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

By Intawat Nookaew, Marta Papini, Natapol Pornputtapong, Gionata Scalcinati, Linn Fagerberg, M. Uhlén, Jens Nielsen. Nucleic Acids Research, 2012

337 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/da0a385dff05ea58e5727f02f37e4c2ece6da7a3

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Citations in the corpus

A comparison of methods for differential expression analysis of RNA-seq data (2013, result): “In [21], the authors presented a case study on Saccharomyces cerevisiae, comparing the results obtained by several differential expression analysis methods for RNA-seq with each other and with results obtained from microarrays, and reported a generally good agreement between the different methods.”
Enriching the gene set analysis of genome-wide data by incorporating directionality of gene expression and combining statistical hypotheses and methods (2013, methodology): “Microarray (continuous) as well as RNA-seq (discrete) data can be used for the analysis.”
Corset: enabling differential gene expression analysis for de novo assembled transcriptomes (2014, methodology): “We tested the performance of Corset against other clustering and counting methods using three RNA-seq datasets: chicken male and female embryonic tissue [24], human primary lung fibroblasts, with and without a small interfering RNA (siRNA) knock down of HOXA1 [25], and yeast grown under batch and ch”
ANOVA-Like Differential Expression (ALDEx) Analysis for Mixed Population RNA-Seq (2013, result): “It is worth noting that a recent comparison of baySeq, DESeq and edgeR using a deeplysequenced and validated dataset showed that DESeq and edgeR were more discordant with each other than either was with baySeq[54], suggesting that baySeq exhibits a lower false positive rate than either of the other ”
Comparison of software packages for detecting differential expression in RNA-seq studies (2013, background): “[17] included in their comparison five packages (edgeR, DESeq, baySeq, NOIseq and Cuffdiff) on a real data set but their case study on Saccharomyces cerevisiae involved only three replicates per group.”
Waste Not, Want Not: Why Rarefying Microbiome Data Is Inadmissible (2013, result): “One minor exception is an increased Type-I error for edgeR compared to later methods [63], which was also detected in our results relative to DESeq and DESeq2 when TMM normalization was used (not shown) but not after switching to RLE normalization (Figure 5, Protocol S2).”
A survey of best practices for RNA-seq data analysis (2016, methodology): “…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].”