Evaluation of read count based RNAseq analysis methods

Background RNAseq technology is replacing microarray technology as the tool of choice for gene expression profiling. While providing much richer data than microarray, analysis of RNAseq data has been much more challenging. To date, there has not been a consensus on the best approach for conducting r...

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Vydáno v:BMC genomics Ročník 14; číslo Suppl 8; s. S2
Hlavní autoři: Guo, Yan, Li, Chung-I, Ye, Fei, Shyr, Yu
Médium: Journal Article
Jazyk:angličtina
Vydáno: London BioMed Central 2013
Springer Nature B.V
Témata:
Animal Genetics and Genomics
Area Under Curve
Biology
Biomedical and Life Sciences
Biomedical research
Computer Simulation
Gene expression
Gene Expression Profiling
Genomes
Genomics
Humans
Life Sciences
Medical research
Methods
Microarrays
Microbial Genetics and Genomics
Plant Genetics and Genomics
Proteomics
ROC Curve
Sensitivity and Specificity
Sequence Analysis, RNA - methods
Software
United States > US
Nashville Tennessee
DESeq
NBPSeq
expression
edger
TSPM
RNAseq
DEGseq
baySeq
ISSN:1471-2164, 1471-2164
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Shrnutí:Background RNAseq technology is replacing microarray technology as the tool of choice for gene expression profiling. While providing much richer data than microarray, analysis of RNAseq data has been much more challenging. To date, there has not been a consensus on the best approach for conducting robust RNAseq analysis. Results In this study, we designed a thorough experiment to evaluate six read count-based RNAseq analysis methods (DESeq, DEGseq, edgeR, NBPSeq, TSPM and baySeq) using both real and simulated data. We found the six methods produce similar fold changes and reasonable overlapping of differentially expressed genes based on p-values. However, all six methods suffer from over-sensitivity. Conclusions Based on the evaluation of runtime using real data and area under the receiver operating characteristic curve (AUC-ROC) using simulated data, we found that edgeR achieves a better balance between speed and accuracy than the other methods.
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ISSN:1471-2164
1471-2164
DOI:10.1186/1471-2164-14-S8-S2