BRIE: Transcriptome-wide splicing quantification in single cells

Genome Biology

Volumn 18, Issue 1, 2017, Pages

  Huang, Yuanhua ^a   Sanguinetti, Guido ^a  

^a UNIVERSITY OF EDINBURGH   (United Kingdom)

Author keywords

Differential splicing; Isoform estimate; Single cell RNA seq

Indexed keywords

TRANSCRIPTOME; ISOPROTEIN;

ANIMAL CELL; ARTICLE; BAYES THEOREM; BAYESIAN REGRESSION FOR ISOFORM ESTIMATION METHOD; CONTROLLED STUDY; DNA METHYLATION; EMBRYO; EXON; HUMAN; HUMAN CELL; MOUSE; NONHUMAN; RNA PROCESSING; RNA SEQUENCE; RNA SPLICING; SENSITIVITY ANALYSIS; SEQUENCE ANALYSIS; SIMULATION; STATISTICAL MODEL; ALTERNATIVE RNA SPLICING; GENETICS; HIGH THROUGHPUT SEQUENCING; SINGLE CELL ANALYSIS;

ALTERNATIVE SPLICING; BAYES THEOREM; EXONS; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HUMANS; PROTEIN ISOFORMS; RNA SPLICING; SEQUENCE ANALYSIS, RNA; SINGLE-CELL ANALYSIS; TRANSCRIPTOME;

EID: 85021264008     PISSN: 14747596     EISSN: 1474760X     Source Type: Journal    
DOI: 10.1186/s13059-017-1248-5     Document Type: Article

References (28)

1. Grün D, van Oudenaarden A. Design and analysis of single-cell sequencing experiments. Cell. 2015; 163(4):799-810.
2. Grün D, Lyubimova A, Kester L, Wiebrands K, Basak O, Sasaki N, et al. Single-cell messenger RNA sequencing reveals rare intestinal cell types. Nature. 2015; 525(7568):251-5.
3. Shalek AK, Satija R, Shuga J, Trombetta JJ, Gennert D, Lu D, et al. Single cell RNA seq reveals dynamic paracrine control of cellular variation. Nature. 2014; 510(7505):363.
4. Patel AP, Tirosh I, Trombetta JJ, Shalek AK, Gillespie SM, Wakimoto H, et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science. 2014; 344(6190):1396-401.
5. Brennecke P, Anders S, Kim JK, Kołodziejczyk AA, Zhang X, Proserpio V, et al. Accounting for technical noise in single-cell RNA-seq experiments. Nat Methods. 2013; 10(11):1093-5.
6. Wang ET, Sandberg R, Luo S, Khrebtukova I, Zhang L, Mayr C, et al. Alternative isoform regulation in human tissue transcriptomes. Nature. 2008; 456(7221):470-6.
7. Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, Van Baren MJ, et al. Transcript assembly and quantification by RNA-seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol. 2010; 28(5):511-5.
8. Katz Y, Wang ET, Airoldi EM, Burge CB. Analysis and design of RNA sequencing experiments for identifying isoform regulation. Nat Methods. 2010; 7(12):1009-15.
9. Huang Y, Sanguinetti G. Statistical modeling of isoform splicing dynamics from RNA-seq time series data. Bioinformatics. 2016; 32(19):2965-72.
10. Liu P, Sanalkumar R, Bresnick EH, Keleş S, Dewey CN. Integrative analysis with ChIP-seq advances the limits of transcript quantification from RNA-seq. Genome Res. 2016; 26(8):1124-33.
11. Xiong HY, Alipanahi B, Lee LJ, Bretschneider H, Merico D, Yuen RK, et al. The human splicing code reveals new insights into the genetic determinants of disease. Science. 2015; 347(6218):1254806.
12. Bray NL, Pimentel H, Melsted P, Pachter L. Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol. 2016; 34(5):525-7.
13. Wu AR, Neff NF, Kalisky T, Dalerba P, Treutlein B, Rothenberg ME, et al. Quantitative assessment of single-cell RNA sequencing methods. Nat Methods. 2014; 11(1):41-6.
14. Qiu X, Hill A, Packer J, Lin D, Ma YA, Trapnell C. Single-cell mRNA quantification and differential analysis with census. Nat Methods. 2017; 14(3):309-15.
15. Barrass JD, Reid JE, Huang Y, Hector RD, Sanguinetti G, Beggs JD, et al. Transcriptome-wide RNA processing kinetics revealed using extremely short 4tU labeling. Genome Biol. 2015; 16(1):1.
16. Shen S, Park JW, Lu Z-X, Lin L, Henry MD, Wu YN, et al. rMATS: robust and flexible detection of differential alternative splicing from replicate RNA-seq data. Proc Natl Acad Sci. 2014; 111(51):5593-601.
17. Scialdone A, Tanaka Y, Jawaid W, Moignard V, Wilson NK, Macaulay IC, et al. Resolving early mesoderm diversification through single-cell expression profiling. Nature. 2016; 535(7611):284-93.
18. Duymich CE, Charlet J, Yang X, Jones PA, Liang G. DNMT3B isoforms without catalytic activity stimulate gene body methylation as accessory proteins in somatic cells. Nat Commun. 2016; 7:11453.
19. Glaus P, Honkela A, Rattray M. Identifying differentially expressed transcripts from RNA-seq data with biological variation. Bioinformatics. 2012; 28(13):1721-8.
20. Welch JD, Hu Y, Prins JF. Robust detection of alternative splicing in a population of single cells. Nucleic Acids Res. 2016; 44(8):73.
21. Buettner F, Natarajan KN, Casale FP, Proserpio V, Scialdone A, Theis FJ, et al. Computational analysis of cell-to-cell heterogeneity in single-cell RNA sequencing data reveals hidden subpopulations of cells. Nat Biotechnol. 2015; 33(2):155-60.
22. Campbell K, Yau C. Ouija: Incorporating prior knowledge in single-cell trajectory learning using Bayesian nonlinear factor analysis. bioRxiv 060442. 2016.
23. Curado J, Iannone C, Tilgner H, Valcárcel J, Guigó R. Promoter-like epigenetic signatures in exons displaying cell type-specific splicing. Genome Biol. 2015; 16(1):1-16.
24. Pollard KS, Hubisz MJ, Rosenbloom KR, Siepel A. Detection of nonneutral substitution rates on mammalian phylogenies. Genome Res. 2010; 20(1):110-21.
25. ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012; 489(7414):57-74.
26. Sturgill D, Malone JH, Sun X, Smith HE, Rabinow L, Samson ML, et al. Design of RNA splicing analysis null models for post hoc filtering of Drosophila head RNA-seq data with the splicing analysis kit (Spanki). BMC Bioinform. 2013; 14(1):1.
27. Kass RE, Raftery AE. Bayes factors. J Am Stat Assoc. 1995; 90(430):773-95.
28. Verdinelli I, Wasserman L. Computing Bayes factors using a generalization of the Savage-Dickey density ratio. J Am Stat Assoc. 1995; 90(430):614-8.