Biology subjects

Schatz, M. C.

Publications and source records attributed to Schatz, M. C..

53 records · Page 3Linked to original sources

bioRxiv2019

Vargas: heuristic-free alignment for assessing linear and graph read aligners

Read alignment is central to many aspects of modern genomics. Most aligners use heuristics to accelerate processing, but these heuristics can fail to find the optimal alignments of reads. Alignment accuracy is typically measured through simulated reads; however, the simulated location may not be the (only) location with the optimal alignment score. Vargas implements a heuristic-free algorithm guaranteed to find the highest-scoring alignment for real sequencing reads to a linear or graph genome. With semiglobal and local alignment modes and affine gap and quality-scaled mismatch penalties, it can implement the scoring functions of commonly used aligners to calculate optimal alignments. While this is computationally intensive, Vargas uses multi-core parallelization and vectorized (SIMD) instructions to make it practical to optimally align large numbers of reads, achieving a maximum speed of 456 billion cell updates per second. We demonstrate how these "gold standard" Vargas alignments can be used to improve heuristic alignment accuracy by optimizing command-line parameters in Bowtie 2, BWA-MEM, and vg to align more reads correctly. Source code implemented in C++ and compiled binary releases are available at https://github.com/langmead-lab/vargas under the MIT license.

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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.

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bioRxiv2019

Recovering rearranged cancer chromosomes from karyotype graphs

Many cancer genomes are extensively rearranged with highly aberrant chromosomal karyotypes. Structural and copy number variations in cancer genomes can be determined via abnormal mapping of sequenced reads to the reference genome. Recently it became possible to reconcile both of these types of large-scale variations into a karyotype graph representation of the rearranged cancer genomes. Such a representation, however, does not directly describe the linear and/or circular structure of the underlying rearranged cancer chromosomes, thus limiting possible analysis of cancer genomes somatic evolutionary process as well as functional genomic changes brought by the large-scale genome rearrangements.\n\nHere we address the aforementioned limitation by introducing a novel methodological framework for recovering rearranged cancer chromosomes from karyotype graphs. For a cancer karyotype graph we formulate an Eulerian Decomposition Problem (EDP) of finding a collection of linear and/or circular rearranged cancer chromosomes that are determined by the graph. We derive and prove computational complexities for several variations of the EDP. We then demonstrate that Eulerian decomposition of the cancer karyotype graphs is not always unique and present the Consistent Contig Covering Problem (CCCP) of recovering unambiguous cancer contigs from the cancer karyotype graph, and describe a novel algorithm CCR capable of solving CCCP in polynomial time.\n\nWe apply CCR on a prostate cancer dataset and demonstrate that it is capable of consistently recovering large cancer contigs even when underlying cancer genomes are highly rearranged. CCR can recover rearranged cancer contigs from karyotype graphs thereby addressing existing limitation in inferring chromosomal structures of rearranged cancer genomes and advancing our understanding of both patient/cancer-specific as well as the overall genetic instability in cancer.

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bioRxiv2019

GenomeScope 2.0 and Smudgeplots: Reference-free profiling of polyploid genomes

An important assessment prior to genome assembly and related analyses is genome profiling, where the k-mer frequencies within raw sequencing reads are analyzed to estimate major genome characteristics such as genome size, heterozygosity, and repetitiveness. Here we introduce GenomeScope 2.0 (https://github.com/tbenavi1/genomescope2.0), which applies combinatorial theory to establish a detailed mathematical model of how k-mer frequencies are distributed in heterozygous and polyploid genomes. We describe and evaluate a practical implementation of the polyploid-aware mixture model that, within seconds, accurately infers genome properties across thousands of simulated and eleven real datasets spanning a broad range of complexity. We also present a new method called Smudgeplots (https://github.com/KamilSJaron/smudgeplot) to visualize and infer the ploidy and genome structure of a genome by analyzing heterozygous k-mer pairs. We successfully apply the approach to systems of known variable ploidy levels in the Meloidogyne genus and also the extreme case of octoploid Fragaria x ananassa.

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bioRxiv2019

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.

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bioRxiv2019

Samovar: Single-sample mosaic SNV calling with linked reads

We present Samovar, a mosaic single-nucleotide variant (SNV) caller for linked-read whole-genome shotgun sequencing data. Samovar scores candidate sites using a random forest model trained using the input dataset that considers read quality, phasing, and linked-read characteristics. We show Samovar calls mosaic SNVs within a single sample with accuracy comparable to what previously required trios or matched tumor/normal pairs and outperform single-sample mosaic variant callers at MAF 5%-50% with at least 30x coverage. Furthermore, we use Samovar to find somatic variants in whole genome sequencing of both tumor and normal from 13 pediatric cancer cases that can be corroborated with high recall with whole exome sequencing. Samovar is available open-source at https://github.com/cdarby/samovar under the MIT license.

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bioRxiv2019

Highly-accurate long-read sequencing improves variant detection and assembly of a human genome

The major DNA sequencing technologies in use today produce either highly-accurate short reads or noisy long reads. We developed a protocol based on single-molecule, circular consensus sequencing (CCS) to generate highly-accurate (99.8%) long reads averaging 13.5 kb and applied it to sequence the well-characterized human HG002/NA24385. We optimized existing tools to comprehensively detect variants, achieving precision and recall above 99.91% for SNVs, 95.98% for indels, and 95.99% for structural variants. We estimate that 2,434 discordances are correctable mistakes in the high-quality Genome in a Bottle benchmark. Nearly all (99.64%) variants are phased into haplotypes, which further improves variant detection. De novo assembly produces a highly contiguous and accurate genome with contig N50 above 15 Mb and concordance of 99.998%. CCS reads match short reads for small variant detection, while enabling structural variant detection and de novo assembly at similar contiguity and markedly higher concordance than noisy long reads.

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bioRxiv2019

Fast and accurate reference-guided scaffolding of draft genomes

BackgroundAs the number of new genome assemblies continues to grow, there is increasing demand for methods to coalesce contigs from draft assemblies into pseudomolecules. Most current methods use genetic maps, optical maps, chromatin conformation (Hi-C), or other long-range linking data, however these data are expensive and analysis methods often fail to accurately order and orient a high percentage of assembly contigs. Other approaches utilize alignments to a reference genome for ordering and orienting, however these tools rely on slow aligners and are not robust to repetitive contigs.\n\nResultsWe present RaGOO, an open-source reference-guided contig ordering and orienting tool that leverages the speed and sensitivity of Minimap2 to accurately achieve chromosome-scale assemblies in just minutes. With the pseudomolecules constructed, RaGOO identifies structural variants, including those spanning sequencing gaps that are not reported by alternative methods. We show that RaGOO accurately orders and orients contigs into nearly complete chromosomes based on de novo assemblies of Oxford Nanopore long-read sequencing from three wild and domesticated tomato genotypes, including the widely used M82 reference cultivar. We then demonstrate the scalability and utility of RaGOO with a pan-genome analysis of 103 Arabidopsis thaliana accessions by examining the structural variants detected in the newly assembled pseudomolecules. RaGOO is available open-source with an MIT license at https://github.com/malonge/RaGOO.\n\nConclusionsWe demonstrate that with a highly contiguous assembly and a structurally accurate reference genome, reference-guided scaffolding with RaGOO outperforms error-prone reference-free methods and enable rapid pan-genome analysis.

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bioRxiv2018

SVCollector: Optimized sample selection for validating and long-read resequencing of structural variants

SummaryStructural Variations (SVs) are increasingly recognized for their importance in genomics. Short-read sequencing is the most widely-used approach for genotyping large numbers of samples for SVs but suffers from relatively poor accuracy. Here we present SVCollector, an open-source method that optimally selects samples to maximize variant discovery and validation using long read resequencing or PCR-based validation. SVCollector has two modes: selecting those samples that are individually the most diverse or those that collectively capture the largest number of variations.\n\nAvailabilityhttps://github.com/fritzsedlazeck/SVCollector\n\nContactfritz.sedlazeck@bcm.edu\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

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bioRxiv2017

Addressing confounding artifacts in reconstruction of gene co-expression networks

BackgroundGene co-expression networks capture diverse biological relationships between genes, and are important tools in predicting gene function and understanding disease mechanisms. Functional interactions between genes have not been fully characterized for most organisms, and therefore reconstruction of gene co-expression networks has been of common interest in a variety of settings. However, methods routinely used for reconstruction of gene co-expression networks do not account for confounding artifacts known to affect high dimensional gene expression measurements.\n\nResultsIn this study, we show that artifacts such as batch effects in gene expression data confound commonly used network reconstruction algorithms. Both theoretically and empirically, we demonstrate that removing the effects of top principal components from gene expression measurements prior to network inference can reduce false discoveries, especially when well annotated technical covariates are not available. Using expression data from the GTEx project in multiple tissues and hundreds of individuals, we show that this latent factor residualization approach often reduces false discoveries in the reconstructed networks.\n\nConclusionNetwork reconstruction is susceptible to confounders that affect measurements of gene expression. Even controlling for major individual known technical covariates fails to fully eliminate confounding variation from the data. In studies where a wide range of annotated technical factors are measured and available, correcting gene expression data with multiple covariates can also improve network reconstruction, but such extensive annotations are not always available. Our study shows that principal component correction, which does not depend on study design or annotation of all relevant confounders, removes patterns of artifactual variation and improves network reconstruction in both simulated data, and gene expression data from GTEx project. We have implemented our PC correction approach in the Bioconductor package sva which can be used prior to network reconstruction with a range of methods.

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bioRxiv2017

Complex rearrangements and oncogene amplifications revealed by long-read DNA and RNA sequencing of a breast cancer cell line

The SK-BR-3 cell line is one of the most important models for HER2+ breast cancers, which affect one in five breast cancer patients. SK-BR-3 is known to be highly rearranged although much of the variation is in complex and repetitive regions that may be underreported. Addressing this, we sequenced SK-BR-3 using long-read single molecule sequencing from Pacific Biosciences, and develop one of the most detailed maps of structural variations (SVs) in a cancer genome available with nearly 20,000 variants present, most of which were missed by prior efforts. Surrounding the important HER2 locus, we discover a complex sequence of nested duplications and translocations, suggesting a punctuated progression. Full-length transcriptome sequencing further revealed several novel gene fusions within the nested genomic variants. Combining long-read genome and transcriptome sequencing enables an in-depth analysis of how SVs disrupt the transcriptome and sheds new light on the complexity of cancer progression.

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bioRxiv2017

Scikit-ribo: Accurate estimation and robust modeling of translation dynamics at codon resolution

Ribosome profiling (Riboseq) is a powerful technique for measuring protein translation, however, sampling errors and biological biases are prevalent and poorly understand. Addressing these issues, we present Scikit-ribo (https://github.com/hanfang/scikit-ribo), the first open-source software for accurate genome-wide A-site prediction and translation efficiency (TE) estimation from Riboseq and RNAseq data. Scikit-ribo accurately identifies A-site locations and reproduces codon elongation rates using several digestion protocols (r = 0.99). Next we show commonly used RPKM-derived TE estimation is prone to biases, especially for low-abundance genes. Scikit-ribo introduces a codon-level generalized linear model with ridge penalty that correctly estimates TE while accommodating variable codon elongation rates and mRNA secondary structure. This corrects the TE errors for over 2000 genes in S. cerevisiae, which we validate using mass spectrometry of protein abundances (r = 0.81) and allows us to determine the Kozak-like sequence directly from Riboseq. We conclude with an analysis of coverage requirements needed for robust codon-level analysis, and quantify the artifacts that can occur from cycloheximide treatment.

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bioRxiv2017

Reference Quality Assembly of the 3.5 Gb genome of Capsicum annuum from a Single Linked-Read Library

BackgroundLinked-Read sequencing technology has recently been employed successfully for de novo assembly of multiple human genomes, however the utility of this technology for complex plant genomes is unproven. We evaluated the technology for this purpose by sequencing the 3.5 gigabase (Gb) diploid pepper (Capsicum annuum) genome with a single Linked-Read library. Plant genomes, including pepper, are characterized by long, highly similar repetitive sequences. Accordingly, significant effort is used to ensure the sequenced plant is highly homozygous and the resulting assembly is a haploid consensus. With a phased assembly approach, we targeted a heterozygous F1 derived from a wide cross to assess the ability to derive both haplotypes for a pungency gene characterized by a large insertion/deletion.\n\nResultsThe Supernova software generated a highly ordered, more contiguous sequence assembly than all currently available C. annuum reference genomes. Eighty-four percent of the final assembly was anchored and oriented using four de novo linkage maps. A comparison of the annotation of conserved eukaryotic genes indicated the completeness of assembly. The validity of the phased assembly is further demonstrated with the complete recovery of both 2.5 kb insertion/deletion haplotypes of the PUN1 locus in the F1 sample that represents pungent and non-pungent peppers.\n\nConclusionsThe most contiguous pepper genome assembly to date has been generated through this work which demonstrates that Linked-Read library technology provides a rapid tool to assemble de novo complex highly repetitive heterozygous plant genomes. This technology can provide an opportunity to cost-effectively develop high-quality reference genome assemblies for other complex plants and compare structural and gene differences through accurate haplotype reconstruction.

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bioRxiv2017

16GT: a fast and sensitive variant caller using a 16-genotype probabilistic model

Summary16GT is a variant caller for Illumina WGS and WES germline data. It uses a new 16-genotype probabilistic model to unify SNP and indel calling in a single variant calling algorithm. In benchmark comparisons with five other widely used variant callers on a modern 36-core server, 16GT ran faster and demonstrated improved sensitivity in calling SNPs, and it provided comparable sensitivity and accuracy in calling indels as compared to the GATK HaplotypeCaller.\n\nAvailability and implementationhttps://github.com/aquaskyline/16GT\n\nContactrluo5@jhu.edu\n\nSupplementary informationSupplementary tables and notes are available at Bioinformatics online.

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bioRxiv2017

LRSim: a Linked Reads Simulator generating insights for better genome partitioning

MotivationLinked reads are a form of DNA sequencing commercialized by 10X Genomics that uses highly multiplexed barcoding within microdroplets to tag short reads to progenitor molecules. The linked reads, spanning tens to hundreds of kilobases, offer an alternative to long-read sequencing for de novo assembly, haplotype phasing and other applications. However, there is no available simulator, making it difficult to measure their capability or develop new informatics tools.\n\nResultsOur analysis of 13 real linked read datasets revealed their characteristics of barcodes, molecules and partitions. Based on this, we introduce LRSim that simulates linked reads by emulating the library preparation and sequencing process with fine control of 1) the number of simulated variants; 2) the linked-read characteristics; and 3) the Illumina reads profile. We conclude from the phasing and genome assembly of multiple datasets, recommendations on coverage, fragment length, and partitioning when sequencing human and non-human genome.\n\nAvailabilityLRSIM is under MIT license and is freely available at https://github.com/aquaskyline/LRSIM\n\nContactrluo5@jhu.edu

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bioRxiv2016

SplitThreader: Exploration and analysis of rearrangements in cancer genomes

Genomic rearrangements and associated copy number changes are important drivers in cancer as they can alter the expression of oncogenes and tumor suppressors, create gene fusions, and misregulate gene expression. Here we present SplitThreader (http://splitthreader.com), an open-source interactive web application for analysis and visualization of genomic rearrangements and copy number variation in cancer genomes. SplitThreader constructs a sequence graph of genomic rearrangements in the sample and uses a priority queue breadth-first search algorithm on the graph to search for novel interactions. This is applied to detect gene fusions and other novel sequences, as well as to evaluate distances in the rearranged genome between any genomic regions of interest, especially the repositioning of regulatory elements and their target genes. SplitThreader also analyzes each variant to categorize it by its relation to other variants and by its copy number concordance. This identifies balanced translocations, identifies simple and complex variants, and suggests likely false positives when copy number is not concordant across a candidate breakpoint. It also provides explanations when multiple variants affect the copy number state and obscure the contribution of a single variant, such as a deletion within a region that is overall amplified. Together, these categories triage the variants into groups and provide a starting point for further systematic analysis and manual curation. To demonstrate its utility, we apply SplitThreader to three cancer cell lines, MCF-7 and A549 with Illumina paired-end sequencing, and SK-BR-3, with long-read PacBio sequencing. Using SplitThreader, we examine the genomic rearrangements responsible for previously observed gene fusions in SK-BR-3 and MCF-7, and discover many of the fusions involved a complex series of multiple genomic rearrangements. We also find notable differences in the types of variants between the three cell lines, in particular a much higher proportion of reciprocal variants in SK-BR-3 and a distinct clustering of interchromosomal variants in SK-BR-3 and MCF-7 that is absent in A549.

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bioRxiv2016

Ribbon: Visualizing complex genome alignments and structural variation

To the EditorVisualization has played an extremely important role in the current genomic revolution to inspect and understand variants, expression patterns, evolutionary changes, and a number of other relationships1-3. However, most of the information in read-to-reference or genome-genome alignments is lost for structural variations in the one-dimensional views of most genome browsers showing only reference coordinates. Instead, structural variations captured by long reads or assembled contigs often need more context to understand, including alignments and other genomic information from multiple chromosomes.

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