Detecting and correcting systematic variation in large-scale RNA sequencing data

Nature Biotechnology

Volumn 32, Issue 9, 2014, Pages 888-895

  Li, Sheng ^a   Labaj, Pawel P ^b   Zumbo, Paul ^a   Sykacek, Peter ^b   Shi, Wei ^c   Shi, Leming ^d   Phan, John ^e   Wu, Po Yen ^e   Wang, May ^e   Wang, Charles ^f   Thierry Mieg, Danielle ^g   Thierry Mieg, Jean ^g   Kreil, David P ^b,h   Mason, Christopher E ^a,i  

^a WEILL CORNELL MEDICAL COLLEGE   (United States)

^b UNIVERSITY OF NATURAL RESOURCES AND LIFE SCIENCES   (Austria)

^c WALTER AND ELIZA HALL INSTITUTE OF MEDICAL RESEARCH   (Australia)

^d FUDAN UNIVERSITY   (China)

^e GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

^f LOMA LINDA UNIVERSITY SCHOOL OF MEDICINE   (United States)

^g NATIONAL INSTITUTES OF HEALTH   (United States)

^h UNIVERSITY OF WARWICK   (United Kingdom)

ⁱ FEIL FAMILY BRAIN AND MIND RESEARCH INSTITUTE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

GENES;

ANALYSIS OF VARIATIONS; DIFFERENTIALLY EXPRESSED GENE; GUANINE CYTOSINES; NORMALIZATION METHODS; REPRODUCIBILITIES; SAMPLE COMPLEXITY; SEQUENCING ERROR RATES; SYSTEMATIC VARIATION;

RNA;

CYTOSINE; GUANINE;

ARTICLE; CONTROLLED STUDY; DNA BASE COMPOSITION; FALSE POSITIVE RESULT; HUMAN; PRIORITY JOURNAL; QUALITY CONTROL; RNA SEQUENCE; SENSITIVITY AND SPECIFICITY; STATISTICAL BIAS; PROCEDURES; REPRODUCIBILITY; SEQUENCE ANALYSIS;

QUALITY CONTROL; REPRODUCIBILITY OF RESULTS; SEQUENCE ANALYSIS, RNA;

EID: 84909587930     PISSN: 10870156     EISSN: 15461696     Source Type: Journal    
DOI: 10.1038/nbt.3000     Document Type: Article

References (62)

1. Irizarry, R.A. et al. Multiple-laboratory comparison of microarray platforms. Nat. Methods 2, 345-350 (2005).
2. Wang, H., He, X., Band, M., Wilson, C., Liu, L. A study of inter-lab and inter-platform agreement of DNA microarray data. BMC Genomics 6, 71 (2005).
3. MAQC Consortium. The MicroArray Quality Control (MAQC) project shows inter-and intraplatform reproducibility of gene expression measurements. Nat. Biotechnol. 24, 1151-1161 (2006).
4. Casciano, D.A., Woodcock, J. Empowering microarrays in the regulatory setting. Nat. Biotechnol. 24, 1103 (2006).
5. Ball, C.A., Brazma, A. MGED standards: work in progress. OMICS 10, 138-144 (2006).
6. Hong, F., Wittner, B., Breitling, R., Smith, C., Battke, F. RankProd: Rank Product method for identifying differentially expressed genes with application in meta-analysis. R package version 2.28.0 (2011).
7. Dudley, J.T., Tibshirani, R., Deshpande, T., Butte, A.J. Disease signatures are robust across tissues and experiments. Mol. Syst. Biol. 5, 307 (2009).
8. Glenn, T.C. Field guide to next-generation DNA sequencers. Mol. Ecol. Resour. 11, 759-769 (2011).
9. Loman, N.J. et al. Performance comparison of benchtop high-throughput sequencing platforms. Nat. Biotechnol. 30, 434-439 (2012).
10. Editorial. Prepare for the deluge. Nat. Biotechnol. 26, 1099 (2008).
11. Ji, H., Davis, R.W. Data quality in genomics and microarrays. Nat. Biotechnol. 24, 1112-1113 (2006).
12. Bullard, J.H., Purdom, E., Hansen, K.D., Dudoit, S. Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC Bioinformatics 11, 94 (2010).
13. Wang, L., Wang, S., Li, W. RSeQC: quality control of RNA-seq experiments. Bioinformatics 28, 2184-2185 (2012).
14. Hansen, K.D., Irizarry, R.A., Wu, Z. Removing technical variability in RNA-seq data using conditional quantile normalization. Biostatistics 13, 204-216 (2012).
15. Risso, D., Schwartz, K., Sherlock, G., Dudoit, S. GC-content normalization for RNA-Seq data. BMC Bioinformatics 12, 480 (2011).
16. Aird, D. et al. Analyzing and minimizing PCR amplification bias in Illumina sequencing libraries. Genome Biol. 12, R18 (2011).
17. Benjamini, Y., Speed, T.P. Summarizing and correcting the GC content bias in high-throughput sequencing. Nucleic Acids Res. 40, e72 (2012).
18. van Heesch, S. et al. Systematic biases in DNA copy number originate from isolation procedures. Genome Biol. 14, R33 (2013).
19. Hansen, K.D., Brenner, S.E., Dudoit, S. Biases in Illumina transcriptome sequencing caused by random hexamer priming. Nucleic Acids Res. 38, e131 (2010).
20. Pickrell, J.K. et al. Understanding mechanisms underlying human gene expression variation with RNA sequencing. Nature 464, 768-772 (2010).
21. Cheung, V.G. et al. Polymorphic cis-and trans-regulation of human gene expression. PLoS Biol. 8, e1000480 (2010).
22. DeLuca, D.S. et al. RNA-SeQC: RNA-seq metrics for quality control and process optimization. Bioinformatics 28, 1530-1532 (2012).
23. Roberts, A., Trapnell, C., Donaghey, J., Rinn, J.L., Pachter, L. Improving RNA-Seq expression estimates by correcting for fragment bias. Genome Biol. 12, R22 (2011).
24. Goncalves, A., Tikhonov, A., Brazma, A., Kapushesky, M. A pipeline for RNA-seq data processing and quality assessment. Bioinformatics 27, 867-869 (2011).
25. Schulze, S.K., Kanwar, R., Golzenleuchter, M., Therneau, T.M., Beutler, A.S. SERE: single-parameter quality control and sample comparison for RNA-Seq. BMC Genomics 13, 524 (2012).
26. Leek, J.T., Johnson, W.E., Parker, H.S., Jaffe, A.E., Storey, J.D. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 28, 882-883 (2012).
27. Leek, J.T., Storey, J.D. Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genet. 3, 1724-1735 (2007).
28. Mooney, M. et al. Comparative RNA-Seq and microarray analysis of gene expression changes in B-cell lymphomas of Canis familiaris. PLoS ONE 8, e61088 (2013).
29. 't Hoen, P.A. et al. Reproducibility of high-throughput mRNA and small RNA sequencing across laboratories. Nat. Biotechnol. 31, 1015-1022 (2013).
30. Stegle, O., Parts, L., Durbin, R., Winn, J. A Bayesian framework to account for complex non-genetic factors in gene expression levels greatly increases power in eQTL studies. PLoS Comput. Biol. 6, e1000770 (2010).
31. Gagnon-Bartsch, J.A., Speed, T.P. Using control genes to correct for unwanted variation in microarray data. Biostatistics 13, 539-552 (2012).
32. Li, S. et al. Multi-platform assessment of transcriptome profiling using RNA-seq in the ABRF next-generation sequencing study. Nat. Biotechnol. doi:10.1038/nbt.2972 (24 August 2014).
33. SEQC/MAQC-III Consortium. A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium. Nat. Biotechnol. doi:10.1038/nbt.2957 (24 August 2014).
34. Li, Y., Terrell, A., Patel, J.M. in Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data 445-456 (ACM, 2011).
35. Wang, K. et al. MapSplice: accurate mapping of RNA-seq reads for splice junction discovery. Nucleic Acids Res. 38, e178 (2010).
36. Li, H., Homer, N. A survey of sequence alignment algorithms for next-generation sequencing. Brief. Bioinform. 11, 473-483 (2010).
37. Trapnell, C. et al. Differential analysis of gene regulation at transcript resolution with RNA-seq. Nat. Biotechnol. 31, 46-53 (2013).
38. Trapnell, C. et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28, 511-515 (2010).
39. Anders, S., Pyl, P.T., Huber, W. HTSeq: A Python framework to work with high-throughput sequencing data. bioRxiv doi:10.1101/002824 (20 February (2014).
40. ?abaj, P.P. et al. Characterization and improvement of RNA-Seq precision in quantitative transcript expression profiling. Bioinformatics 27, i383-i391 (2011).
41. Baldi, P., Brunak, S., Chauvin, Y., Andersen, C.A., Nielsen, H. Assessing the accuracy of prediction algorithms for classification: an overview. Bioinformatics 16, 412-424 (2000).
42. Shi, L. et al. The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models. Nat. Biotechnol. 28, 827-838 (2010).
43. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15-21 (2013).
44. Lawrence, M. et al. Software for computing and annotating genomic ranges. PLOS Comput. Biol. 9, e1003118 (2013).
45. Thierry-Mieg, D., Thierry-Mieg, J. AceView: a comprehensive cDNA-supported gene and transcripts annotation. Genome Biol. 7 (suppl. 1), S12 1-14 (2006).
46. Robinson, M.D., McCarthy, D.J., Smyth, G.K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139-140 (2010).
47. Tripathi, A.K. et al. Transcriptomic dissection of myogenic differentiation signature in caprine by RNA-Seq. Mech. Dev. 132, 79-92 (2014).
48. Bragg, L.M., Stone, G., Butler, M.K., Hugenholtz, P., Tyson, G.W. Shining a light on dark sequencing: characterising errors in Ion Torrent PGM data. PLoS Comput. Biol. 9, e1003031 (2013).
49. Lonsdale, J. et al. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580-585 (2013).
50. Bernstein, B.E. et al. The NIH Roadmap Epigenomics Mapping Consortium. Nat. Biotechnol. 28, 1045-1048 (2010).
51. Purcell, S.M. et al. A polygenic burden of rare disruptive mutations in schizophrenia. Nature 506, 185-190 (2014).
52. Pipes, L. et al. The non-human primate reference transcriptome resource (NHPRTR) for comparative functional genomics. Nucleic Acids Res. 41, D906-D914 (2013).
53. Morgan, M., Pages, H., Obenchain, V. Rsamtools: Binary alignment (BAM), variant call (BCF), or tabix file import. R package version 1.14.3 (2014).
54. Quinlan, A.R., Hall, I.M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841-842 (2010).
55. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078-2079 (2009).
56. Barnett, D.W., Garrison, E.K., Quinlan, A.R., Stromberg, M.P., Marth, G.T. BamTools: a C. API and toolkit for analyzing and managing BAM files. Bioinformatics 27, 1691-1692 (2011).
57. Faulhaber, J. Boost interval container library http://www.boost.org/doc/libs/1-55-0/libs/icl/doc/html/index.html (2010).
58. Law, C.W., Chen, Y., Shi, W., Smyth, G.K. Voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014).
59. Smyth, G.K. in Bioinformatics and Computational Biology Solutions Using R and Bioconductor (eds. Gentleman, R., Carey, V., Huber, W., Irizarry, R., Dudoit, S.) 397-420 (Springer, New York, 2005).
60. Robinson, M.D., Smyth, G.K. Small-sample estimation of negative binomial dispersion, with applications to SAGE data. Biostatistics 9, 321-332 (2008).
61. Robinson, M.D., Smyth, G.K. Moderated statistical tests for assessing differences in tag abundance. Bioinformatics 23, 2881-2887 (2007).
62. Dvinge, H., Bertone, P. HTqPCR: high-throughput analysis and visualization of quantitative real-time PCR data in R. Bioinformatics 25, 3325-3326 (2009).