Improving PacBio Long Read Accuracy by Short Read Alignment
Open accessAbstract
The recent development of third generation sequencing (TGS) generates much longer reads than second generation sequencing (SGS) and thus provides a chance to solve problems that are difficult to study through SGS alone. However, higher raw read error rates are an intrinsic drawback in most TGS technologies. Here we present a computational method, LSC, to perform error correction of TGS long reads (LR) by SGS short reads (SR). Aiming to reduce the error rate in homopolymer runs in the main TGS platform, the PacBio® RS, LSC applies a homopolymer compression (HC) transformation strategy to increase the sensitivity of SR-LR alignment without scarifying alignment accuracy. We applied LSC to 100,000 PacBio long reads from human brain cerebellum RNA-seq data and 64 million single-end 75 bp reads from human brain RNA-seq data. The results show LSC can correct PacBio long reads to reduce the error rate by more than 3 folds. The improved accuracy greatly benefits many downstream analyses, such as directional gene isoform detection in RNA-seq study. Compared with another hybrid correction tool, LSC can achieve over double the sensitivity and similar specificity.
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Long-read sequencing has limitations, including a still high error rate that limits de novo transcript identification and forces reliance on the reference genome.
“Nevertheless, long-read sequencing has its own set of limitations, such as a still high error rate that limits de novo transcript identifications and forces the technology to leverage the reference genome [188].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [188]
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References 8 · Cited by 335
- Authors
- Au, K. F., Underwood, J. G., Lee, L., & Wong, W. H.
- Affiliations
- Stanford University (US)
- Pacific Biosciences (United States) (US)
- Corresponding author
- Wong, Wing Hung
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2012-10-04
- Volume
- 7
- Issue
- 10
- Pages
- e46679
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC3464235
- OpenAlex
- W2041126213
- Cited by
- 335
- References
- 8
- Citations by year
- 2026: 1 · 2025: 8 · 2024: 9 · 2023: 16 · 2022: 30 · 2021: 36 · 2020: 35 · 2019: 36 · 2018: 25 · 2017: 37 · 2016: 49 · 2015: 21 · 2014: 16 · 2013: 14 · 2012: 2
- FWCI
- 6.81
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Funders
- National Institutes of Health
- National Human Genome Research Institute (R01HG005717)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD057970)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Physical Sciences › Computer Science › Artificial Intelligence › Algorithms and Data Compression
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Keywords
- PacBio long reads, long-read error correction, hybrid sequencing, short-read alignment, long-read sequencing, RNA-seq, leukemia stem cells
- MeSH terms
- Software, Reproducibility of Results, Sequence Analysis, DNA, Sequence Analysis, RNA, Computational Biology
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