RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome
Open accessAbstract
Background: RNA-Seq is revolutionizing the way transcript abundances are measured. A key challenge in transcript quantification from RNA-Seq data is the handling of reads that map to multiple genes or isoforms. This issue is particularly important for quantification with de novo transcriptome assemblies in the absence of sequenced genomes, as it is difficult to determine which transcripts are isoforms of the same gene. A second significant issue is the design of RNA-Seq experiments, in terms of the number of reads, read length, and whether reads come from one or both ends of cDNA fragments. Results: We present RSEM, an user-friendly software package for quantifying gene and isoform abundances from single-end or paired-end RNA-Seq data. RSEM outputs abundance estimates, 95% credibility intervals, and visualization files and can also simulate RNA-Seq data. In contrast to other existing tools, the software does not require a reference genome. Thus, in combination with a de novo transcriptome assembler, RSEM enables accurate transcript quantification for species without sequenced genomes. On simulated and real data sets, RSEM has superior or comparable performance to quantification methods that rely on a reference genome. Taking advantage of RSEM's ability to effectively use ambiguously-mapping reads, we show that accurate gene-level abundance estimates are best obtained with large numbers of short single-end reads. On the other hand, estimates of the relative frequencies of isoforms within single genes may be improved through the use of paired-end reads, depending on the number of possible splice forms for each gene. Conclusions: RSEM is an accurate and user-friendly software tool for quantifying transcript abundances from RNA-Seq data. As it does not rely on the existence of a reference genome, it is particularly useful for quantification with de novo transcriptome assemblies. In addition, RSEM has enabled valuable guidance for cost-efficient design of quantification experiments with RNA-Seq, which is currently relatively expensive.
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Transcriptome alignment substantially reduces target complexity by limiting it to known transcripts, including all possible splice isoforms.
“Transcriptome alignment substantially reduces target complexity by limiting it to known transcripts, including all possible splice isoforms (Li and Dewey 2011).”
From RNA-Seq Alignment to Individualized Genomes Improves Transcript Abundance Estimates in Multiparent Populations (2014) · cites this paper as Li and Dewey 2011
In Implementation > Reference sequence preparation (page unavailable), the paper says transcript-level alignment uses a reference transcript set and that the total length of all possible transcripts is often much smaller than the genome, allowing faster alignment. It also says that set must be supplied from transcript sequences or annotations; it does not establish that it includes all possible splice isoforms. (LLM, conf=0.88)
Experimental — accuracy not yet measuredRelative gene abundance estimates based solely on unique read counts are biased-genes composed primarily of unique sequence are overestimated while genes with closely related pseudogenes or family members are underestimated.
“our results confirm published reports that relative gene abundance estimates based solely on unique read counts are biased—genes composed primarily of unique sequence are overestimated while genes with closely related pseudogenes or family members are underestimated (Mortazavi et al. 2008; Li and Dewey 2011).”
From RNA-Seq Alignment to Individualized Genomes Improves Transcript Abundance Estimates in Multiparent Populations (2014) · cites this paper as Li and Dewey 2011
The Background > Related work section says that counting only uniquely mapped reads produces biased estimates when mappability is not taken into account. It does not explicitly state the claimed direction of bias for genes with mostly unique sequence versus genes with closely related pseudogenes or family members. Page numbers are unavailable in our copy. (LLM, conf=0.83)
Experimental — accuracy not yet measuredThe inclusion of multireads adds complexity to the abundance estimation problem but EM algorithms are well suited to this task.
“The inclusion of multireads adds complexity to the abundance estimation problem but EM algorithms are well suited to this task (Li and Dewey 2011; Nicolae et al. 2011; Turro et al. 2011; Roberts and Pachter 2013).”
From RNA-Seq Alignment to Individualized Genomes Improves Transcript Abundance Estimates in Multiparent Populations (2014) · cites this paper as Li and Dewey 2011
The RSEM algorithm uses an expectation maximization approach that returns TPM values.
“Additionally, the RSEM algorithm uses an expectation maximization approach that returns TPM values [40].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [40]
Raw read counts are assembled outside limma using tools such as featureCounts, HTSeq-counts or RSEM.
“Raw read counts are assembled outside limma using tools such as featureCounts (29), HTSeq-counts (30) or RSEM (31).”
From limma powers differential expression analyses for RNA-sequencing and microarray studies (2015) · cites this paper as [31]
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- Authors
- Li, B., & Dewey, C. N.
- Affiliations
- University of Wisconsin–Madison (US)
- Corresponding author
- Dewey, Colin N
- ORCID
- Venue
- BMC Bioinformatics
- Published
- 2011-08-04
- Volume
- 12
- Issue
- 1
- Pages
- 323-323
- ISSN
- 1471-2105 (ISSN-L)
- PMC
- PMC3163565
- OpenAlex
- W1999574084
- Cited by
- 22,179
- References
- 39
- Citations by year
- 2026: 1650 · 2025: 2387 · 2024: 2647 · 2023: 2517 · 2022: 2448 · 2021: 2588 · 2020: 2146 · 2019: 1674 · 2018: 1311 · 2017: 1062 · 2016: 794 · 2015: 487 · 2014: 279 · 2013: 134 · 2012: 28
- FWCI
- 57.58
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Bioinformatics (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)
- Funders
- National Institutes of Health
- National Human Genome Research Institute (R01HG005232)
- 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
- RSEM, transcript quantification, RNA-seq, isoform abundance, gene expression, de novo transcriptome assembly, paired-end reads, single-end reads, credibility intervals
- MeSH terms
- Animals, Computer Simulation, Humans, RNA, Software, Sequence Analysis, RNA, Protein Isoforms, Gene Expression Profiling, Mice
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