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Opportunities and challenges in long-read sequencing data analysis
Open accessAbstract
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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“Conventional single-cell transcriptomics assays capture information at the gene level, thereby missing much of the biological diversity and regulatory mechanisms that occurs at the isoform level.30”
From hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data (2023) · cites this paper as [30]
Experimental — accuracy not yet measuredIn the citing paper
“Although there exist batch-effect correction methods, it's still challenging to distinguish true biological diversity from technical differences and thus preserve important biological variations 13 .”
From scBERT as a Large-scale Pretrained Deep Language Model for Cell Type Annotation of Single-cell RNA-seq Data (2021) · cites this paper as [13]
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Cites
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106
Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp616 · Cited by 41,364
limma powers differential expression analyses for RNA-sequencing and microarray studies
Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv007 · Cited by 40,458
Self-citationfeatureCounts: an efficient general purpose program for assigning sequence reads to genomic features
Liao, Y., Smyth, G. K., & Shi, W. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btt656 · Cited by 27,219
Salmon provides fast and bias-aware quantification of transcript expression
Patro, R., Duggal, G., Love, M. I., Irizarry, R. A., & Kingsford, C. (2017) · Nature Methods · doi:10.1038/nmeth.4197 · Cited by 12,700
Walker, B. J., Abeel, T., Shea, T., Priest, M., Abouelliel, A., Sakthikumar, S., Cuomo, C. A., Zeng, Q., Wortman, J. R., Young, S., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0112963 · Cited by 9,130
Unicycler: Resolving bacterial genome assemblies from short and long sequencing reads
Wick, R. R., Judd, L. M., Gorrie, C. L., & Holt, K. E. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005595 · Cited by 8,728
Canu: scalable and accurate long-read assembly via adaptive k -mer weighting and repeat separationPreprint
Koren, S., Walenz, B. P., Berlin, K., Miller, J. R., Bergman, N. H., & Phillippy, A. M. (2017) · Genome Research · doi:10.1101/gr.215087.116 · Cited by 7,260
Assembly of long, error-prone reads using repeat graphs
Kolmogorov, M., Yuan, J., Lin, Y., & Pevzner, P. A. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0072-8 · Cited by 5,986
McCarthy, D. J., Chen, Y., & Smyth, G. K. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks042 · Cited by 5,195
Research works printed but not linked: 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
- Venue
- Genome Biology
- Published
- 2020-02-07
- Volume
- 21
- Issue
- 1
- Pages
- 30-30
- ISSN
- 1465-6906 (ISSN-L)
- 1474-760X
- 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
- 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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