Alexander Wolff1, Michaela Bayerlová1, Jochen Gaedcke2, Dieter Kube3, Tim Beißbarth1. 1. Dept. of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany. 2. Dept. of General-, Visceral- and Pediatric Surgery, University Medical Center Göttingen, Göttingen, Germany. 3. Dept. of Hematology and Oncology, University Medical Center Göttingen, Göttingen, Germany.
Abstract
BACKGROUND: Pipeline comparisons for gene expression data are highly valuable for applied real data analyses, as they enable the selection of suitable analysis strategies for the dataset at hand. Such pipelines for RNA-Seq data should include mapping of reads, counting and differential gene expression analysis or preprocessing, normalization and differential gene expression in case of microarray analysis, in order to give a global insight into pipeline performances. METHODS: Four commonly used RNA-Seq pipelines (STAR/HTSeq-Count/edgeR, STAR/RSEM/edgeR, Sailfish/edgeR, TopHat2/Cufflinks/CuffDiff)) were investigated on multiple levels (alignment and counting) and cross-compared with the microarray counterpart on the level of gene expression and gene ontology enrichment. For these comparisons we generated two matched microarray and RNA-Seq datasets: Burkitt Lymphoma cell line data and rectal cancer patient data. RESULTS: The overall mapping rate of STAR was 98.98% for the cell line dataset and 98.49% for the patient dataset. Tophat's overall mapping rate was 97.02% and 96.73%, respectively, while Sailfish had only an overall mapping rate of 84.81% and 54.44%. The correlation of gene expression in microarray and RNA-Seq data was moderately worse for the patient dataset (ρ = 0.67-0.69) than for the cell line dataset (ρ = 0.87-0.88). An exception were the correlation results of Cufflinks, which were substantially lower (ρ = 0.21-0.29 and 0.34-0.53). For both datasets we identified very low numbers of differentially expressed genes using the microarray platform. For RNA-Seq we checked the agreement of differentially expressed genes identified in the different pipelines and of GO-term enrichment results. CONCLUSION: In conclusion the combination of STAR aligner with HTSeq-Count followed by STAR aligner with RSEM and Sailfish generated differentially expressed genes best suited for the dataset at hand and in agreement with most of the other transcriptomics pipelines.
BACKGROUND: Pipeline comparisons for gene expression data are highly valuable for applied real data analyses, as they enable the selection of suitable analysis strategies for the dataset at hand. Such pipelines for RNA-Seq data should include mapping of reads, counting and differential gene expression analysis or preprocessing, normalization and differential gene expression in case of microarray analysis, in order to give a global insight into pipeline performances. METHODS: Four commonly used RNA-Seq pipelines (STAR/HTSeq-Count/edgeR, STAR/RSEM/edgeR, Sailfish/edgeR, TopHat2/Cufflinks/CuffDiff)) were investigated on multiple levels (alignment and counting) and cross-compared with the microarray counterpart on the level of gene expression and gene ontology enrichment. For these comparisons we generated two matched microarray and RNA-Seq datasets: Burkitt Lymphoma cell line data and rectal cancerpatient data. RESULTS: The overall mapping rate of STAR was 98.98% for the cell line dataset and 98.49% for the patient dataset. Tophat's overall mapping rate was 97.02% and 96.73%, respectively, while Sailfish had only an overall mapping rate of 84.81% and 54.44%. The correlation of gene expression in microarray and RNA-Seq data was moderately worse for the patient dataset (ρ = 0.67-0.69) than for the cell line dataset (ρ = 0.87-0.88). An exception were the correlation results of Cufflinks, which were substantially lower (ρ = 0.21-0.29 and 0.34-0.53). For both datasets we identified very low numbers of differentially expressed genes using the microarray platform. For RNA-Seq we checked the agreement of differentially expressed genes identified in the different pipelines and of GO-term enrichment results. CONCLUSION: In conclusion the combination of STAR aligner with HTSeq-Count followed by STAR aligner with RSEM and Sailfish generated differentially expressed genes best suited for the dataset at hand and in agreement with most of the other transcriptomics pipelines.
Authors: James R Bradford; Yvonne Hey; Tim Yates; Yaoyong Li; Stuart D Pepper; Crispin J Miller Journal: BMC Genomics Date: 2010-05-05 Impact factor: 3.969
Authors: Nikolay Kolesnikov; Emma Hastings; Maria Keays; Olga Melnichuk; Y Amy Tang; Eleanor Williams; Miroslaw Dylag; Natalja Kurbatova; Marco Brandizi; Tony Burdett; Karyn Megy; Ekaterina Pilicheva; Gabriella Rustici; Andrew Tikhonov; Helen Parkinson; Robert Petryszak; Ugis Sarkans; Alvis Brazma Journal: Nucleic Acids Res Date: 2014-10-31 Impact factor: 16.971
Authors: Christy L Trejo; Miloš Babić; Elliot Imler; Migdalia Gonzalez; Sergei I Bibikov; Peter J Shepard; Harper C VanSteenhouse; Joanne M Yeakley; Bruce E Seligmann Journal: PLoS One Date: 2019-02-22 Impact factor: 3.240
Authors: Sasagu Kurozumi; Chitra Joseph; Sultan Sonbul; Sami Alsaeed; Yousif Kariri; Abrar Aljohani; Sara Raafat; Mansour Alsaleem; Angela Ogden; Simon J Johnston; Mohammed A Aleskandarany; Takaaki Fujii; Ken Shirabe; Carlos Caldas; Ibraheem Ashankyty; Leslie Dalton; Ian O Ellis; Christine Desmedt; Andrew R Green; Nigel P Mongan; Emad A Rakha Journal: Br J Cancer Date: 2019-05-22 Impact factor: 7.640