JOURNAL ARTICLE

Benchmarking multi-platform sequencing technologies for human genome assembly.

  • Published In: Briefings in Bioinformatics, 2023, v. 24, n. 5. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Wang, Jingjing; Veldsman, Werner Pieter; Fang, Xiaodong; Huang, Yufen; Xie, Xuefeng; Lyu, Aiping; Zhang, Lu 3 of 3

Abstract

The article focuses on benchmarking sequencing technologies and assembly tools for human genome assembly using two publicly available Genome in a Bottle (GIAB) samples, NA12878 and NA24385. It evaluates contig assembly, polishing, scaffolding, and diploid assembly across multiple platforms including Oxford Nanopore Technologies (ONT) long-reads, PacBio Continuous Long Reads (CLR), PacBio High-Fidelity (HiFi) long-reads, Illumina short-reads, 10× Genomics linked-reads, Bionano optical maps, Hi-C, and Strand-seq data. The study finds that PacBio HiFi long-reads produce assemblies with the lowest base error rates, while ONT long-reads yield the most continuous contigs but require additional polishing, preferably with short-reads, to improve accuracy. For scaffolding, Hi-C data combined with tools like 3D-DNA provides the best chromosome-level assembly, and for diploid assembly, the hifiasm tool using PacBio HiFi and Hi-C data outperforms others in phasing and variant calling. The authors recommend integrating multiple sequencing technologies and appropriate tools to optimize assembly quality, continuity, and completeness, while noting that further advancements are expected to enhance chromosome-level, haplotype-resolved human genome assemblies.

Additional Information

  • Source:Briefings in Bioinformatics. 2023/09, Vol. 24, Issue 5, p1
  • Document Type:Article
  • Subject Area:Health and Medicine
  • Publication Date:2023
  • ISSN:1467-5463
  • DOI:10.1093/bib/bbad300
  • Accession Number:172331620
  • Copyright Statement:Copyright of Briefings in Bioinformatics is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.