VISTA: an integrated framework for structural variant discovery.

Structural variation (SV) refers to insertions, deletions, inversions, and duplications in human genomes. SVs are present in approximately 1.5% of the human genome. Still, this small subset of genetic variation has been implicated in the pathogenesis of psoriasis, Crohn's disease and other autoimmune disorders, autism spectrum and other neurodevelopmental disorders, and schizophrenia. Since identifying structural variants is an important problem in genetics, several specialized computational techniques have been developed to detect structural variants directly from sequencing data. With advances in whole-genome sequencing (WGS) technologies, a plethora of SV detection methods have been developed. However, dissecting SVs from WGS data remains a challenge, with the majority of SV detection methods prone to a high false-positive rate, and no existing method able to precisely detect a full range of SVs present in a sample. Previous studies have shown that none of the existing SV callers can maintain high accuracy across various SV lengths and genomic coverages. Here, we report an integrated structural variant calling framework, Variant Identification and Structural Variant Analysis (VISTA), that leverages the results of individual callers using a novel and robust filtering and merging algorithm. In contrast to existing consensus-based tools which ignore the length and coverage, VISTA overcomes this limitation by executing various combinations of top-performing callers based on variant length and genomic coverage to generate SV events with high accuracy. We evaluated the performance of VISTA on comprehensive gold-standard datasets across varying organisms and coverage. We benchmarked VISTA using the Genome-in-a-Bottle gold standard SV set, haplotype-resolved de novo assemblies from the Human Pangenome Reference Consortium, along with an in-house polymerase chain reaction (PCR)-validated mouse gold standard set. VISTA maintained the highest F1 score among top consensus-based tools measured using a comprehensive gold standard across both mouse and human genomes. VISTA also has an optimized mode, where the calls can be optimized for precision or recall. VISTA-optimized can attain 100% precision and the highest sensitivity among other variant callers. In conclusion, VISTA represents a significant advancement in structural variant calling, offering a robust and accurate framework that outperforms existing consensus-based tools and sets a new standard for SV detection in genomic research.

Genotyping sequence-resolved copy-number variation using pangenomes reveals paralog-specific global diversity and expression divergence of duplicated genes.

Human pangenomes contain assemblies of non-reference copy-number variable (CNV) genes. We developed a new method, ctyper, to identify the copy-number of specific alleles of CNV genes cataloged in pangenomes with NGS datasets. Applying ctyper to the 1000-genomes samples revealed population stratification of paralogs and two classes of CNVs: recent CNVs due to ongoing duplications, and polymorphic CNVs from non-reference ancient paralogs. Expression quantitative trait locus analysis determined allele-specific expression within gene families, revealing that 7.94% of paralogs and 3.28% orthologs had significantly divergent expression. Case studies of individual genes include finding lower expression on SMN-1 copies that arose from conversion from SMN-2, and increased expression on a form of AMY2B that has undergone a translocation. Moreover, 4.7% of paralogs and 1.2% of orthologs had different most-expressed tissues. Furthermore, the genotypes explain more expression variance than known eQTL variants. Overall, ctyper enables biobank-scale genotyping of sequence-resolved CNVs.

Analysis and benchmarking of small and large genomic variants across tandem repeats.

Tandem repeats (TRs) are highly polymorphic in the human genome, have thousands of associated molecular traits and are linked to over 60 disease phenotypes. However, they are often excluded from at-scale studies because of challenges with variant calling and representation, as well as a lack of a genome-wide standard. Here, to promote the development of TR methods, we created a catalog of TR regions and explored TR properties across 86 haplotype-resolved long-read human assemblies. We curated variants from the Genome in a Bottle (GIAB) HG002 individual to create a TR dataset to benchmark existing and future TR analysis methods. We also present an improved variant comparison method that handles variants greater than 4 bp in length and varying allelic representation. The 8.1% of the genome covered by the TR catalog holds ~24.9% of variants per individual, including 124,728 small and 17,988 large variants for the GIAB HG002 'truth-set' TR benchmark. We demonstrate the utility of this pipeline across short-read and long-read technologies.

A High-Quality Blue Whale Genome, Segmental Duplications, and Historical Demography.

The blue whale, Balaenoptera musculus, is the largest animal known to have ever existed, making it an important case study in longevity and resistance to cancer. To further this and other blue whale-related research, we report a reference-quality, long-read-based genome assembly of this fascinating species. We assembled the genome from PacBio long reads and utilized Illumina/10×, optical maps, and Hi-C data for scaffolding, polishing, and manual curation. We also provided long read RNA-seq data to facilitate the annotation of the assembly by NCBI and Ensembl. Additionally, we annotated both haplotypes using TOGA and measured the genome size by flow cytometry. We then compared the blue whale genome with other cetaceans and artiodactyls, including vaquita (Phocoena sinus), the world's smallest cetacean, to investigate blue whale's unique biological traits. We found a dramatic amplification of several genes in the blue whale genome resulting from a recent burst in segmental duplications, though the possible connection between this amplification and giant body size requires further study. We also discovered sites in the insulin-like growth factor-1 gene correlated with body size in cetaceans. Finally, using our assembly to examine the heterozygosity and historical demography of Pacific and Atlantic blue whale populations, we found that the genomes of both populations are highly heterozygous and that their genetic isolation dates to the last interglacial period. Taken together, these results indicate how a high-quality, annotated blue whale genome will serve as an important resource for biology, evolution, and conservation research.

Chromosome level genome assembly of the Etruscan shrew Suncus etruscus.

Suncus etruscus is one of the world's smallest mammals, with an average body mass of about 2 grams. The Etruscan shrew's small body is accompanied by a very high energy demand and numerous metabolic adaptations. Here we report a chromosome-level genome assembly using PacBio long read sequencing, 10X Genomics linked short reads, optical mapping, and Hi-C linked reads. The assembly is partially phased, with the 2.472 Gbp primary pseudohaplotype and 1.515 Gbp alternate. We manually curated the primary assembly and identified 22 chromosomes, including X and Y sex chromosomes. The NCBI genome annotation pipeline identified 39,091 genes, 19,819 of them protein-coding. We also identified segmental duplications, inferred GO term annotations, and computed orthologs of human and mouse genes. This reference-quality genome will be an important resource for research on mammalian development, metabolism, and body size control.

Benchmarking of small and large variants across tandem repeats.

Tandem repeats (TRs) are highly polymorphic in the human genome, have thousands of associated molecular traits, and are linked to over 60 disease phenotypes. However, their complexity often excludes them from at-scale studies due to challenges with variant calling, representation, and lack of a genome-wide standard. To promote TR methods development, we create a comprehensive catalog of TR regions and explore its properties across 86 samples. We then curate variants from the GIAB HG002 individual to create a tandem repeat benchmark. We also present a variant comparison method that handles small and large alleles and varying allelic representation. The 8.1% of the genome covered by the TR catalog holds ∼24.9% of variants per individual, including 124,728 small and 17,988 large variants for the GIAB HG002 TR benchmark. We work with the GIAB community to demonstrate the utility of this benchmark across short and long read technologies.

Advances in the discovery and analyses of human tandem repeats.

Long-read sequencing platforms provide unparalleled access to the structure and composition of all classes of tandemly repeated DNA from STRs to satellite arrays. This review summarizes our current understanding of their organization within the human genome, their importance with respect to disease, as well as the advances and challenges in understanding their genetic diversity and functional effects. Novel computational methods are being developed to visualize and associate these complex patterns of human variation with disease, expression, and epigenetic differences. We predict accurate characterization of this repeat-rich form of human variation will become increasingly relevant to both basic and clinical human genetics.

Scalable Nanopore sequencing of human genomes provides a comprehensive view of haplotype-resolved variation and methylation.

Long-read sequencing technologies substantially overcome the limitations of short-reads but have not been considered as a feasible replacement for population-scale projects, being a combination of too expensive, not scalable enough or too error-prone. Here we develop an efficient and scalable wet lab and computational protocol, Napu, for Oxford Nanopore Technologies long-read sequencing that seeks to address those limitations. We applied our protocol to cell lines and brain tissue samples as part of a pilot project for the National Institutes of Health Center for Alzheimer's and Related Dementias. Using a single PromethION flow cell, we can detect single nucleotide polymorphisms with F1-score comparable to Illumina short-read sequencing. Small indel calling remains difficult within homopolymers and tandem repeats, but achieves good concordance to Illumina indel calls elsewhere. Further, we can discover structural variants with F1-score on par with state-of-the-art de novo assembly methods. Our protocol phases small and structural variants at megabase scales and produces highly accurate, haplotype-specific methylation calls.

HQAlign: aligning nanopore reads for SV detection using current-level modeling.

MOTIVATION: Detection 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. RESULTS: We 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 IMPLEMENTATION: https://github.com/joshidhaivat/HQAlign.git.

vamos: variable-number tandem repeats annotation using efficient motif sets.

Roughly 3% of the human genome is composed of variable-number tandem repeats (VNTRs): arrays of motifs at least six bases. These loci are highly polymorphic, yet current approaches that define and merge variants based on alignment breakpoints do not capture their full diversity. Here we present a method vamos: VNTR Annotation using efficient Motif Sets that instead annotates VNTR using repeat composition under different levels of motif diversity. Using vamos we estimate 7.4-16.7 alleles per locus when applied to 74 haplotype-resolved human assemblies, compared to breakpoint-based approaches that estimate 4.0-5.5 alleles per locus.

Scalable, accessible, and reproducible reference genome assembly and evaluation in Galaxy.

Improvements in genome sequencing and assembly are enabling high-quality reference genomes for all species. However, the assembly process is still laborious, computationally and technically demanding, lacks standards for reproducibility, and is not readily scalable. Here we present the latest Vertebrate Genomes Project assembly pipeline and demonstrate that it delivers high-quality reference genomes at scale across a set of vertebrate species arising over the last ~500 million years. The pipeline is versatile and combines PacBio HiFi long-reads and Hi-C-based haplotype phasing in a new graph-based paradigm. Standardized quality control is performed automatically to troubleshoot assembly issues and assess biological complexities. We make the pipeline freely accessible through Galaxy, accommodating researchers even without local computational resources and enhanced reproducibility by democratizing the training and assembly process. We demonstrate the flexibility and reliability of the pipeline by assembling reference genomes for 51 vertebrate species from major taxonomic groups (fish, amphibians, reptiles, birds, and mammals).

The motif composition of variable number tandem repeats impacts gene expression.

Understanding the impact of DNA variation on human traits is a fundamental question in human genetics. Variable number tandem repeats (VNTRs) make up ∼3% of the human genome but are often excluded from association analysis owing to poor read mappability or divergent repeat content. Although methods exist to estimate VNTR length from short-read data, it is known that VNTRs vary in both length and repeat (motif) composition. Here, we use a repeat-pangenome graph (RPGG) constructed on 35 haplotype-resolved assemblies to detect variation in both VNTR length and repeat composition. We align population-scale data from the Genotype-Tissue Expression (GTEx) Consortium to examine how variations in sequence composition may be linked to expression, including cases independent of overall VNTR length. We find that 9422 out of 39,125 VNTRs are associated with nearby gene expression through motif variations, of which only 23.4% are accessible from length. Fine-mapping identifies 174 genes to be likely driven by variation in certain VNTR motifs and not overall length. We highlight two genes, CACNA1C and RNF213, that have expression associated with motif variation, showing the utility of RPGG analysis as a new approach for trait association in multiallelic and highly variable loci.

Structural variation across 138,134 samples in the TOPMed consortium.

Ever larger Structural Variant (SV) catalogs highlighting the diversity within and between populations help researchers better understand the links between SVs and disease. The identification of SVs from DNA sequence data is non-trivial and requires a balance between comprehensiveness and precision. Here we present a catalog of 355,667 SVs (59.34% novel) across autosomes and the X chromosome (50bp+) from 138,134 individuals in the diverse TOPMed consortium. We describe our methodologies for SV inference resulting in high variant quality and >90% allele concordance compared to long-read de-novo assemblies of well-characterized control samples. We demonstrate utility through significant associations between SVs and important various cardio-metabolic and hemotologic traits. We have identified 690 SV hotspots and deserts and those that potentially impact the regulation of medically relevant genes. This catalog characterizes SVs across multiple populations and will serve as a valuable tool to understand the impact of SV on disease development and progression.

Structural variation across 138,134 samples in the TOPMed consortium.

Ever larger Structural Variant (SV) catalogs highlighting the diversity within and between populations help researchers better understand the links between SVs and disease. The identification of SVs from DNA sequence data is non-trivial and requires a balance between comprehensiveness and precision. Here we present a catalog of 355,667 SVs (59.34% novel) across autosomes and the X chromosome (50bp+) from 138,134 individuals in the diverse TOPMed consortium. We describe our methodologies for SV inference resulting in high variant quality and >90% allele concordance compared to long-read de-novo assemblies of well-characterized control samples. We demonstrate utility through significant associations between SVs and important various cardio-metabolic and hematologic traits. We have identified 690 SV hotspots and deserts and those that potentially impact the regulation of medically relevant genes. This catalog characterizes SVs across multiple populations and will serve as a valuable tool to understand the impact of SV on disease development and progression.

Scalable Nanopore sequencing of human genomes provides a comprehensive view of haplotype-resolved variation and methylation.

Long-read sequencing technologies substantially overcome the limitations of short-reads but to date have not been considered as feasible replacement at scale due to a combination of being too expensive, not scalable enough, or too error-prone. Here, we develop an efficient and scalable wet lab and computational protocol for Oxford Nanopore Technologies (ONT) long-read sequencing that seeks to provide a genuine alternative to short-reads for large-scale genomics projects. We applied our protocol to cell lines and brain tissue samples as part of a pilot project for the NIH Center for Alzheimer's and Related Dementias (CARD). Using a single PromethION flow cell, we can detect SNPs with F1-score better than Illumina short-read sequencing. Small indel calling remains to be difficult inside homopolymers and tandem repeats, but is comparable to Illumina calls elsewhere. Further, we can discover structural variants with F1-score comparable to state-of the-art methods involving Pacific Biosciences HiFi sequencing and trio information (but at a lower cost and greater throughput). Using ONT based phasing, we can then combine and phase small and structural variants at megabase scales. Our protocol also produces highly accurate, haplotype-specific methylation calls. Overall, this makes large-scale long-read sequencing projects feasible; the protocol is currently being used to sequence thousands of brain-based genomes as a part of the NIH CARD initiative. We provide the protocol and software as open-source integrated pipelines for generating phased variant calls and assemblies.

HQAlign: Aligning nanopore reads for SV detection using current-level modeling.

Motivation: Detection of structural variants (SV) 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 paper, we design and evaluate HQAlign, an aligner for SV detection using nanopore sequenced reads. The key ideas of HQAlign include (i) using basecalled nanopore reads along with the nanopore physics to improve alignments for SVs (ii) incorporating SV specific changes to the alignment pipeline (iii) adapting these into existing state-of-the-art long read aligner pipeline, minimap2 (v2.24), for efficient alignments. Results: We 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 for about 10 - 50% of 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: https://github.com/joshidhaivat/HQAlign.git.

HQAlign: Aligning nanopore reads for SV detection using current-level modeling.

MOTIVATION: Detection of structural variants (SV) 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 paper, we design and evaluate HQAlign, an aligner for SV detection using nanopore sequenced reads. The key ideas of HQAlign include (i) using basecalled nanopore reads along with the nanopore physics to improve alignments for SVs (ii) incorporating SV specific changes to the alignment pipeline (iii) adapting these into existing state-of-the-art long read aligner pipeline, minimap2 (v2.24), for efficient alignments. RESULTS: We 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 for about 10%-50% of 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.

A haplotype-resolved genome assembly of the Nile rat facilitates exploration of the genetic basis of diabetes.

BACKGROUND: The Nile rat (Avicanthis niloticus) is an important animal model because of its robust diurnal rhythm, a cone-rich retina, and a propensity to develop diet-induced diabetes without chemical or genetic modifications. A closer similarity to humans in these aspects, compared to the widely used Mus musculus and Rattus norvegicus models, holds the promise of better translation of research findings to the clinic. RESULTS: We report a 2.5 Gb, chromosome-level reference genome assembly with fully resolved parental haplotypes, generated with the Vertebrate Genomes Project (VGP). The assembly is highly contiguous, with contig N50 of 11.1 Mb, scaffold N50 of 83 Mb, and 95.2% of the sequence assigned to chromosomes. We used a novel workflow to identify 3613 segmental duplications and quantify duplicated genes. Comparative analyses revealed unique genomic features of the Nile rat, including some that affect genes associated with type 2 diabetes and metabolic dysfunctions. We discuss 14 genes that are heterozygous in the Nile rat or highly diverged from the house mouse. CONCLUSIONS: Our findings reflect the exceptional level of genomic resolution present in this assembly, which will greatly expand the potential of the Nile rat as a model organism.

Semi-automated assembly of high-quality diploid human reference genomes.

The current human reference genome, GRCh38, represents over 20 years of effort to generate a high-quality assembly, which has benefitted society1,2. However, it still has many gaps and errors, and does not represent a biological genome as it is a blend of multiple individuals3,4. Recently, a high-quality telomere-to-telomere reference, CHM13, was generated with the latest long-read technologies, but it was derived from a hydatidiform mole cell line with a nearly homozygous genome5. To address these limitations, the Human Pangenome Reference Consortium formed with the goal of creating high-quality, cost-effective, diploid genome assemblies for a pangenome reference that represents human genetic diversity6. Here, in our first scientific report, we determined which combination of current genome sequencing and assembly approaches yield the most complete and accurate diploid genome assembly with minimal manual curation. Approaches that used highly accurate long reads and parent-child data with graph-based haplotype phasing during assembly outperformed those that did not. Developing a combination of the top-performing methods, we generated our first high-quality diploid reference assembly, containing only approximately four gaps per chromosome on average, with most chromosomes within ±1% of the length of CHM13. Nearly 48% of protein-coding genes have non-synonymous amino acid changes between haplotypes, and centromeric regions showed the highest diversity. Our findings serve as a foundation for assembling near-complete diploid human genomes at scale for a pangenome reference to capture global genetic variation from single nucleotides to structural rearrangements.