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Cited by 2,530Genomics and Phylogenetic StudiesSingle-cell and spatial transcriptomicsGenomic variations and chromosomal abnormalities

The DOI matches its Crossref record; 169 of the 169 references it cites were found in a registry or an index.

Opportunities and challenges in long-read sequencing data analysis

Open access
Amarasinghe, Shanika L., Su, Shian, Dong, Xueyi, Zappia, Luke, Ritchie, Matthew E., Gouil, Quentin·2020·Genome Biology
DOI 10.1186/s13059-020-1935-5PMC PMC7006217
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Abstract

Long-read technologies are overcoming early limitations in accuracy and throughput, broadening their application domains in genomics. Dedicated analysis tools that take into account the characteristics of long-read data are thus required, but the fast pace of development of such tools can be overwhelming. To assist in the design and analysis of long-read sequencing projects, we review the current landscape of available tools and present an online interactive database, long-read-tools.org, to facilitate their browsing. We further focus on the principles of error correction, base modification detection, and long-read transcriptomics analysis and highlight the challenges that remain.

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Crossref · 10.1186/s13059-020-1935-5

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Fig. 1 Overview of long-read analysis tools and pipelines. a Release of tools identified from various sources and milestones of long-read sequencing. b Functional categories. c Typical long-read analysis pipelines for SMRT and nanopore data. Six main stages are identified through the presented workflow (i.e. basecalling, quality control, read error correction, assembly/alignment, assembly refinement, and downstream analyses). The green-coloured boxes represent processes common to both short-read and long-read analyses. The orange-coloured boxes represent the processes unique to long-read analyses. Unfilled boxes represent optional steps. Commonly used tools for each step in long-read analysis are within brackets. Italics signify tools developed by either PacBio or ONT companies, and non-italics signify tools developed by external parties. Arrows represent the direction of the workflow
Fig. 1 Overview of long-read analysis tools and pipelines. a Release of tools identified from various sources and milestones of long-read sequencing. b Functional categories. c Typical long-read analysis pipelines for SMRT and nanopore data. Six main stages are identified through the presented workflow (i.e. basecalling, quality control, read error correction, assembly/alignment, assembly refinement, and downstream analyses). The green-coloured boxes represent processes common to both short-read and long-read analyses. The orange-coloured boxes represent the processes unique to long-read analyses. Unfilled boxes represent optional steps. Commonly used tools for each step in long-read analysis are within brackets. Italics signify tools developed by either PacBio or ONT companies, and non-italics signify tools developed by external parties. Arrows represent the direction of the workflowCC BY · PubMed Central
Fig. 2 Paradigms of error correction (a) and polishing (b). Errors in long reads and assembly are denoted by red crosses. Non-hybrid methods only require long reads, while hybrid methods additionally require accurate short reads (purple)
Fig. 2 Paradigms of error correction (a) and polishing (b). Errors in long reads and assembly are denoted by red crosses. Non-hybrid methods only require long reads, while hybrid methods additionally require accurate short reads (purple)CC BY · PubMed Central
Fig. 3 Methods to detect base modifications in long-read sequencing. Base modifications can be inferred from their effect on the current intensity (nanopore) and inter-pulse duration (IPD, SMRT). Strategies to call base modifications in nanopore sequencing and the corresponding tools are further depicted
Fig. 3 Methods to detect base modifications in long-read sequencing. Base modifications can be inferred from their effect on the current intensity (nanopore) and inter-pulse duration (IPD, SMRT). Strategies to call base modifications in nanopore sequencing and the corresponding tools are further depictedCC BY · PubMed Central
Fig. 4 Types of transcriptomic analyses and their steps. The choice of sequencing protocol amongst the six available workflows affects the type, characteristics, and quantity of data generated. Only direct RNA sequencing allows epitranscriptomic studies, but SMRT direct RNA sequencing is a custom technique that is not fully supported. The remaining non-exclusive applications are isoform detection, quantification, and differential analysis. The dashed lines in arrows represent upstream processes to transcriptomics
Fig. 4 Types of transcriptomic analyses and their steps. The choice of sequencing protocol amongst the six available workflows affects the type, characteristics, and quantity of data generated. Only direct RNA sequencing allows epitranscriptomic studies, but SMRT direct RNA sequencing is a custom technique that is not fully supported. The remaining non-exclusive applications are isoform detection, quantification, and differential analysis. The dashed lines in arrows represent upstream processes to transcriptomicsCC BY · PubMed Central

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Research works printed but not linked: 1

  1. Oxford Nanopore Technologies. Clive Brown’s keynote at Nanopore Community Meeting 2018. 2018. https://nanoporetech.com/resource-centre/clive-brown-ncm-2018. Accessed 6 June 2019.

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Metadata

Authors
Amarasinghe, S. L., Su, S., Dong, X., Zappia, L., Ritchie, M. E., & Gouil, Q.
Affiliations
The University of Melbourne (AU)
Corresponding author
Gouil, Quentin
ORCID
Venue
Genome Biology
Publisher
Springer Science and Business Media LLC
Published
2020-02-07
Volume
21
Issue
1
Pages
30-30
ISSN
  • 1465-6906 (ISSN-L)
  • 1474-760X
DOI
10.1186/s13059-020-1935-5
PMC
PMC7006217
OpenAlex
W3007172120
Cited by
2,530
References
167
Citations by year
2026: 253 · 2025: 439 · 2024: 498 · 2023: 437 · 2022: 441 · 2021: 356 · 2020: 100
FWCI
32.42
Type
journal article
Language
en
Open access
Licence
Locations
  • Genome biology (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Minerva Access (University of Melbourne) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomic variations and chromosomal abnormalities
Keywords
long-read sequencing, error correction, sequencing accuracy, sequencing throughput
MeSH terms
Whole Genome Sequencing, Data Science, Nanopore Sequencing, Animals, Humans, Genomics

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