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
| Autor: | Linn Fagerberg, Marta Papini, Intawat Nookaew, Natapol Pornputtapong, Matthias Uhlén, Jens Nielsen, Gionata Scalcinati |
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| Rok vydání: | 2012 |
| Předmět: |
Molecular Sequence Data
Sequence assembly RNA-Seq Saccharomyces cerevisiae yeast Biology algorithms Polymorphism Single Nucleotide Genome Transcriptome sequence count data 03 medical and health sciences 0302 clinical medicine INDEL Mutation framework Genetics genome Gene Oligonucleotide Array Sequence Analysis 030304 developmental biology 0303 health sciences Base Sequence Bioinformatics and Systems Biology Sequence Analysis RNA Gene Expression Profiling Chromosome Mapping High-Throughput Nucleotide Sequencing Reproducibility of Results Computational Biology alignment landscape quantification Gene expression profiling messenger-rna Data Interpretation Statistical Genome Fungal DNA microarray metabolism Software 030217 neurology & neurosurgery Reference genome |
| Zdroj: | Nookaew, I, Papini, M, Pornputtapong, N, Scalcinati, G, Fagerberg, L, Uhlén, M & Nielsen, J 2012, ' 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 ', Nucleic Acids Research, vol. 40, no. 20, pp. 10084-10097 . https://doi.org/10.1093/nar/gks804 Nucleic Acids Research (0305-1048) vol.40(2012) Nucleic Acids Research |
| ISSN: | 1362-4962 0305-1048 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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