Cited by 310Genomics and Phylogenetic StudiesGene expression and cancer classificationSingle-cell and spatial transcriptomics

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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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Nookaew, Intawat, Papini, Marta, Pornputtapong, Natapol, Scalcinati, Gionata, Fagerberg, Linn, Uhlén, Matthias, Nielsen, Jens·2012·Nucleic Acids Research

DOI 10.1093/nar/gks804PMC PMC3488244

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Abstract

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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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
ORCID: Nookaew, Intawat
Pornputtapong, Natapol
Fagerberg, Linn
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
DOI: 10.1093/nar/gks804
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: cc-by-nc
creativecommons.org/licenses/by-nc/3.0 (vor, 1521 d)
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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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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