自引率: 5.1%
被引量: 149362
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审稿周期: 2.89
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国人发稿量: 36
投稿须知/期刊简介:
The journal aims to publish high quality peer-reviewed original scientific papers and excellent review articles in the fields of computational molecular biology biological databases and genome bioinformatics.
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JTK: targeted diploid genome assembler.
Diploid assembly, or determining sequences of homologous chromosomes separately, is essential to elucidate genetic differences between haplotypes. One approach is to call and phase single nucleotide variants (SNVs) on a reference sequence. However, this approach becomes unstable on large segmental duplications (SDs) or structural variations (SVs) because the alignments of reads deriving from these regions tend to be unreliable. Another approach is to use highly accurate PacBio HiFi reads to output diploid assembly directly. Nonetheless, HiFi reads cannot phase homozygous regions longer than their length and require oxford nanopore technology (ONT) reads or Hi-C to produce a fully phased assembly. Is a single long-read sequencing technology sufficient to create an accurate diploid assembly? Here, we present JTK, a megabase-scale diploid genome assembler. It first randomly samples kilobase-scale sequences (called 'chunks') from the long reads, phases variants found on them, and produces two haplotypes. The novel idea of JTK is to utilize chunks to capture SNVs and SVs simultaneously. From 60-fold ONT reads on the HG002 and a Japanese sample, it fully assembled two haplotypes with approximately 99.9% accuracy on the histocompatibility complex (MHC) and the leukocyte receptor complex (LRC) regions, which was impossible by the reference-based approach. In addition, in the LRC region on a Japanese sample, JTK output an assembly of better contiguity than those built from high-coverage HiFi+Hi-C. In the coming age of pan-genomics, JTK would complement the reference-based phasing method to assemble the difficult-to-assemble but medically important regions. JTK is available at https://github.com/ban-m/jtk, and the datasets are available at https://doi.org/10.5281/zenodo.7790310 or JGAS000580 in DDBJ.
被引量：1 发表：2023
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Accurate haplotype-resolved assembly reveals the origin of structural variants for human trios.
Achieving a near complete understanding of how the genome of an individual affects the phenotypes of that individual requires deciphering the order of variations along homologous chromosomes in species with diploid genomes. However, true diploid assembly of long-range haplotypes remains challenging. To address this, we have developed Haplotype-resolved Assembly for Synthetic long reads using a Trio-binning strategy, or HAST, which uses parental information to classify reads into maternal or paternal. Once sorted, these reads are used to independently de novo assemble the parent-specific haplotypes. We applied HAST to cobarcoded second-generation sequencing data from an Asian individual, resulting in a haplotype assembly covering 94.7% of the reference genome with a scaffold N50 longer than 11 Mb. The high haplotyping precision (∼99.7%) and recall (∼95.9%) represents a substantial improvement over the commonly used tool for assembling cobarcoded reads (Supernova), and is comparable to a trio-binning-based third generation long-read-based assembly method (TrioCanu) but with a significantly higher single-base accuracy [up to 99.99997% (Q65)]. This makes HAST a superior tool for accurate haplotyping and future haplotype-based studies. The code of the analysis is available at https://github.com/BGI-Qingdao/HAST. Supplementary data are available at Bioinformatics online.
被引量：- 发表：2021
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HiC-Hiker: a probabilistic model to determine contig orientation in chromosome-length scaffolds with Hi-C.
De novo assembly of reference-quality genomes used to require enormously laborious tasks. In particular, it is extremely time-consuming to build genome markers for ordering assembled contigs along chromosomes; thus, they are only available for well-established model organisms. To resolve this issue, recent studies demonstrated that Hi-C could be a powerful and cost-effective means to output chromosome-length scaffolds for non-model species with no genome marker resources, because the Hi-C contact frequency between a pair of two loci can be a good estimator of their genomic distance, even if there is a large gap between them. Indeed, state-of-the-art methods such as 3D-DNA are now widely used for locating contigs in chromosomes. However, it remains challenging to reduce errors in contig orientation because shorter contigs have fewer contacts with their neighboring contigs. These orientation errors lower the accuracy of gene prediction, read alignment, and synteny block estimation in comparative genomics. To reduce these contig orientation errors, we propose a new algorithm, named HiC-Hiker, which has a firm grounding in probabilistic theory, rigorously models Hi-C contacts across contigs, and effectively infers the most probable orientations via the Viterbi algorithm. We compared HiC-Hiker and 3D-DNA using human and worm genome contigs generated from short reads, evaluated their performances, and observed a remarkable reduction in the contig orientation error rate from 4.3% (3D-DNA) to 1.7% (HiC-Hiker). Our algorithm can consider long-range information between distal contigs and precisely estimates Hi-C read contact probabilities among contigs, which may also be useful for determining the ordering of contigs. HiC-Hiker is freely available at: https://github.com/ryought/hic_hiker.
被引量：6 发表：2020
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Identifying and removing haplotypic duplication in primary genome assemblies.
Rapid development in long-read sequencing and scaffolding technologies is accelerating the production of reference-quality assemblies for large eukaryotic genomes. However, haplotype divergence in regions of high heterozygosity often results in assemblers creating two copies rather than one copy of a region, leading to breaks in contiguity and compromising downstream steps such as gene annotation. Several tools have been developed to resolve this problem. However, they either focus only on removing contained duplicate regions, also known as haplotigs, or fail to use all the relevant information and hence make errors. Here we present a novel tool, purge_dups, that uses sequence similarity and read depth to automatically identify and remove both haplotigs and heterozygous overlaps. In comparison with current tools, we demonstrate that purge_dups can reduce heterozygous duplication and increase assembly continuity while maintaining completeness of the primary assembly. Moreover, purge_dups is fully automatic and can easily be integrated into assembly pipelines. The source code is written in C and is available at https://github.com/dfguan/purge_dups. Supplementary data are available at Bioinformatics online.
被引量：1000 发表：2020
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A haplotype-aware de novo assembly of related individuals using pedigree sequence graph.
Reconstructing high-quality haplotype-resolved assemblies for related individuals has important applications in Mendelian diseases and population genomics. Through major genomics sequencing efforts such as the Personal Genome Project, the Vertebrate Genome Project (VGP) and the Genome in a Bottle project (GIAB), a variety of sequencing datasets from trios of diploid genomes are becoming available. Current trio assembly approaches are not designed to incorporate long- and short-read data from mother-father-child trios, and therefore require relatively high coverages of costly long-read data to produce high-quality assemblies. Thus, building a trio-aware assembler capable of producing accurate and chromosomal-scale diploid genomes of all individuals in a pedigree, while being cost-effective in terms of sequencing costs, is a pressing need of the genomics community. We present a novel pedigree sequence graph based approach to diploid assembly using accurate Illumina data and long-read Pacific Biosciences (PacBio) data from all related individuals, thereby generalizing our previous work on single individuals. We demonstrate the effectiveness of our pedigree approach on a simulated trio of pseudo-diploid yeast genomes with different heterozygosity rates, and real data from human chromosome. We show that we require as little as 30× coverage Illumina data and 15× PacBio data from each individual in a trio to generate chromosomal-scale phased assemblies. Additionally, we show that we can detect and phase variants from generated phased assemblies. https://github.com/shilpagarg/WHdenovo.
被引量：16 发表：2020
