IsoLasso: A LASSO regression approach to RNA-Seq based transcriptome assembly

Journal of Computational Biology

Volumn 18, Issue 11, 2011, Pages 1693-1707

  Li, Wei ^a   Feng, Jianxing ^b   Jiang, Tao ^a,c  

^a University of California Riverside   (United States)

^b TONGJI UNIVERSITY   (China)

^c TSINGHUA UNIVERSITY   (China)

Author keywords

algorithms; computational molecular biology; machine learning; probability; sequence analysis

Indexed keywords

ISOPROTEIN;

ALGORITHM; ANIMAL; ARTICLE; BIOLOGICAL MODEL; CELL CULTURE; COMPARATIVE STUDY; COMPUTER PROGRAM; COMPUTER SIMULATION; EXON; GENE EXPRESSION; GENE EXPRESSION PROFILING; GENETICS; HUMAN; METHODOLOGY; MOUSE; MULTIVARIATE ANALYSIS; REGRESSION ANALYSIS; SEQUENCE ANALYSIS;

ALGORITHMS; ANIMALS; CELLS, CULTURED; COMPUTER SIMULATION; EXONS; GENE EXPRESSION; GENE EXPRESSION PROFILING; HUMANS; MICE; MODELS, GENETIC; MULTIVARIATE ANALYSIS; PROTEIN ISOFORMS; REGRESSION ANALYSIS; SEQUENCE ANALYSIS, RNA; SOFTWARE;

EID: 80955139613     PISSN: 10665277     EISSN: None     Source Type: Journal    
DOI: 10.1089/cmb.2011.0171     Document Type: Article

References (40)

1. Au, K. F., Jiang, H., and Lin, L. et al. 2010. Detection of splice junctions from paired-end RNA-Seq data by splicemap. Nucleic Acids Res. 38, 4570-4578.
2. Birol, I., Jackman, S. D., Nielsen, C. B., et al. 2009. De novo transcriptome assembly with abyss. Bioinformatics 25, 2872-2877.
3. Cloonan, N., Forrest, A. R. R., Kolle, G., et al. 2008. Stem cell transcriptome profiling via massive-scale mRNA sequencing. Nat. Meth. 5, 613-619. (Pubitemid 351911866)
4. Efron, B., Hastie, T., Johnstone, L., et al. 2004. Least angle regression. Ann. Stat. 32, 407-499. (Pubitemid 41250302)
5. Feng, J., Li, W., and Jiang, T. 2010. Inference of isoforms from short sequence reads. Lect. Notes Comput Sci. 6044, 138-157.
6. Gustafsson, M., Hornquist, M., and Lombardi, A. 2005. Constructing and analyzing a large-scale gene-to-gene regulatory network-LASSO-constrained inference and biological validation. IEEE/ACM Trans. Comput. Biol. Bioinform. 2, 254-261. (Pubitemid 41560417)
7. Guttman, M., Garber, M., Levin, J. Z., et al. 2010. Ab initio reconstruction of cell type-specific transcriptomes in mouse reveals the conserved multi-exonic structure of lincRNAs. Nat. Biotech. 28, 503-510.
8. Haas, B. J., and Zody, M. C. 2010. Advancing RNA-Seq analysis. Nat. Biotech. 28, 421-423.
9. Hastie, T., Tibshirani, R., and Friedman, J. H. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, New York.
10. Hocking, R. R., and Leslie, R. N. 1967. Selection of the best subset in regression analysis. Technometrics 9, 531-540.
11. Hoerl, A. E., and Kennard, R. W. 1970. Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12, 55-67.
12. Holt, K. E., Parkhill, J., Mazzoni, C. J., et al. 2008. High-throughput sequencing provides insights into genome variation and evolution in Salmonella typhi. Nat. Genet. 40, 987-993.
13. Jiang, H., and Wong, W. H. 2009. Statistical inferences for isoform expression in RNA-Seq. Bioinformatics 25, 1026-1032.
14. Kim, S., Sohn, K.-A., and Xing, E. P. 2009. A multivariate regression approach to association analysis of a quantitative trait network. Bioinformatics 25, i204-i212.
15. Lee, S., Seo, C. H., Lim, B., et al. 2010. Accurate quantification of transcriptome from RNA-Seq data by effective length normalization. Nucleic Acids Res. 39, e9.
16. Li, J., Jiang, H., and Wong, W. H. 2010. Modeling non-uniformity in short-read rates in RNA-Seq data. Genome Biol. 11, R50.
17. Lister, R., O'Malley, R. C., Tonti-Filippini, J., et al. 2008. Highly integrated single-base resolution maps of the epigenome in Arabidopsis. Cell 133, 523-536.
18. Ma, S., Song, X., and Huang, J. 2007. Supervised group LASSO with applications to microarray data analysis. BMC Bioinform. 8, 60.
19. Marioni, J. C., Mane, S. M., et al. 2008. RNA-Seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res. 18, 1509-1517.
20. Morin, R., Bainbridge, M., Fejes, A., et al. 2008. Profiling the HeLa s3 transcriptome using randomly primed cDNA and massively parallel short-read sequencing. BioTechniques 45, 81-94.
21. Morozova, O., Hirst, M., and Marra, M. A. 2009. Applications of new sequencing technologies for transcriptome analysis. Annu. Rev. Genomics Hum. Genet. 10, 135-151.
22. Mortazavi, A., Williams, B. A., McCue, K., et al. 2008. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Meth. 5, 621-628. (Pubitemid 351911867)
23. Nagalakshmi, U., Wang, Z., Waern, K., et al. 2008. The transcriptional landscape of the yeast genome defined by RNA sequencing. Science 320, 1344-1349. (Pubitemid 351929471)
24. Paşaniuc, B., Zaitlen, N., and Halperin, E. 2010. Accurate estimation of expression levels of homologous genes in RNA-Seq experiments. Lect. Notes Comput. Sci. 6044, 397-409.
25. Park, M. Y., and Hastie, T. 2007. L1-regularization path algorithm for generalized linear models. J. R. Stat. Soc. Ser. B Stat. Methodol. 69, 659-677.
26. Richard, H., Schulz, M. H., Sultan, M., et al. 2010. Prediction of alternative isoforms from exon expression levels in RNA-Seq experiments. Nucleic Acids Res. 38, e112.
27. Sammeth, M., Lacroix, V., Ribeca, P., et al. 2010. The flux simulator. Available at: http://flux.sammeth.net. Accessed August 15, 2011.
28. Srivastava, S., and Chen, L. 2010. A two-parameter generalized Poisson model to improve the analysis of RNA-Seq data. Nucleic Acids Res. 38, e170.
29. The ENCODE Project Consortium. 2007. Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature 447, 799-816.
30. The Mathworks. 2004. Optimization Toolbox User's Guide. The Mathworks, Natick, MA.
31. Tibshirani, R. 1996. Regression shrinkage and selection via the LASSO. J. R. Stat. Soc. Ser. B Stat. Methodol. 58, 267-288.
32. Trapnell, C., Pachter, L., and Salzberg, S. L. 2009. Tophat: discovering splice junctions with RNA-Seq. Bioinformatics 25, 1105-1111.
33. Trapnell, C., Williams, B. A., Pertea, G., et al. 2010. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotech. 28, 511-515.
34. Wall, P. K., Leebens-Mack, J., Chanderbali, A., et al. 2009. Comparison of next generation sequencing technologies for transcriptome characterization. BMC Genomics 10, 347.
35. Wang, Z., Gerstein, M., and Snyder, M. 2009. RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10, 57-63.
36. Wheeler, D. A., Srinivasan, M., Egholm, M., et al. 2008. The complete genome of an individual by massively parallel DNA sequencing. Nature 452, 872-876. (Pubitemid 351550870)
37. Wilhelm, B. T., Marguerat, S., Stephen W., et al. 2008. Dynamic repertoire of a eukaryotic transcriptome surveyed at single-nucleotide resolution. Nature 453, 1239-1243. (Pubitemid 351913597)
38. Wu, T. T., Chen, Y. F., Hastie, T., et al. 2009. Genome-wide association analysis by LASSO penalized logistic regression. Bioinformatics 25, 714-721.
39. Yassour, M., Kaplan, T., Fraser, H. B., et al. 2009. Ab initio construction of a eukaryotic transcriptome by massively parallel mRNA sequencing. Proc. Natl. Acad. Sci. USA 106, 3264-3269.
40. Zou, H., and Hastie, T. 2005. Regularization and variable selection via the elastic net. J. R. Stat. Soc. Ser. B Stat. Methodol. 67, 301-320. (Pubitemid 40465877)