Computational methods for transcriptome annotation and quantification using RNA-seq
Abstract
High-throughput RNA sequencing (RNA-seq) promises a comprehensive picture of the transcriptome, allowing for the complete annotation and quantification of all genes and their isoforms across samples. Realizing this promise requires increasingly complex computational methods. These computational challenges fall into three main categories: (i) read mapping, (ii) transcriptome reconstruction and (iii) expression quantification. Here we explain the major conceptual and practical challenges, and the general classes of solutions for each category. Finally, we highlight the interdependence between these categories and discuss the benefits for different biological applications.
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Paired-end reads are preferable to single-end reads for de novo transcript discovery or isoform expression analysis.
“Furthermore, sequencing can involve single-end (SE) or paired-end (PE) reads, although the latter is preferable for de novo transcript discovery or isoform expression analysis [4, 5].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [5]
Longer reads improve mappability and transcript identification.
“Similarly, longer reads improve mappability and transcript identification [5, 6].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [5]
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Zhao, S., & Zhang, B. (2015) · BMC Genomics · doi:10.1186/s12864-015-1308-8 · Cited by 130 · see citing works
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Genes, behavior and next‐generation <scp>RNA</scp> sequencing
Hitzemann, R., Bottomly, D., Darakjian, P., Walter, N., Iancu, O., Searles, R., Wilmot, B., & McWeeney, S. (2013) · Genes, Brain and Behavior · doi:10.1111/gbb.12007 · Cited by 88 · see citing works
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RNA-Seq Reveals Novel Transcriptional Reorganization in Human Alcoholic Brain
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Miller, J. A., Menon, V., Goldy, J., Kaykas, A., Lee, C.-K., Smith, K. A., Shen, E. H., Phillips, J. W., Lein, E. S., & Hawrylycz, M. J. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-154 · Cited by 48 · see citing works
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Yuste, R., Hawrylycz, M., Aalling, N., Aguilar-Valles, A., Arendt, D., Armañanzas, R., Ascoli, G. A., Bielza, C., Bokharaie, V., Bergmann, T. B., et al. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0685-8 · Cited by 32 · see citing works
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Introduction to Sequencing the Brain Transcriptome
Hitzemann, R., Darakjian, P., Walter, N., Dan Iancu, O., Searles, R., & McWeeney, S. (2014) · International Review of Neurobiology · doi:10.1016/b978-0-12-801105-8.00001-1 · Cited by 20 · see citing works
- Authors
- Garber, M., Grabherr, M. G., Guttman, M., & Trapnell, C.
- Affiliations
- Broad Institute (US)
- Corresponding author
- Garber, Manuel
- Venue
- Nature Methods
- Published
- 2011-05-27
- Volume
- 8
- Issue
- 6
- Pages
- 469-477
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W2055770801
- Cited by
- 1,042
- References
- 86
- Citations by year
- 2026: 19 · 2025: 23 · 2024: 45 · 2023: 54 · 2022: 65 · 2021: 68 · 2020: 55 · 2019: 54 · 2018: 63 · 2017: 74 · 2016: 87 · 2015: 123 · 2014: 92 · 2013: 115 · 2012: 84
- FWCI
- 8.80
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Methods (publishedVersion)
- PubMed (publishedVersion)
- CaltechAUTHORS (California Institute of Technology) (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 › Molecular Biology › Gene expression and cancer classification
- Keywords
- RNA-seq, transcriptome annotation, gene expression quantification, read mapping, transcriptome assembly, isoform quantification, computational genomics
- MeSH terms
- Animals, Humans, Sequence Alignment, Sequence Analysis, RNA, Computational Biology, Gene Expression Profiling, Genomics, High-Throughput Nucleotide Sequencing
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