Differential analyses for RNA-seq: Transcript-level estimates improve gene-level inferences

F1000Research

Volumn 4, Issue , 2016, Pages

  Soneson, Charlotte ^a   Love, Michael I ^b,c   Robinson, Mark D ^a  

^a UNIVERSITY OF ZURICH   (Switzerland)

^b DANA FARBER CANCER INSTITUTE   (United States)

^c HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DNA TRANSCRIPTION; GENE EXPRESSION; PIPELINE; QUANTITATIVE STUDY; STATISTICAL MODEL; TRANSCRIPTION INITIATION; TRANSCRIPTION REGULATION;

EID: 85010908291     PISSN: 20461402     EISSN: 1759796X     Source Type: Journal    
DOI: 10.12688/F1000RESEARCH.7563.2     Document Type: Article

References (35)

1. Liao Y, Smyth GK, Shi W: featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014; 30(7): 923-30.
2. Anders S, Pyl PT, Huber W: HTSeq--a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015; 31(2): 166-169.
3. Trapnell C, Roberts A, Goff L, et al.: Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc. 2012; 7(3): 562-78.
4. Li B, Dewey CN: RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics. 2011; 12: 323.
5. Glaus P, Honkela A, Rattray M: Identifying differentially expressed transcripts from RNA-seq data with biological variation. Bioinformatics. 2012; 28(13): 1721-1728.
6. Bray N, Pimentel H, Melsted P, et al.: Near-optimal RNA-Seq quantification. arXiv:1505.02710. 2015.
7. Patro R, Duggal G, Kingsford C: Accurate, fast, and model-aware transcript expression quantification with Salmon. bioRxiv. 2015.
8. Mortazavi A, Williams BA, McCue K, et al.: Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008; 5(7): 621-628.
9. 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): 511-515
10. Wagner GP, Kin K, Lynch VJ: Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples. Theory Biosci. 2012; 131(4): 281-285.
11. Love MI, Huber W, Anders S: Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014; 15(12): 550.
12. Robinson MD, McCarthy DJ, Smyth GK: edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010; 26(1): 139-40.
13. Ritchie ME, Phipson B, Wu D, et al.: limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015; 43(7): e47.
14. Bottomly D, Walter NA, Hunter JE, et al.: Evaluating gene expression in C57BL/6J and DBA/2J mouse striatum using RNA-Seq and microarrays. PLoS One. 2011; 6(3): e17820.
15. Yang S, Marín-Juez R, Meijer AH, et al.: Common and specific downstream signaling targets controlled by Tlr2 and Tlr5 innate immune signaling in zebrafish. BMC Genomics. 2015; 16(1): 547.
16. Currais A, Goldberg J, Farrokhi C, et al.: A comprehensive multiomics approach toward understanding the relationship between aging and dementia. Aging (Albany NY). 2015; 7(11): 937-955.
17. Chang AJ, Ortega FE, Riegler J, et al.: Oxygen regulation of breathing through an olfactory receptor activated by lactate. Nature. 2015; 527(7577): 240-244.
18. Soneson C, Matthes KL, Nowicka M, et al.: Differential transcript usage from RNA-seq data: isoform pre-filtering improves performance of count-based methods. bioRxiv. 2015.
19. Kanitz A, Gypas F, Gruber AJ, et al.: Comparative assessment of methods for the computational inference of transcript isoform abundance from RNA-seq data. Genome Biol. 2015; 16(1): 150.
20. Roberts A, Trapnell C, Donaghey J, et al.: Improving RNA-Seq expression estimates by correcting for fragment bias. Genome Biol. 2011; 12(3): R22.
21. Robert C, Watson M: Errors in RNA-Seq quantification affect genes of relevance to human disease. Genome Biol. 2015; 16(1): 177
22. Anders S, Reyes A, Huber W: Detecting differential usage of exons from RNA-seq data. Genome Res. 2012; 22(10): 2008-17.
23. Lawrence M, Huber W, Pagès H, et al.: Software for computing and annotating genomic ranges. PLoS Comput Biol. 2013; 9(8): e1003118.
24. Trapnell C, Hendrickson DG, Sauvageau M, et al.: Differential analysis of gene regulation at transcript resolution with RNA-seq. Nat Biotechnol. 2013; 31(1): 46-53.
25. Zhao S, Xi L, Zhang B: Union Exon Based Approach for RNA-Seq Gene Quantification: To Be or Not to Be? PLoS One. 2015; 10(11): e0141910.
26. Gonzàlez-Porta M, Frankish A, Rung J, et al.: Transcriptome analysis of human tissues and cell lines reveals one dominant transcript per gene. Genome Biol. 2013; 14(7): R70.
27. Jia C, Guan W, Yang A, et al.: MetaDiff: differential isoform expression analysis using random-effects meta-regression. BMC Bioinformatics. 2015; 16(1): 208.
28. Love MI, Hogenesch JB, Irizarry RA: Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation. bioRxiv. 2015.
29. Antonarakis ES, Lu C, Wang H, et al.: AR-V7 and resistance to enzalutamide and abiraterone in prostate cancer. N Engl J Med. 2014; 371(11): 1028-38.
30. Soneson C, Love MI, Robinson MD: Data set 1 in: Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Research. 2016.
31. Soneson C, Love MI, Robinson MD: Data set 2 in: Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Research. 2016.
32. Soneson C, Love MI, Robinson MD: Data set 3 in: Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Research. 2016.
33. Soneson C, Love MI, Robinson MD: Data set 4 in: Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Research. 2016.
34. Soneson C, Love MI, Robinson MD: Data set 5 in: Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Research. 2016.
35. Soneson C, Love MI, Robinson MD: Data set 6 in: Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Research. 2016.