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Asset Tag: bioinformatics

April 21, 2020

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, a fast and accurate genotyper that models SVs using sequence graphs and SV annotations produced by a range of methods and technologies. We demonstrate the accuracy of Paragraph on whole genome sequence data from a control sample with both short and long read sequencing data available, and then apply it at scale to a cohort of 100 samples of diverse ancestry sequenced with short-reads. Comparative analyses indicate that Paragraph has better accuracy than other existing genotypers. The Paragraph software is open-source and available at ?https://github.com/Illumina/paragraph

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April 21, 2020

Quantifying the Benefit Offered by Transcript Assembly on Single-Molecule Long Reads

Third-generation sequencing technologies benefit transcriptome analysis by generating longer sequencing reads. However, not all single-molecule long reads represent full transcripts due to incomplete cDNA synthesis and the sequencing length limit of the platform. This drives a need for long read transcript assembly. We quantify the benefit that can be achieved by using a transcript assembler on long reads. Adding long-read-specific algorithms, we evolved Scallop to make Scallop-LR, a long-read transcript assembler, to handle the computational challenges arising from long read lengths and high error rates. Analyzing 26 SRA PacBio datasets using Scallop-LR, Iso-Seq Analysis, and StringTie, we quantified the amount by which assembly improved Iso-Seq results. Through combined evaluation methods, we found that Scallop-LR identifies 2100–4000 more (for 18 human datasets) or 1100–2200 more (for eight mouse datasets) known transcripts than Iso-Seq Analysis, which does not do assembly. Further, Scallop-LR finds 2.4–4.4 times more potentially novel isoforms than Iso-Seq Analysis for the human and mouse datasets. StringTie also identifies more transcripts than Iso-Seq Analysis. Adding long-read-specific optimizations in Scallop-LR increases the numbers of predicted known transcripts and potentially novel isoforms for the human transcriptome compared to several recent short-read assemblers (e.g. StringTie). Our findings indicate that transcript assembly by Scallop-LR can reveal a more complete human transcriptome.

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April 21, 2020

Loss-of-function tolerance of enhancers in the human genome

Previous studies have surveyed the potential impact of loss-of-function (LoF) variants and identified LoF-tolerant protein-coding genes. However, the tolerance of human genomes to losing enhancers has not yet been evaluated. Here we present the catalog of LoF-tolerant enhancers using structural variants from whole-genome sequences. Using a conservative approach, we estimate that each individual human genome possesses at least 28 LoF-tolerant enhancers on average. We assessed the properties of LoF-tolerant enhancers in a unified regulatory network constructed by integrating tissue-specific enhancers and gene-gene interactions. We find that LoF-tolerant enhancers are more tissue-specific and regulate fewer and more dispensable genes. They are enriched in immune-related cells while LoF-intolerant enhancers are enriched in kidney and brain/neuronal stem cells. We developed a supervised learning approach to predict the LoF- tolerance of enhancers, which achieved an AUROC of 96%. We predict 5,677 more enhancers would be likely tolerant to LoF and 75 enhancers that would be highly LoF-intolerant. Our predictions are supported by known set of disease enhancers and novel deletions from PacBio sequencing. The LoF-tolerance scores provided here will serve as an important reference for disease studies.

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April 21, 2020

metaFlye: scalable long-read metagenome assembly using repeat graphs

Long-read sequencing technologies substantially improved assemblies of many isolate bacterial genomes as compared to fragmented assemblies produced with short-read technologies. However, assembling complex metagenomic datasets remains a challenge even for the state-of-the-art long-read assemblers. To address this gap, we present the metaFlye assembler and demonstrate that it generates highly contiguous and accurate metagenome assemblies. In contrast to short-read metagenomics assemblers that typically fail to reconstruct full-length 16S RNA genes, metaFlye captures many 16S RNA genes within long contigs, thus providing new opportunities for analyzing the microbial “dark matter of life”. We also demonstrate that long-read metagenome assemblers significantly improve full-length plasmid and virus reconstruction as compared to short-read assemblers and reveal many novel plasmids and viruses.

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April 21, 2020

Benchmarking Transposable Element Annotation Methods for Creation of a Streamlined, Comprehensive Pipeline

Sequencing technology and assembly algorithms have matured to the point that high-quality de novo assembly is possible for large, repetitive genomes. Current assemblies traverse transposable elements (TEs) and allow for annotation of TEs. There are numerous methods for each class of elements with unknown relative performance metrics. We benchmarked existing programs based on a curated library of rice TEs. Using the most robust programs, we created a comprehensive pipeline called Extensive de-novo TE Annotator (EDTA) that produces a condensed TE library for annotations of structurally intact and fragmented elements. EDTA is open-source and freely available: https://github.com/oushujun/EDTA.List of abbreviationsTETransposable ElementsLTRLong Terminal RepeatLINELong Interspersed Nuclear ElementSINEShort Interspersed Nuclear ElementMITEMiniature Inverted Transposable ElementTIRTerminal Inverted RepeatTSDTarget Site DuplicationTPTrue PositivesFPFalse PositivesTNTrue NegativeFNFalse NegativesGRFGeneric Repeat FinderEDTAExtensive de-novo TE Annotator

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April 21, 2020

deSALT: fast and accurate long transcriptomic read alignment with de Bruijn graph-based index

Long-read RNA sequencing (RNA-seq) is promising to transcriptomics studies, however, the alignment of the reads is still a fundamental but non-trivial task due to the sequencing errors and complicated gene structures. We propose deSALT, a tailored two-pass long RNA-seq read alignment approach, which constructs graph-based alignment skeletons to sensitively infer exons, and use them to generate spliced reference sequence to produce refined alignments. deSALT addresses several difficult issues, such as small exons, serious sequencing errors and consensus spliced alignment. Benchmarks demonstrate that this approach has a better ability to produce high-quality full-length alignments, which has enormous potentials to transcriptomics studies.

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April 21, 2020

Extended haplotype phasing of de novo genome assemblies with FALCON-Phase

Haplotype-resolved genome assemblies are important for understanding how combinations of variants impact phenotypes. These assemblies can be created in various ways, such as use of tissues that contain single-haplotype (haploid) genomes, or by co-sequencing of parental genomes, but these approaches can be impractical in many situations. We present FALCON-Phase, which integrates long-read sequencing data and ultra-long-range Hi-C chromatin interaction data of a diploid individual to create high-quality, phased diploid genome assemblies. The method was evaluated by application to three datasets, including human, cattle, and zebra finch, for which high-quality, fully haplotype resolved assemblies were available for benchmarking. Phasing algorithm accuracy was affected by heterozygosity of the individual sequenced, with higher accuracy for cattle and zebra finch (>97%) compared to human (82%). In addition, scaffolding with the same Hi-C chromatin contact data resulted in phased chromosome-scale scaffolds.

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April 21, 2020

HLA*LA – HLA typing from linearly projected graph alignments.

HLA*LA implements a new graph alignment model for HLA type inference, based on the projection of linear alignments onto a variation graph. It enables accurate HLA type inference from whole-genome (99% accuracy) and whole-exome (93% accuracy) Illumina data; from long-read Oxford Nanopore and Pacific Biosciences data (98% accuracy for whole-genome and targeted data); and from genome assemblies. Computational requirements for a typical sample vary between 0.7 and 14 CPU hours per sample.HLA*LA is implemented in C?++ and Perl and freely available as a bioconda package or from https://github.com/DiltheyLab/HLA-LA (GPL v3).Supplementary data are available online. © The Author(s) 2019. Published by Oxford University Press.

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April 21, 2020

Graph analysis of fragmented long-read bacterial genome assemblies.

Long-read genome assembly tools are expected to reconstruct bacterial genomes nearly perfectly, however they still produce fragmented assemblies in some cases. It would be beneficial to understand whether these cases are intrinsically impossible to resolve, or if assemblers are at fault, implying that genomes could be refined or even finished with little to no additional experimental cost.We propose a set of computational techniques to assist inspection of fragmented bacterial genome assemblies, through careful analysis of assembly graphs. By finding paths of overlapping raw reads between pairs of contigs, we recover potential short-range connections between contigs that were lost during the assembly process. We show that our procedure recovers 45% of missing contig adjacencies in fragmented Canu assemblies, on samples from the NCTC bacterial sequencing project. We also observe that a simple procedure based on enumerating weighted Hamiltonian cycles can suggest likely contig orderings. In our tests, the correct contig order is ranked first in half of the cases and within the top-3 predictions in nearly all evaluated cases, providing a direction for finishing fragmented long-read assemblies.https://gitlab.inria.fr/pmarijon/knot.Supplementary data are available at Bioinformatics online. © The Author(s) (2019). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

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April 21, 2020

SyRI: identification of syntenic and rearranged regions from whole-genome assemblies

We present SyRI, an efficient tool for genome-wide identification of structural rearrangements (SR) from genome graphs, which are built up from pair-wise whole-genome alignments. Instead of searching for differences, SyRI starts by finding all co-linear regions between the genomes. As all remaining regions are SRs by definition, they can be classified as inversions, translocations, or duplications based on their positions in convoluted networks of repetitive alignments. Finally, SyRI reports local variations like SNPs and indels within syntenic and rearranged regions. We show SyRItextquoterights broad applicability to multiple species and genetically validate the presence of ~100 translocations identified in Arabidopsis.

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April 21, 2020

The radish genome database (RadishGD): an integrated information resource for radish genomics.

Radish (Raphanus sativus L.) is an important root vegetable crop in the family Brassicaceae, which provides diverse nutrients for human health and is closely related to the Brassica crop species. Recently, we sequenced and assembled the radish genome into nine chromosome pseudomolecules. In addition, we developed diverse genomic resources, including genetic maps, molecular markers, transcriptome, genome-wide methylation and variome data. In this study, we describe the radish genome database (RadishGD), including details of data sets that we generated and the web interface that allows access to these data. RadishGD comprises six major units that enable researchers and general users to search, browse and analyze the radish genomic data in an integrated manner. The Search unit provides gene structures and sequences for gene models through keyword or BLAST searches. The Genome browser displays graphic representations of gene models, mRNAs, repetitive sequences, genome-wide methylation and variomes among various genotypes. The Functional annotation unit offers gene ontology, plant ontology, pathway and gene family information for gene models. The Genetic map unit provides information about markers and their genetic locations using two types of genetic maps. The Expression unit presents transcriptional characteristics and methylation levels for each gene in 18 tissues. All sequence data incorporated into RadishGD can be downloaded from the Data resources unit. RadishGD will be continually updated to serve as a community resource for radish genomics and breeding research.

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April 21, 2020

A comprehensive evaluation of long read error correction methods

Motivation: Third-generation sequencing technologies can sequence long reads, which is advancing the frontiers of genomics research. However, their high error rates prohibit accurate and efficient downstream analysis. This difficulty has motivated the development of many long read error correction tools, which tackle this problem through sampling redundancy and/or leveraging accurate short reads of the same biological samples. Existing studies to asses these tools use simulated data sets, and are not sufficiently comprehensive in the range of software covered or diversity of evaluation measures used. Results: In this paper, we present a categorization and review of long read error correction methods, and provide a comprehensive evaluation of the corresponding long read error correction tools. Leveraging recent real sequencing data, we establish benchmark data sets and set up evaluation criteria for a comparative assessment which includes quality of error correction as well as run-time and memory usage. We study how trimming and long read sequencing depth affect error correction in terms of length distribution and genome coverage post-correction, and the impact of error correction performance on an important application of long reads, genome assembly. We provide guidelines for practitioners for choosing among the available error correction tools and identify directions for future research.

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April 21, 2020

Fast and accurate long-read assembly with wtdbg2

Existing long-read assemblers require tens of thousands of CPU hours to assemble a human genome and are being outpaced by sequencing technologies in terms of both throughput and cost. We developed a novel long-read assembler wtdbg2 that, for human data, is tens of times faster than published tools while achieving comparable contiguity and accuracy. It represents a significant algorithmic advance and paves the way for population-scale long-read assembly in future.

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April 21, 2020

Uncovering Missing Heritability in Rare Diseases.

The problem of ‘missing heritability’ affects both common and rare diseases hindering: discovery, diagnosis, and patient care. The ‘missing heritability’ concept has been mainly associated with common and complex diseases where promising modern technological advances, like genome-wide association studies (GWAS), were unable to uncover the complete genetic mechanism of the disease/trait. Although rare diseases (RDs) have low prevalence individually, collectively they are common. Furthermore, multi-level genetic and phenotypic complexity when combined with the individual rarity of these conditions poses an important challenge in the quest to identify causative genetic changes in RD patients. In recent years, high throughput sequencing has accelerated discovery and diagnosis in RDs. However, despite the several-fold increase (from ~10% using traditional to ~40% using genome-wide genetic testing) in finding genetic causes of these diseases in RD patients, as is the case in common diseases-the majority of RDs are also facing the ‘missing heritability’ problem. This review outlines the key role of high throughput sequencing in uncovering genetics behind RDs, with a particular focus on genome sequencing. We review current advances and challenges of sequencing technologies, bioinformatics approaches, and resources.

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April 21, 2020

A critical comparison of technologies for a plant genome sequencing project.

A high-quality genome sequence of any model organism is an essential starting point for genetic and other studies. Older clone-based methods are slow and expensive, whereas faster, cheaper short-read-only assemblies can be incomplete and highly fragmented, which minimizes their usefulness. The last few years have seen the introduction of many new technologies for genome assembly. These new technologies and associated new algorithms are typically benchmarked on microbial genomes or, if they scale appropriately, on larger (e.g., human) genomes. However, plant genomes can be much more repetitive and larger than the human genome, and plant biochemistry often makes obtaining high-quality DNA that is free from contaminants difficult. Reflecting their challenging nature, we observe that plant genome assembly statistics are typically poorer than for vertebrates.Here, we compare Illumina short read, Pacific Biosciences long read, 10x Genomics linked reads, Dovetail Hi-C, and BioNano Genomics optical maps, singly and combined, in producing high-quality long-range genome assemblies of the potato species Solanum verrucosum. We benchmark the assemblies for completeness and accuracy, as well as DNA compute requirements and sequencing costs.The field of genome sequencing and assembly is reaching maturity, and the differences we observe between assemblies are surprisingly small. We expect that our results will be helpful to other genome projects, and that these datasets will be used in benchmarking by assembly algorithm developers. © The Author(s) 2019. Published by Oxford University Press.

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