A comprehensive comparison of RNA-Seq-based transcriptome analysis from reads to differential gene expression and cross-comparison with microarrays: a case study in Saccharomyces cerevisiae
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RNA-seq, has recently become an attractive method of choice in the studies of transcriptomes, promising several advantages compared with microarrays. In this study, we sought to assess the contribution of the different analytical steps involved in the analysis of RNA-seq data generated with the Illumina platform, and to perform a cross-platform comparison based on the results obtained through Affymetrix microarray. As a case study for our work we, used the Saccharomyces cerevisiae strain CEN.PK 113-7D, grown under two different conditions (batch and chemostat). Here, we asses the influence of genetic variation on the estimation of gene expression level using three different aligners for read-mapping (Gsnap, Stampy and TopHat) on S288c genome, the capabilities of five different statistical methods to detect differential gene expression (baySeq, Cuffdiff, DESeq, edgeR and NOISeq) and we explored the consistency between RNA-seq analysis using reference genome and de novo assembly approach. High reproducibility among biological replicates (correlation≥0.99) and high consistency between the two platforms for analysis of gene expression levels (correlation≥0.91) are reported. The results from differential gene expression identification derived from the different statistical methods, as well as their integrated analysis results based on gene ontology annotation are in good agreement. Overall, our study provides a useful and comprehensive comparison between the two platforms (RNA-seq and microrrays) for gene expression analysis and addresses the contribution of the different steps involved in the analysis of RNA-seq data.
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Because simulations rely on specific distributions or limited datasets and spike-in datasets have only technical replicates with minimal variation, comparisons have been complemented with real datasets with true biological replicates.
“As simulations typically rely on specific statistical distributions or on limited experimental datasets and as spike-in datasets represent only technical replicates with minimal variation, comparisons using simulated datasets have been complemented with more practical comparisons in real datasets with true biological replicates [64, 210, 211].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [210]
DESeq and edgeR rank genes similarly but DESeq is often relatively conservative and edgeR too liberal in controlling FDR.
“DESeq and edgeR perform similarly in ranking genes but are often relatively conservative or too liberal, respectively, in controlling FDR [63, 209, 210].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [210]
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- Authors
- Nookaew, I., Papini, M., Pornputtapong, N., Scalcinati, G., Fagerberg, L., Uhlén, M., & Nielsen, J.
- Affiliations
- Chalmers University of Technology (SE)
- Novo Nordisk Foundation (DK)
- Corresponding author
- Nielsen, Jens
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2012-09-08
- Volume
- 40
- Issue
- 20
- Pages
- 10084-10097
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC3488244
- OpenAlex
- W2102921564
- Cited by
- 309
- References
- 59
- Citations by year
- 2026: 1 · 2025: 8 · 2024: 9 · 2023: 9 · 2022: 19 · 2021: 10 · 2020: 17 · 2019: 31 · 2018: 30 · 2017: 25 · 2016: 40 · 2015: 33 · 2014: 44 · 2013: 30 · 2012: 2
- FWCI
- 14.02
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nucleic Acids Research (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- HighWire Press Open Archive (submittedVersion)
- Chalmers Publication Library (Chalmers University of Technology) (submittedVersion)
- Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) (submittedVersion, cc-by-nc)
- Chalmers Research (Chalmers University of Technology) (submittedVersion)
- Funders
- Novo Nordisk Fonden (NNF10CC1016517)
- 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
- RNA-seq, gene expression profiling, differential gene expression, microarray comparison, Saccharomyces cerevisiae, read mapping, de novo assembly, reference genome, gene ontology annotation, S288c
- MeSH terms
- Base Sequence, Chromosome Mapping, Data Interpretation, Statistical, Molecular Sequence Data, Saccharomyces cerevisiae, Software, Reproducibility of Results, Genome, Fungal, Sequence Analysis, RNA, Oligonucleotide Array Sequence Analysis, Polymorphism, Single Nucleotide, Gene Expression Profiling, INDEL Mutation, High-Throughput Nucleotide Sequencing
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