Biology subjects

Kirsche, M.

Publications and source records attributed to Kirsche, M..

9 recordsLinked to original sources

bioRxiv2022

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 greatly benefited society1, 2. However, it still has many gaps and errors, and does not represent a biological human genome since it is a blend of multiple individuals3, 4. Recently, a high-quality telomere-to-telomere reference genome, CHM13, was generated with the latest long-read technologies, but it was derived from a hydatidiform mole cell line with a duplicate genome, and is thus nearly homozygous5. To address these limitations, the Human Pangenome Reference Consortium (HPRC) recently formed with the goal of creating a collection of 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 automated assembly approaches yields the most complete, accurate, and cost-effective diploid genome assemblies with minimal manual curation. Approaches that used highly accurate long reads and parent-child data to sort haplotypes during assembly outperformed those that did not. Developing a combination of all the top performing methods, we generated our first high- quality diploid reference assembly, containing only [~]4 gaps (range 0-12) per chromosome, most within + 1% of CHM13s length. Nearly 1/4th of protein coding genes have synonymous amino acid changes between haplotypes, and centromeric regions showed the highest density of variation. Our findings serve as a foundation for assembling near-complete diploid human genomes at the scale required for constructing a human pangenome reference that captures all genetic variation from single nucleotides to large structural rearrangements.

genomics↗

bioRxiv2021

Automated assembly scaffolding elevates a new tomato system for high-throughput genome editing

Advancing crop genomics requires efficient genetic systems enabled by high-quality personalized genome assemblies. Here, we introduce RagTag, a toolset for automating assembly scaffolding and patching, and we establish chromosome-scale reference genomes for the widely used tomato genotype M82 along with Sweet-100, a rapid-cycling genotype that we developed to accelerate functional genomics and genome editing. This work outlines strategies to rapidly expand genetic systems and genomic resources in other plant species.

plant biology↗

bioRxiv2021

A complete reference genome improves analysis of human genetic variation

Compared to its predecessors, the Telomere-to-Telomere CHM13 genome adds nearly 200 Mbp of sequence, corrects thousands of structural errors, and unlocks the most complex regions of the human genome to clinical and functional study. Here we demonstrate how the new reference universally improves read mapping and variant calling for 3,202 and 17 globally diverse samples sequenced with short and long reads, respectively. We identify hundreds of thousands of novel variants per sample--a new frontier for evolutionary and biomedical discovery. Simultaneously, the new reference eliminates tens of thousands of spurious variants per sample, including up to 12-fold reduction of false positives in 269 medically relevant genes. The vast improvement in variant discovery coupled with population and functional genomic resources position T2T-CHM13 to replace GRCh38 as the prevailing reference for human genetics. One Sentence SummaryThe T2T-CHM13 reference genome universally improves the analysis of human genetic variation.

genomics↗

bioRxiv2021

Jasmine: Population-scale structural variant comparison and analysis

The increasing availability of long-reads is revolutionizing studies of structural variants (SVs). However, because SVs vary across individuals and are discovered through imprecise read technologies and methods, they can be difficult to compare. Addressing this, we present Jasmine (https://github.com/mkirsche/Jasmine), a fast and accurate method for SV refinement, comparison, and population analysis. Using an SV proximity graph, Jasmine outperforms five widely-used comparison methods, including reducing the rate of Mendelian discordance in trio datasets by more than five-fold, and reveals a set of high confidence de novo SVs confirmed by multiple long-read technologies. We also present a harmonized callset of 205,192 SVs from 31 samples of diverse ancestry sequenced with long reads. We genotype these SVs in 444 short read samples from the 1000 Genomes Project with both DNA and RNA sequencing data and assess their widespread impact on gene expression, including within several medically relevant genes.

genomics↗

bioRxiv2021

Multi-tissue integrative analysis of personal epigenomes

Understanding how genetic variants impact molecular phenotypes is a key goal of functional genomics, currently hindered by reliance on a single haploid reference genome. Here, we present the EN-TEx resource of personal epigenomes, for [~]25 tissues and >10 assays in four donors (>1500 open-access functional genomic and proteomic datasets, in total). Each dataset is mapped to a matched, diploid personal genome, which has long-read phasing and structural variants. The mappings enable us to identify >1 million loci with allele-specific behavior. These loci exhibit coordinated epigenetic activity along haplotypes and less conservation than matched, non-allele-specific loci, in a fashion broadly paralleling tissue-specificity. Surprisingly, they can be accurately modelled just based on local nucleotide-sequence context. Combining EN-TEx with existing genome annotations reveals strong associations between allele-specific and GWAS loci and enables models for transferring known eQTLs to difficult-to-profile tissues. Overall, EN-TEx provides rich data and generalizable models for more accurate personal functional genomics.

genomics↗

bioRxiv2020

Benchmarking challenging small variants with linked and long reads

Genome in a Bottle (GIAB) benchmarks have been widely used to help validate clinical sequencing pipelines and develop new variant calling and sequencing methods. Here, we use accurate linked reads and long reads to expand the prior benchmarks in 7 samples to include difficult-to-map regions and segmental duplications that are not readily accessible to short reads. Our new benchmark adds more than 300,000 SNVs, 50,000 indels, and 16 % new exonic variants, many in challenging, clinically relevant genes not previously covered (e.g., PMS2). For HG002, we include 92% of the autosomal GRCh38 assembly, while excluding problematic regions for benchmarking small variants (e.g., copy number variants and reference errors) that should not have been in the previous version, which included 85% of GRCh38. By including difficult-to-map regions, this benchmark identifies eight times more false negatives in a short read variant call set relative to our previous benchmark.We have demonstrated the utility of this benchmark to reliably identify false positives and false negatives across technologies in more challenging regions, which enables continued technology and bioinformatics development.

genomics↗

bioRxiv2020

Sapling: Accelerating Suffix Array Queries with Learned Data Models

MotivationAs genomic data becomes more abundant, efficient algorithms and data structures for sequence alignment become increasingly important. The suffix array is a widely used data structure to accelerate alignment, but the binary search algorithm used to query it requires widespread memory accesses, causing a large number of cache misses on large datasets. ResultsHere we present Sapling, an algorithm for sequence alignment which uses a learned data model to augment the suffix array and enable faster queries. We investigate different types of data models, providing an analysis of different neural network models as well as providing an open-source aligner with a compact, practical piecewise linear model. We show that Sapling outperforms both an optimized binary search approach and multiple existing read aligners on a wide collection of genomes, including human, bacteria, and plants, speeding up the algorithm by more than a factor of two while adding less than 1% to the suffix arrays memory footprint. Availability and implementationThe source code and tutorial are available open-source at https://github.com/mkirsche/sapling. Supplementary InformationSupplementary notes and figures are available online.

bioinformatics↗

bioRxiv2019

Comprehensive analysis of structural variants in breast cancer genomes using single molecule sequencing

Improved identification of structural variants (SVs) in cancer can lead to more targeted and effective treatment options as well as advance our basic understanding of disease progression. We performed whole genome sequencing of the SKBR3 breast cancer cell-line and patient-derived tumor and normal organoids from two breast cancer patients using 10X/Illumina, PacBio, and Oxford Nanopore sequencing. We then inferred SVs and large-scale allele-specific copy number variants (CNVs) using an ensemble of methods. Our findings demonstrate that long-read sequencing allows for substantially more accurate and sensitive SV detection, with between 90% and 95% of variants supported by each long-read technology also supported by the other. We also report high accuracy for long-reads even at relatively low coverage (25x-30x). Furthermore, we inferred karyotypes from these data using our enhanced RCK algorithm to present a more accurate representation of the mutated cancer genomes, and find hundreds of variants affecting known cancer-related genes detectable only through long-read sequencing. These findings highlight the need for long-read sequencing of cancer genomes for the precise analysis of their genetic instability.

bioinformatics↗

bioRxiv2019

Paragraph: A graph-based structural variant genotyper for short-read sequence data

Accurate detection and genotyping of structural variations (SVs) from short-read data is a long-standing area of development in genomics research and clinical sequencing pipelines. We introduce Paragraph, an accurate genotyper that models SVs using sequence graphs and SV annotations. We demonstrate the accuracy of Paragraph on whole-genome sequence data from three samples using long read SV calls as the truth set, and then apply Paragraph at scale to a cohort of 100 short-read sequenced samples of diverse ancestry. Our analysis shows that Paragraph has better accuracy than other existing genotypers and can be applied to population-scale studies.

genomics↗