call structural variants at 90.0% recall compared to the GIAB structural variant benchmark “truth” set, with the added advantages of base pair resolution for variant calls and improved recall at compound heterozygous loci. With minimap2 alignments, GATK4 HaplotypeCaller variant calls, and simple variant filtration, we have achieved a SNP F-Score of 99.51% and an INDEL F-Score of 80.10% against the GIAB short variant benchmark “truth” set, in addition to calling variants outside of the high confidence region established by GIAB using previous technologies. With the long-range information available in 15 kbp reads, we applied the read-backed phasing tool WhatsHap to generate phase blocks with a mean length of 65 kbp across the entire genome. Using an alignment-based approach, we typed all major MHC class I and class II genes to at least 3-field precision. This new data type has the potential to expand the GIAB high confidence regions and “truth” benchmark sets to many previously difficult-to-map genes and allow a single sequencing protocol to address both short variants and large structural variants.


April 21, 2020  |  

Long-read sequencing for rare human genetic diseases.

During the past decade, the search for pathogenic mutations in rare human genetic diseases has involved huge efforts to sequence coding regions, or the entire genome, using massively parallel short-read sequencers. However, the approximate current diagnostic rate is <50% using these approaches, and there remain many rare genetic diseases with unknown cause. There may be many reasons for this, but one plausible explanation is that the responsible mutations are in regions of the genome that are difficult to sequence using conventional technologies (e.g., tandem-repeat expansion or complex chromosomal structural aberrations). Despite the drawbacks of high cost and a shortage of standard analytical methods, several studies have analyzed pathogenic changes in the genome using long-read sequencers. The results of these studies provide hope that further application of long-read sequencers to identify the causative mutations in unsolved genetic diseases may expand our understanding of the human genome and diseases. Such approaches may also be applied to molecular diagnosis and therapeutic strategies for patients with genetic diseases in the future.


April 21, 2020  |  

A robust benchmark for germline structural variant detection

New technologies and analysis methods are enabling genomic structural variants (SVs) to be detected with ever-increasing accuracy, resolution, and comprehensiveness. Translating these methods to routine research and clinical practice requires robust benchmark sets. We developed the first benchmark set for identification of both false negative and false positive germline SVs, which complements recent efforts emphasizing increasingly comprehensive characterization of SVs. To create this benchmark for a broadly consented son in a Personal Genome Project trio with broadly available cells and DNA, the Genome in a Bottle (GIAB) Consortium integrated 19 sequence-resolved variant calling methods, both alignment- and de novo assembly-based, from short-, linked-, and long-read sequencing, as well as optical and electronic mapping. The final benchmark set contains 12745 isolated, sequence-resolved insertion and deletion calls =50 base pairs (bp) discovered by at least 2 technologies or 5 callsets, genotyped as heterozygous or homozygous variants by long reads. The Tier 1 benchmark regions, for which any extra calls are putative false positives, cover 2.66 Gbp and 9641 SVs supported by at least one diploid assembly. Support for SVs was assessed using svviz with short-, linked-, and long-read sequence data. In general, there was strong support from multiple technologies for the benchmark SVs, with 90 % of the Tier 1 SVs having support in reads from more than one technology. The Mendelian genotype error rate was 0.3 %, and genotype concordance with manual curation was >98.7 %. We demonstrate the utility of the benchmark set by showing it reliably identifies both false negatives and false positives in high-quality SV callsets from short-, linked-, and long-read sequencing and optical mapping.


April 21, 2020  |  

Schizophrenia risk variants influence multiple classes of transcripts of sorting nexin 19 (SNX19).

Genome-wide association studies (GWAS) have identified many genomic loci associated with risk for schizophrenia, but unambiguous identification of the relationship between disease-associated variants and specific genes, and in particular their effect on risk conferring transcripts, has proven difficult. To better understand the specific molecular mechanism(s) at the schizophrenia locus in 11q25, we undertook cis expression quantitative trait loci (cis-eQTL) mapping for this 2 megabase genomic region using postmortem human brain samples. To comprehensively assess the effects of genetic risk upon local expression, we evaluated multiple transcript features: genes, exons, and exon-exon junctions in multiple brain regions-dorsolateral prefrontal cortex (DLPFC), hippocampus, and caudate. Genetic risk variants strongly associated with expression of SNX19 transcript features that tag multiple rare classes of SNX19 transcripts, whereas they only weakly affected expression of an exon-exon junction that tags the majority of abundant transcripts. The most prominent class of SNX19 risk-associated transcripts is predicted to be overexpressed, defined by an exon-exon splice junction between exons 8 and 10 (junc8.10) and that is predicted to encode proteins that lack the characteristic nexin C terminal domain. Risk alleles were also associated with either increased or decreased expression of multiple additional classes of transcripts. With RACE, molecular cloning, and long read sequencing, we found a number of novel SNX19 transcripts that further define the set of potential etiological transcripts. We explored epigenetic regulation of SNX19 expression and found that DNA methylation at CpG sites near the primary transcription start site and within exon 2 partially mediate the effects of risk variants on risk-associated expression. ATAC sequencing revealed that some of the most strongly risk-associated SNPs are located within a region of open chromatin, suggesting a nearby regulatory element is involved. These findings indicate a potentially complex molecular etiology, in which risk alleles for schizophrenia generate epigenetic alterations and dysregulation of multiple classes of SNX19 transcripts.


April 21, 2020  |  

Draft Genome Sequence of Mesosutterella multiformis JCM 32464T, a Member of the Family Sutterellaceae, Isolated from Human Feces.

Here, we report the draft genome sequence of Mesosutterella multiformis JCM 32464T, a new member of the family Sutterellaceae that was isolated from human feces. The genome assembly comprised 2,621,983?bp, with a G+C content of 56.9%. This genomic analysis will be useful for understanding the metabolic activities of this asaccharolytic bacterium.Copyright © 2019 Ikeyama et al.


April 21, 2020  |  

Adaptive archaic introgression of copy number variants and the discovery of previously unknown human genes

As they migrated out of Africa and into Europe and Asia, anatomically modern humans interbred with archaic hominins, such as Neanderthals and Denisovans. The result of this genetic introgression on the recipient populations has been of considerable interest, especially in cases of selection for specific archaic genetic variants. Hsieh et al. characterized adaptive structural variants and copy number variants that are likely targets of positive selection in Melanesians. Focusing on population-specific regions of the genome that carry duplicated genes and show an excess of amino acid replacements provides evidence for one of the mechanisms by which genetic novelty can arise and result in differentiation between human genomes.Science, this issue p. eaax2083INTRODUCTIONCharacterizing genetic variants underlying local adaptations in human populations is one of the central goals of evolutionary research. Most studies have focused on adaptive single-nucleotide variants that either arose as new beneficial mutations or were introduced after interbreeding with our now-extinct relatives, including Neanderthals and Denisovans. The adaptive role of copy number variants (CNVs), another well-known form of genomic variation generated through deletions or duplications that affect more base pairs in the genome, is less well understood, despite evidence that such mutations are subject to stronger selective pressures.RATIONALEThis study focuses on the discovery of introgressed and adaptive CNVs that have become enriched in specific human populations. We combine whole-genome CNV calling and population genetic inference methods to discover CNVs and then assess signals of selection after controlling for demographic history. We examine 266 publicly available modern human genomes from the Simons Genome Diversity Project and genomes of three ancient homininstextemdasha Denisovan, a Neanderthal from the Altai Mountains in Siberia, and a Neanderthal from Croatia. We apply long-read sequencing methods to sequence-resolve complex CNVs of interest specifically in the Melanesianstextemdashan Oceanian population distributed from Papua New Guinea to as far east as the islands of Fiji and known to harbor some of the greatest amounts of Neanderthal and Denisovan ancestry.RESULTSConsistent with the hypothesis of archaic introgression outside Africa, we find a significant excess of CNV sharing between modern non-African populations and archaic hominins (P = 0.039). Among Melanesians, we observe an enrichment of CNVs with potential signals of positive selection (n = 37 CNVs), of which 19 CNVs likely introgressed from archaic hominins. We show that Melanesian-stratified CNVs are significantly associated with signals of positive selection (P = 0.0323). Many map near or within genes associated with metabolism (e.g., ACOT1 and ACOT2), development and cell cycle or signaling (e.g., TNFRSF10D and CDK11A and CDK11B), or immune response (e.g., IFNLR1). We characterize two of the largest and most complex CNVs on chromosomes 16p11.2 and 8p21.3 that introgressed from Denisovans and Neanderthals, respectively, and are absent from most other human populations. At chromosome 16p11.2, we sequence-resolve a large duplication of >383 thousand base pairs (kbp) that originated from Denisovans and introgressed into the ancestral Melanesian population 60,000 to 170,000 years ago. This large duplication occurs at high frequency (>79%) in diverse Melanesian groups, shows signatures of positive selection, and maps adjacent to Homo sapienstextendashspecific duplications that predispose to rearrangements associated with autism. On chromosome 8p21.3, we identify a Melanesian haplotype that carries two CNVs, a ~6-kbp deletion, and a ~38-kbp duplication, with a Neanderthal origin and that introgressed into non-Africans 40,000 to 120,000 years ago. This CNV haplotype occurs at high frequency (44%) and shows signals consistent with a partial selective sweep in Melanesians. Using long-read sequencing genomic and transcriptomic data, we reconstruct the structure and complex evolutionary history for these two CNVs and discover previously undescribed duplicated genes (TNFRSF10D1, TNFRSF10D2, and NPIPB16) that show an excess of amino acid replacements consistent with the action of positive selection.CONCLUSIONOur results suggest that large CNVs originating in archaic hominins and introgressed into modern humans have played an important role in local population adaptation and represent an insufficiently studied source of large-scale genetic variation that is absent from current reference genomes.Large adaptive-introgressed CNVs at chromosomes 8p21.3 and 16p11.2 in Melanesians.The magnifying glasses highlight structural differences between the archaic (top) and reference (bottom) genomes. Neanderthal (red) and Denisovan (blue) haplotypes encompassing large CNVs occur at high frequencies in Melanesians (44 and 79%, respectively) but are absent (black) in all non-Melanesians. These CNVs create positively selected genes (TNFRSF10D1, TNFRSF10D2, and NPIPB16) that are absent from the reference genome.Copy number variants (CNVs) are subject to stronger selective pressure than single-nucleotide variants, but their roles in archaic introgression and adaptation have not been systematically investigated. We show that stratified CNVs are significantly associated with signatures of positive selection in Melanesians and provide evidence for adaptive introgression of large CNVs at chromosomes 16p11.2 and 8p21.3 from Denisovans and Neanderthals, respectively. Using long-read sequence data, we reconstruct the structure and complex evolutionary history of these polymorphisms and show that both encode positively selected genes absent from most human populations. Our results collectively suggest that large CNVs originating in archaic hominins and introgressed into modern humans have played an important role in local population adaptation and represent an insufficiently studied source of large-scale genetic variation.


April 21, 2020  |  

Characterizing the major structural variant alleles of the human genome.

In order to provide a comprehensive resource for human structural variants (SVs), we generated long-read sequence data and analyzed SVs for fifteen human genomes. We sequence resolved 99,604 insertions, deletions, and inversions including 2,238 (1.6 Mbp) that are shared among all discovery genomes with an additional 13,053 (6.9 Mbp) present in the majority, indicating minor alleles or errors in the reference. Genotyping in 440 additional genomes confirms the most common SVs in unique euchromatin are now sequence resolved. We report a ninefold SV bias toward the last 5 Mbp of human chromosomes with nearly 55% of all VNTRs (variable number of tandem repeats) mapping to this portion of the genome. We identify SVs affecting coding and noncoding regulatory loci improving annotation and interpretation of functional variation. These data provide the framework to construct a canonical human reference and a resource for developing advanced representations capable of capturing allelic diversity. Copyright © 2018 Elsevier Inc. All rights reserved.


April 21, 2020  |  

Genetic Variation, Comparative Genomics, and the Diagnosis of Disease.

The discovery of mutations associated with human genetic dis- ease is an exercise in comparative genomics (see Glossary). Although there are many different strategies and approaches, the central premise is that affected persons harbor a significant excess of pathogenic DNA variants as com- pared with a group of unaffected persons (controls) that is either clinically defined1 or established by surveying large swaths of the general population.2 The more exclu- sive the variant is to the disease, the greater its penetrance, the larger its effect size, and the more relevant it becomes to both disease diagnosis and future therapeutic investigation. The most popular approach used by researchers in human genetics is the case–control design, but there are others that can be used to track variants and disease in a family context or that consider the probability of different classes of mutations based on evolutionary patterns of divergence or de novo mutational change.3,4 Although the approaches may be straightforward, the discovery of patho- genic variation and its mechanism of action often is less trivial, and decades of research can be required in order to identify the variants underlying both mendelian and complex genetic traits.


April 21, 2020  |  

TSD: A Computational Tool To Study the Complex Structural Variants Using PacBio Targeted Sequencing Data.

PacBio sequencing is a powerful approach to study DNA or RNA sequences in a longer scope. It is especially useful in exploring the complex structural variants generated by random integration or multiple rearrangement of endogenous or exogenous sequences. Here, we present a tool, TSD, for complex structural variant discovery using PacBio targeted sequencing data. It allows researchers to identify and visualize the genomic structures of targeted sequences by unlimited splitting, alignment and assembly of long PacBio reads. Application to the sequencing data derived from an HBV integrated human cell line(PLC/PRF/5) indicated that TSD could recover the full profile of HBV integration events, especially for the regions with the complex human-HBV genome integrations and multiple HBV rearrangements. Compared to other long read analysis tools, TSD showed a better performance for detecting complex genomic structural variants. TSD is publicly available at: https://github.com/menggf/tsd. Copyright © 2019 Meng et al.


April 21, 2020  |  

The role of genomic structural variation in the genetic improvement of polyploid crops

Many of our major crop species are polyploids, containing more than one genome or set of chromosomes. Polyploid crops present unique challenges, including difficulties in genome assembly, in discriminating between multiple gene and sequence copies, and in genetic mapping, hindering use of genomic data for genetics and breeding. Polyploid genomes may also be more prone to containing structural variation, such as loss of gene copies or sequences (presence–absence variation) and the presence of genes or sequences in multiple copies (copy-number variation). Although the two main types of genomic structural variation commonly identified are presence–absence variation and copy-number variation, we propose that homeologous exchanges constitute a third major form of genomic structural variation in polyploids. Homeologous exchanges involve the replacement of one genomic segment by a similar copy from another genome or ancestrally duplicated region, and are known to be extremely common in polyploids. Detecting all kinds of genomic structural variation is challenging, but recent advances such as optical mapping and long-read sequencing offer potential strategies to help identify structural variants even in complex polyploid genomes. All three major types of genomic structural variation (presence–absence, copy-number, and homeologous exchange) are now known to influence phenotypes in crop plants, with examples of flowering time, frost tolerance, and adaptive and agronomic traits. In this review, we summarize the challenges of genome analysis in polyploid crops, describe the various types of genomic structural variation and the genomics technologies and data that can be used to detect them, and collate information produced to date related to the impact of genomic structural variation on crop phenotypes. We highlight the importance of genomic structural variation for the future genetic improvement of polyploid crops.


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