Polyester : simulating RNA-seq datasets with differential transcript expression
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Abstract
Motivation: Statistical methods development for differential expression analysis of RNA sequencing (RNA-seq) requires software tools to assess accuracy and error rate control. Since true differential expression status is often unknown in experimental datasets, artificially constructed datasets must be utilized, either by generating costly spike-in experiments or by simulating RNA-seq data. Results: Polyester is an R package designed to simulate RNA-seq data, beginning with an experimental design and ending with collections of RNA-seq reads. Its main advantage is the ability to simulate reads indicating isoform-level differential expression across biological replicates for a variety of experimental designs. Data generated by Polyester is a reasonable approximation to real RNA-seq data and standard differential expression workflows can recover differential expression set in the simulation by the user. Availability and implementation: Polyester is freely available from Bioconductor (http://bioconductor.org/). Contact: jtleek@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online.
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Polyester allows simulating experiments with differential transcript expression and biological variability, enabling assessment of accuracy and downstream differential expression performance.
“In addition to the ability to generate reads, Polyester allows simulating experiments with differential transcript expression and biological variability. Thus, we can assess not only the accuracy of the resulting estimates, but also how these estimates would perform in a typical downstream analysis task like differential expression testing.”
From Salmon provides fast and bias-aware quantification of transcript expression (2017) · cites this paper as [13]
Results from SEQC, GEUVADIS, and Polyester (simulated with bias) are expected to better represent typical real-world performance.
“However, we expect results on the SEQC [12], GEUVADIS [11], and Polyester [13] (simulated with bias) data sets to be more representative of typical real-world performance.”
From Salmon provides fast and bias-aware quantification of transcript expression (2017) · cites this paper as [13]
A parallel self-hosted continuous integration system runs Salmon on simulated samples on each commit, stores accuracy metrics, and allows tracking accuracy and identifying performance regressions across commits.
“For this task, a parallel, self-hosted continuous integration system has been created. On each commit to the Salmon repository, a Drone (Drone-CI) server pulls the latest commit, and builds it in a clean CentOS5 environment using Docker [30]. In addition to the functionality tests, Salmon is run on simulated samples (generated using Polyester [13]).”
From Salmon provides fast and bias-aware quantification of transcript expression (2017) · cites this paper as [13]
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- Authors
- Frazee, A. C., Jaffe, A. E., Langmead, B., & Leek, J. T.
- Affiliations
- Johns Hopkins University (US)
- Corresponding author
- Leek, Jeffrey T.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2015-04-28
- Volume
- 31
- Issue
- 17
- Pages
- 2778-2784
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC4635655
- OpenAlex
- W2112876600
- Cited by
- 313
- References
- 31
- Citations by year
- 2026: 9 · 2025: 23 · 2024: 35 · 2023: 15 · 2022: 32 · 2021: 72 · 2020: 31 · 2019: 36 · 2018: 27 · 2017: 16 · 2016: 13 · 2015: 2 · 2014: 2
- FWCI
- 8.39
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Bioinformatics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- HighWire Press Open Archive (submittedVersion)
- Funders
- Johns Hopkins University
- Lieber Institute for Brain Development
- National Institutes of Health (NIH-R01, UL1TR001079)
- National Institute of General Medical Sciences (R01GM105705)
- National Center for Advancing Translational Sciences
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Keywords
- differential gene expression, synthetic dataset, spike-in experiments, biological replicates, experimental design, Bioconductor
- MeSH terms
- Algorithms, Chromosomes, Human, Pair 22, Europe, Gene Expression Regulation, Genetics, Population, Haplotypes, Humans, RNA, Software, Binomial Distribution, Sequence Analysis, RNA, Computational Biology, Protein Isoforms, Gene Expression Profiling, High-Throughput Nucleotide Sequencing
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