Assessment of transcript reconstruction methods for RNA-seq
Open accessAbstract
The RGASP consortium compared 25 RNA-seq analysis programs in their ability to identify exons, reconstruct transcripts and quantify expression levels. Assembly of isoforms and their expression levels in higher eukaryotes remains a challenge. We evaluated 25 protocol variants of 14 independent computational methods for exon identification, transcript reconstruction and expression-level quantification from RNA-seq data. Our results show that most algorithms are able to identify discrete transcript components with high success rates but that assembly of complete isoform structures poses a major challenge even when all constituent elements are identified. Expression-level estimates also varied widely across methods, even when based on similar transcript models. Consequently, the complexity of higher eukaryotic genomes imposes severe limitations on transcript recall and splice product discrimination that are likely to remain limiting factors for the analysis of current-generation RNA-seq data.
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It is difficult to identify transcription start and end sites.
“In addition, it is difficult to identify transcription start and end sites [21], and tools such as GRIT [22] that incorporate other data such as 5’ ends from CAGE or RAMPAGE typically have a better chance of annotating the major expressed isoforms correctly.”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [21]
Additional factors that interfere with intra-sample comparisons include changes in transcript length across samples or conditions, positional biases in coverage along the transcript, average fragment size, and the GC contents of genes.
“Additional factors that interfere with intra-sample comparisons include changes in transcript length across samples or conditions [50], positional biases in coverage along the transcript (which are accounted for in Cufflinks), average fragment size [43], and the GC contents of genes (corrected in the EDAseq package [21]).”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [21]
The accuracy of isoform estimates is hindered by incomplete annotations, limitations of short-read sequencing, coverage, and genomic biases like GC content.
“the accuracy of these estimates is hindered by incomplete annotations, as well as by limitations of short-read sequencing, coverage, and genomic biases like GC content (84, 85).”
From Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder (2018) · cites this paper as [84]
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References 36 · Cited by 699
- Authors
- The RGASP Consortium, Steijger, T., Abril, J. F., Engström, P. G., Kokocinski, F., Hubbard, T. J., Guigó, R., Harrow, J., & Bertone, P.
- Affiliations
- European Bioinformatics Institute (GB)
- Universitat de Barcelona (ES)
- Wellcome Sanger Institute (GB)
- Universitat Pompeu Fabra (ES)
- ORCID
- Venue
- Nature Methods
- Published
- 2013-11-03
- Volume
- 10
- Issue
- 12
- Pages
- 1177-1184
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC3851240
- OpenAlex
- W2117688129
- Cited by
- 699
- References
- 36
- Citations by year
- 2026: 28 · 2025: 29 · 2024: 34 · 2023: 46 · 2022: 55 · 2021: 85 · 2020: 58 · 2019: 73 · 2018: 72 · 2017: 58 · 2016: 58 · 2015: 61 · 2014: 38 · 2013: 3 · 2012: 1
- FWCI
- 23.91
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc-sa
- www.springer.com/tdm (unspecified)
- Locations
- Nature Methods (publishedVersion, cc-by-nc-sa)
- PubMed Central (publishedVersion, cc-by-nc-sa)
- PubMed (publishedVersion)
- Repositori digital de la UPF (Universitat Pompeu Fabra) (publishedVersion, cc-by-nc-sa)
- SERVAL (Université de Lausanne) (publishedVersion, cc-by-nc-sa)
- Archive ouverte UNIGE (University of Geneva) (publishedVersion, other-oa)
- Cold Spring Harbor Laboratory Institutional Repository (Cold Spring Harbor Laboratory) (acceptedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- IRIS (submittedVersion, cc-by-nc-sa)
- Minerva Access (University of Melbourne) (submittedVersion, cc-by-nc-sa)
- HAL (submittedVersion)
- Dipòsit Digital de la Universitat de Barcelona (Universitat de Barcelona) (submittedVersion, cc-by-nc-sa)
- eScholarship (California Digital Library) (submittedVersion)
- Max Planck Digital Library (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Research Portal (King's College London) (submittedVersion, other-oa)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- RECERCAT (Consorci de Serveis Universitaris de Catalunya) (submittedVersion, other-oa)
- ResearchSpace (University of Auckland) (submittedVersion, cc-by-nc-sa)
- Archive ouverte UNIGE (University of Geneva) (submittedVersion, cc-by)
- Apollo (University of Cambridge) (submittedVersion, cc-by-nc-sa)
- Apollo
- Funders
- Wellcome Trust (095908, 26205, 062023, 077198, WT 098051)
- European Molecular Biology Laboratory
- National Institutes of Health (5u54hg004555-04)
- National Human Genome Research Institute (U54-HG004555, U41HG007234, U54HG004557)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Keywords
- RNA-seq, exon identification, expression quantification, computational methods comparison
- MeSH terms
- Algorithms, Animals, Drosophila melanogaster, Exons, Humans, Introns, RNA Splicing, RNA, Messenger, Software, Genome, Caenorhabditis elegans, Sequence Analysis, RNA, Computational Biology, Gene Expression Profiling, RNA Splice Sites
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