The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation
Open accessAbstract
High-throughput mRNA sequencing (RNA-Seq) promises simultaneous transcript discovery and abundance estimation. However, this would require algorithms that are not restricted by prior gene annotations and that account for alternative transcription and splicing. Here we introduce such algorithms in an open-source software program called Cufflinks. To test Cufflinks, we sequenced and analyzed >430 million paired 75-bp RNA-Seq reads from a mouse myoblast cell line over a differentiation time series. We detected 13,692 known transcripts and 3,724 previously unannotated ones, 62% of which are supported by independent expression data or by homologous genes in other species. Over the time series, 330 genes showed complete switches in the dominant transcription start site (TSS) or splice isoform, and we observed more subtle shifts in 1,304 other genes. These results suggest that Cufflinks can illuminate the substantial regulatory flexibility and complexity in even this well-studied model of muscle development and that it can improve transcriptome-based genome annotation.
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In the citing paper
“transcripts of between 8000 and 16 000 protein-coding genes expressed from a single cell type can be detected”
From Deep proteome and transcriptome mapping of a human cancer cell line (2011) · cites this paper as Trapnell et al, 2010
In the citing paper
“assembled into 49 000 unique transcripts (Trapnell et al, 2010) that mapped to 16 554 different protein-coding genes”
From Deep proteome and transcriptome mapping of a human cancer cell line (2011) · cites this paper as Trapnell et al, 2010
In the citing paper
“Cufflinks [39] estimates transcript expression from a mapping to the genome obtained from mappers such as TopHat using an expectation-maximization approach that estimates transcript abundances.”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [39]
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Metadata
- Authors
- Trapnell, C., Williams, B. A., Pertea, G., Mortazavi, A., Kwan, G., van Baren, M. J., Salzberg, S. L., Wold, B. J., & Pachter, L.
- Affiliations
- University of Maryland, College Park (US)
- California Institute of Technology (US)
- University of California, Berkeley (US)
- Corresponding author
- Pachter, Lior
- ORCID
- Venue
- Nature Biotechnology
- Published
- 2010-05
- Volume
- 28
- Issue
- 5
- Pages
- 511-515
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC3146043
- OpenAlex
- W2141458291
- Cited by
- 15,556
- References
- 31
- Citations by year
- 2026: 449 · 2025: 708 · 2024: 865 · 2023: 1081 · 2022: 1349 · 2021: 1458 · 2020: 1444 · 2019: 1349 · 2018: 1319 · 2017: 1225 · 2016: 1051 · 2015: 936 · 2014: 889 · 2013: 684 · 2012: 447
- FWCI
- 185.65
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion)
- Funders
- National Human Genome Research Institute (U54HG004576, R01HG006102)
- National Institute of General Medical Sciences (R01GM083873)
- U.S. National Library of Medicine (R01LM006845)
- 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
- transcript assembly, transcript quantification, Cufflinks, unannotated transcripts, isoform switching, alternative splicing, alternative transcription, cell differentiation, myogenesis, genome annotation
- MeSH terms
- Algorithms, Animals, Cell Differentiation, Cell Line, RNA, Messenger, Software, Proto-Oncogene Proteins c-myc, Genome, Sequence Analysis, RNA, Protein Isoforms, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Mice
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