Literature DB >> 32449778

CSA: A high-throughput chromosome-scale assembly pipeline for vertebrate genomes.

Heiner Kuhl1, Ling Li1,2, Sven Wuertz1, Matthias Stöck1, Xu-Fang Liang2, Christophe Klopp3.   

Abstract

BACKGROUND: Easy-to-use and fast bioinformatics pipelines for long-read assembly that go beyond the contig level to generate highly continuous chromosome-scale genomes from raw data remain scarce. RESULT: Chromosome-Scale Assembler (CSA) is a novel computationally highly efficient bioinformatics pipeline that fills this gap. CSA integrates information from scaffolded assemblies (e.g., Hi-C or 10X Genomics) or even from diverged reference genomes into the assembly process. As CSA performs automated assembly of chromosome-sized scaffolds, we benchmark its performance against state-of-the-art reference genomes, i.e., conventionally built in a laborious fashion using multiple separate assembly tools and manual curation. CSA increases the contig lengths using scaffolding, local re-assembly, and gap closing. On certain datasets, initial contig N50 may be increased up to 4.5-fold. For smaller vertebrate genomes, chromosome-scale assemblies can be achieved within 12 h using low-cost, high-end desktop computers. Mammalian genomes can be processed within 16 h on compute-servers. Using diverged reference genomes for fish, birds, and mammals, we demonstrate that CSA calculates chromosome-scale assemblies from long-read data and genome comparisons alone. Even contig-level draft assemblies of diverged genomes are helpful for reconstructing chromosome-scale sequences. CSA is also capable of assembling ultra-long reads.
CONCLUSIONS: CSA can speed up and simplify chromosome-level assembly and significantly lower costs of large-scale family-level vertebrate genome projects.
© The Author(s) 2020. Published by Oxford University Press.

Entities:  

Keywords:  chromosomes; comparative genomics; genome assembly; genome evolution; genome scaffolding; long-read; vertebrates

Year:  2020        PMID: 32449778      PMCID: PMC7247394          DOI: 10.1093/gigascience/giaa034

Source DB:  PubMed          Journal:  Gigascience        ISSN: 2047-217X            Impact factor:   6.524


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