Biology subjects

Saunders, C. T.

Publications and source records attributed to Saunders, C. T..

12 recordsLinked to original sources

bioRxiv2026

The Genome In A Bottle HG002 assembly-based variant benchmark set enables comprehensive benchmarking of small and structural variants.

High-accuracy, high-resolution benchmark data are fundamental to improving the accuracy and generalizability of sequencing technologies and associated algorithms. Most existing benchmarks for DNA sequencing rely on mapping to a single linear reference, omitting inaccessible and highly repetitive complex genomic regions. Here, we present the Genome in a Bottle (GIAB) Consortium's HG002 v5.0q benchmark set, a more comprehensive variant benchmark derived from a fully phased, highly curated, and accurate diploid assembly of HG002. Our approach leverages recent advances in long-read sequencing and assembly to provide confident small- and structural-variant calls across previously inaccessible regions, including segmental duplications, homopolymers, tandem repeats, and complex immune loci. The v5.0 benchmark significantly expands the callable genome relative to v4.2.1, adding x200 Mbp of sequence and increasing variant detection by 18 % for small variants and 3-fold for structural variants compared to previous benchmarks. The benchmark regions include more variants in repetitive regions, including 1.2 x segmental duplications, 1.8 x tandem repeats, and 2.0 x homopolymers, compared to v4.2.1. Separate and combined small- and structural-variant benchmarks are available for three versions of the human reference genome (GRCh37, GRCh38, and CHM13). We validate the reliability of this benchmark set by comparing multiple sequencing technologies and variant calling pipelines and curating differences. The v5.0q benchmark provides a critical resource for the development and validation of next-generation sequencing technologies and analysis methods.

genomics↗

bioRxiv2026

Portello: Making global assembly more effective for rare-disease whole genome sequencing

MotivationLong-read de novo assembly methods provide great potential to improve whole genome sequencing analysis for rare-disease, however these methods are typically under-utilized. Among the complications of an assembly-based approach is the difficulty of reviewing read evidence for assembly-based inferences, including access to read-based information on basecall qualities, methylation, and mosaic variants not captured in the assembly consensus. An additional complication is resolving conflicts in variant representation between assembly-based inferences and those based on conventional reference-based read-mapping pipelines. Here we propose a new assembly-based mapping approach, portello, which addresses both of these issues by transferring read mappings from the samples de novo assembly contigs onto a standard reference sequence. ResultsPortellos assembly-based mapping approach results in read-to-reference alignments that can be used by standard mapping-based variant callers without modification, resulting in improved accuracy due to higher-quality mapping. This approach also enables assembly and reference mapping-based inferences to be unified onto a consistent view of the sample, and enables direct review of read-level support for the assembly consensus in the context of a standard reference genome. To demonstrate portellos impact on variant calling, we show that DeepVariant calls from portello alignments remove 47% of small variant basecall errors found in calls made from conventional read mapping. We additionally demonstrate how portello alignments can improve visualization and interpretation of complex loci, including an example where a copy number gain in a segmental duplication region can be easily inferred from assembly-based mapping while interpretation of the same region from conventional read mapping remains impractical. Portello is also capable of directly phasing and haplotagging read alignments as part of the alignment transfer process, including for partially-phased assembly contig inputs where phasing blocks need to be delineated within each contig. AvailabilityThe portello source and pre-compiled binary is released on GitHub: https://github.com/PacificBiosciences/portello.

genomics↗

bioRxiv2025

Aardvark: Sifting through differences in a mound of variants

Variant benchmarking is critical in assessing the accuracy of genomic secondary pipelines. However, traditional benchmarking tools that require exact genotype matches inject biases from variant representation and are ill-suited for tandem repeat or structural variation. We describe Aardvark, a variant benchmarking tool that introduces the basepair score to directly compare haplotype sequences, reducing representation biases while allowing for partial credit scoring. The tool also includes a traditional genotype score and supports separate or joint benchmarking of small variants, tandem repeats, and structural variants (<10 kb). Aardvark accepts standard inputs, runs {approx}16x faster than hap.py, and is freely available and open source (https://github.com/PacificBiosciences/aardvark).

bioinformatics↗

bioRxiv2025

Complex structural variant visualization with SVTopo

Structural variants are genomic variants that impact at least 50 nucleotides and can play major roles in diversity and human health. Many structural variants are complex multi-breakpoint rearrangements that are difficult to comprehend with existing visualization tools. We present SVTopo, a tool to visualize complex structural variants with supporting evidence from high-accuracy long reads, in easily understood figures. We include examples of eleven categories of complex structural variants from seven human genomes. SVTopo shows breakpoint evidence in ways that aid reasoning about the impact of large, multi-breakpoint events such as inversions, translocations, and combinations of simpler structural variants.

bioinformatics↗

bioRxiv2025

A Murine Database of Structural Variants Enables the Genetic Architecture of a Spontaneous Murine Lymphoma to be Characterized

A more complete map of the pattern of genetic variation among inbred mouse strains is essential for characterizing the genetic architecture of the many available mouse genetic models of important biomedical traits. Although structural variants (SVs) are a major component of genetic variation, they have not been adequately characterized among inbred strains due to methodological limitations. To address this, we generated high-quality long-read sequencing data for 40 inbred strains; and designed a pipeline to optimally identify and validate different types of SVs. This generated a database for 40 inbred strains with 573,191SVs, which included 10,815 duplications and 2,115 inversions, which also has 70 million SNPs and 7.5 million insertions/deletions. Analysis of this SV database identified an SV that is one component of a bi-genic model for lymphoma susceptibility in SJL mice, which provides mechanistic insight into the genetic basis for susceptibility to murine (and potentially human) lymphomas.

genetics↗

bioRxiv2024

StarPhase: Comprehensive Phase-Aware Pharmacogenomic Diplotyper for Long-Read Sequencing Data

Pharmacogenomics is central to precision medicine, informing medication safety and efficacy. Phar-macogenomic diplotyping of complex genes requires full-length DNA sequences and detection of structural rearrangements. We introduce StarPhase, a tool that leverages PacBio HiFi sequence data to diplotype 21 CPIC Level A pharmacogenes and provides detailed haplotypes and supporting visualizations for HLA-A, HLA-B, and CYP2D6. StarPhase diplotypes have high concordance with benchmarks where 99.5% are either exact matches or minor discrepancies. Manual inspection of the 0.5% mismatches indicates they were correctly called by StarPhase. With StarPhase, we update or correct 26.2% of GeT-RM pharmacogenomic diplotypes. Population distributions from StarPhase mostly reflect those of the All of Us cohort, while also highlighting gaps in existing pharmacogenomic databases that long-read sequencing can fill. With a single HiFi whole genome sequencing assay, StarPhase enables robust PGx diplotyping even as additional pharmacogenes and haplotypes are discovered.

bioinformatics↗

bioRxiv2024

The Platinum Pedigree: A long-read benchmark for genetic variants

Recent advances in genome sequencing have improved variant calling in complex regions of the human genome. However, it is difficult to quantify variant calling performance since existing standards often focus on specificity, neglecting completeness in difficult to analyze regions. To create a more comprehensive truth set, we used Mendelian inheritance in a large pedigree (CEPH-1463) to filter variants across Illumina, PacBio high-fidelity (HiFi), and Oxford Nanopore Technologies platforms. This generated a variant map with over 4.7 million single-nucleotide variants, 767,795 indels, 537,486 tandem repeats, and 24,315 structural variants, covering 2.77 Gb of the GRCh38 genome. This work adds [~]200 Mb of high-confidence regions, including 8% more small variants, and introduces the first tandem repeat and structural variant truth sets for NA12878. As an example of the value of this improved benchmark, we retrained DeepVariant using this data to reduce genotyping errors by [~]34%.

genomics↗

bioRxiv2024

Sawfish: Improving long-read structural variant discovery and genotyping with local haplotype modeling

MotivationStructural variants (SVs) play an important role in evolutionary and functional genomics but are challenging to characterize. High-accuracy, long-read sequencing can substantially improve SV characterization when coupled with effective calling methods. While state-of the-art long-read SV callers are highly accurate, further improvements are achievable by systematically modeling local haplotypes during SV discovery and genotyping. ResultsWe describe sawfish, an SV caller for mapped high-quality long reads incorporating systematic SV haplotype modeling to improve accuracy and resolution. Assessment against the draft Genome in a Bottle (GIAB) SV benchmark from the T2T-HG002-Q100 diploid assembly shows that sawfish has the highest accuracy among state-of-the-art long-read SV callers across every tested SV size group. Additionally, sawfish maintains the highest accuracy at every tested depth level from 10 to 32-fold coverage, such that other callers required at least 30-fold coverage to match sawfish accuracy at 15-fold coverage. Sawfish also shows the highest accuracy in the GIAB challenging medically relevant genes benchmark, demonstrating improvements in both comprehensive and medically relevant contexts. When joint-genotyping 10 samples from CEPH-1463, sawfish has over 9000 more pedigree-concordant calls than other state-of-the-art SV callers, with the highest proportion of concordant SVs (78%) as well. Sawfishs quality model can be used to select for an even higher proportion of concordant SVs (86%), while still calling over 5000 more pedigree-concordant SVs than other callers. These results demonstrate that sawfish improves on the state-of-the-art for long-read SV calling accuracy across both individual and joint-sample analyses. AvailabilitySawfish is released as a pre-compiled Linux binary and user guide on GitHub: https://github.com/PacificBiosciences/sawfish.

bioinformatics↗

bioRxiv2024

LCSKPOA: Enabling banded semi-global partial order alignments via efficient and accurate backbone generation through extended lcsk++

BackgroundMost multiple sequence alignment and string-graph alignment algorithms focus on global alignment, but many applications exist for semi-global and local string-graph alignment. Long reads require enormous amounts of memory and runtime to fill out large dynamic programming tables. Effective algorithms for finding the backbone and thus defining a band of an alignment such as the longest common subsequence with kmer matches (LCSk++) exist but do not work with graphs. This study introduces an adaptation of the Longest Common Subsequence with kmer matches (LCSk++) algorithm tailored for graph structures, particularly focusing on Partial Order Alignment (POA) graphs. POA graphs, which are directed acyclic graphs, represent multiple sequence alignments and effectively capture the relationships between sequences. Current state of the art methods like ABPOA and SPOA, while improving POA, primarily focus on global alignment and thus are limited in local and semi-global banding scenarios. Our approach addresses these limitations by extending the LCSk++ algorithm to accommodate the complexities of graph-based alignment. ResultsOur extended LCSk++ algorithm integrates dynamic programming and graph traversal techniques to detect conserved regions within POA graphs, termed the LCSk++ backbone. This backbone enables precise banding of the POA matrix for local and semi-global alignment, significantly enhancing the construction of consensus sequences. Compared to unbanded semi-global POA, our method demonstrates substantial memory savings (up to 98%) and significant run-time reductions (up to 37-fold), particularly for long sequences. The method maintains high alignment scores and proves effective across various string lengths and datasets, including synthetic and PacBio HiFi reads. Parallel processing further enhances runtime efficiency, achieving up to 150x speed improvements on conventional PCs. ConclusionThe extended LCSk++ algorithm for graph structures offers a substantial advancement in sequence alignment technology. It effectively reduces memory consumption and optimizes run times without compromising alignment quality, thus providing a robust solution for local and semi-global alignment in POA graphs. This method enhances the utility of POA in critical applications such as multiple sequence alignment for phylogeny construction and graph-based reference alignment.

genomics↗

bioRxiv2023

Synchronized long-read genome, methylome, epigenome, and transcriptome for resolving a Mendelian condition

Resolving the molecular basis of a Mendelian condition (MC) remains challenging owing to the diverse mechanisms by which genetic variants cause disease. To address this, we developed a synchronized long-read genome, methylome, epigenome, and transcriptome sequencing approach, which enables accurate single-nucleotide, insertion-deletion, and structural variant calling and diploid de novo genome assembly, and permits the simultaneous elucidation of haplotype-resolved CpG methylation, chromatin accessibility, and full-length transcript information in a single long-read sequencing run. Application of this approach to an Undiagnosed Diseases Network (UDN) participant with a chromosome X;13 balanced translocation of uncertain significance revealed that this translocation disrupted the functioning of four separate genes (NBEA, PDK3, MAB21L1, and RB1) previously associated with single-gene MCs. Notably, the function of each gene was disrupted via a distinct mechanism that required integration of the four omes to resolve. These included nonsense-mediated decay, fusion transcript formation, enhancer adoption, transcriptional readthrough silencing, and inappropriate X chromosome inactivation of autosomal genes. Overall, this highlights the utility of synchronized long-read multi-omic profiling for mechanistically resolving complex phenotypes.

genetics↗

bioRxiv2023

HiPhase: Jointly phasing small and structural variants from HiFi sequencing

BackgroundIn diploid organisms, phasing is the problem of assigning the heterozygous variants to one of two haplotypes. Reads from PacBio HiFi sequencing provide long, accurate observations that can be used as the basis for both calling and phasing variants. HiFi reads also excel at calling larger classes of variation such as structural variants. However, current phasing tools typically only phase small variants, leaving larger structural variants unphased. MethodsWe developed HiPhase, a tool that jointly phases SNVs, indels, and structural variants. The main benefits of HiPhase are 1) dual mode allele assignment for detecting structural variants, 2) a novel application of the A*-algorithm to phasing, and 3) logic allowing phase blocks to span breaks caused by alignment issues around reference gaps and homozygous deletions. ResultsIn our assessment, HiPhase produced an average phase block NG50 of 493 kb with 933 switchflip errors and fully phased 95.2% of genes, improving over the current state of the art. Additionally, HiPhase jointly phases SNVs, indels, and structural variants and includes innate multi-threading, statistics gathering, and concurrent phased alignment output generation. Availabilityhttps://github.com/PacificBiosciences/HiPhase

bioinformatics↗

bioRxiv2017

Strelka2: Fast and accurate variant calling for clinical sequencing applications

We describe Strelka2 (https://github.com/Illumina/strelka), an open-source small variant calling method for clinical germline and somatic sequencing applications. Strelka2 introduces a novel mixture-model based estimation of indel error parameters from each sample, an efficient tiered haplotype modeling strategy and a normal sample contamination model to improve liquid tumor analysis. For both germline and somatic calling, Strelka2 substantially outperforms current leading tools on both variant calling accuracy and compute cost.

bioinformatics↗