Assessing the consistency of public human tissue RNA-seq data sets

Briefings in Bioinformatics

Volumn 16, Issue 6, 2015, Pages 941-949

  Danielsson, Frida ^a   James, Tojo ^b   Gomez Cabrero, David ^b   Huss, Mikael ^c  

^a ROYAL INSTITUTE OF TECHNOLOGY   (Sweden)

^b KAROLINSKA INSTITUTE   (Sweden)

^c STOCKHOLM UNIVERSITY   (Sweden)

Author keywords

Clustering; Gene expression; Meta analysis; Public data; RNA seq

Indexed keywords

RNA;

BRAIN; CARDIAC MUSCLE; GENE EXPRESSION PROFILING; GENETIC DATABASE; GENETICS; HUMAN; KIDNEY; METABOLISM; SEQUENCE ANALYSIS;

BRAIN; DATABASES, GENETIC; GENE EXPRESSION PROFILING; HUMANS; KIDNEY; MYOCARDIUM; RNA; SEQUENCE ANALYSIS, RNA;

EID: 84941241201     PISSN: 14675463     EISSN: 14774054     Source Type: Journal    
DOI: 10.1093/bib/bbv017     Document Type: Article

References (17)

1. Mortazavi A, Williams BA, McCue K, et al. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods 2008;5(7):621-8.
2. Evangelou E, Ioannidis JP. Meta-analysis methods for genome- wide association studies and beyond. Nat Rev Genet 2013;14(6):379-89.
3. Leek JT, Scharpf RB, Bravo HC, et al. Tackling the widespread and critical impact of batch effects in high-throughput data. Nat Rev Genet 2010;11(10):733-9.
4. t Hoen PA, Friedlander MR, Almlof J, et al. Reproducibility of high-throughput mRNA and small RNA sequencing across laboratories. Nat Biotechnol 2013;31(11):1015-22.
5. Li S, Tighe SW, Nicolet CM, et al. Multi-platform assessment of transcriptome profiling using RNA-seq in the ABRF nextgeneration sequencing study. Nat Biotechnol 2014;32(9): 915-25.
6. Fonseca NA, Marioni JA, Brazma A. RNA-seq gene profiling-a systematic empirical comparison. bioRxiv, 2014. doi: http://dx. doi.rog/10.1101/005207.
7. Leek JT, Johnson WE, Parker HS, et al. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 2012;28(6):882-3.
8. Trapnell C, Williams BA, Pertea G, et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol 2010;28(5):511-15.
9. Lonsdale J, Thomas J, Salvatore M, et al. The Genotype-Tissue Expression (GTEx) project. Nat Genet 2013;45(6):580-5.
10. Fagerberg L, Hallstrom BM, Oksvold P, et al. Analysis of the human tissue-specific expression by genome-wide integration of transcriptomics and antibody-based proteomics. Mol Cell Proteom 2014;13(2):397-406.
11. Krupp M, Marquardt JU, Sahin U, et al. RNA-Seq Atlas-a reference database for gene expression profiling in normal tissue by next-generation sequencing. Bioinformatics 2012;28(8): 1184-5.
12. Wang ET, Sandberg R, Luo S, et al. Alternative isoform regulation in human tissue transcriptomes. Nature 2008;456(7221): 470-6.
13. Zwiener I, Frisch B, Binder H. Transforming RNA-seq data to improve the performance of prognostic gene signatures. PLoS One 2014;9(1):e85150.
14. Asmann YW, Necela BM, Kalari KR, et al. Detection of redundant fusion transcripts as biomarkers or disease-specific therapeutic targets in breast cancer. Cancer Res 2012;72(8): 1921-8.
15. Brawand D, Soumillon M, Necsulea A, et al. The evolution of gene expression levels in mammalian organs. Nature 2011; 478(7369):343-8.
16. Trapnell C, Pachter L, Salzberg SL. TopHat: discovering splice junctions with RNA-seq. Bioinformatics 2009;25(9):1105-11.
17. Li S, Labaj PP, Zumbo P, et al. Detecting and correcting systematic variation in large-scale RNA sequencing data. Nat Biotechnol 2014;32(9):888-95.