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

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Opportunities and challenges in long-read sequencing data analysis

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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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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)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 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 transcriptomicsCC BY · PubMed Central

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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: Amarasinghe, Shanika L.
Su, Shian
Dong, Xueyi
Zappia, Luke
Ritchie, Matthew E.
Gouil, Quentin
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: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0 (tdm)
creativecommons.org/licenses/by/4.0 (vor)
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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Opportunities and challenges in long-read sequencing data analysis

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