EBSeqHMM is a tool for the identification of isoforms with variable expression profiles over time. The EBSeqHMM algorithm uses an empirical Bayes mixture modeling and an auto-regressive hidden Markov model to assess the dependency of gene expression and conditions.
Statistics and probability; Gene expression; Molecular interactions, pathways and networks; RNA-Seq
Leng N, Li Y, McIntosh BE, Nguyen BK, Duffin B, Tian S, Thomson JA, Dewey CN, Stewart R, Kendziorski C "EBSeq-HMM: a Bayesian approach for identifying gene-expression changes in ordered RNA-seq experiments." Bioinformatics 2015; 31(16):2614-22 https://doi.org/10.1093/bioinformatics/btv193
PMID: 25847007
PMCID: PMC4528625
Huber W, Carey VJ, Gentleman R, Anders S, Carlson M, Carvalho BS, Bravo HC, Davis S, Gatto L, Girke T, Gottardo R, Hahne F, Hansen KD, Irizarry RA, Lawrence M, Love MI, MacDonald J1, Obenchain V, Oleś AK, Pagès H, Reyes A, Shannon P, Smyth GK, Tenenbaum D, Waldron L, Morgan M "Orchestrating high-throughput genomic analysis with Bioconductor." Nat Methods. 2015 Feb;12(2):115-21. https://doi.org/10.1038/nmeth.3252
PMID: 25633503
PMCID: PMC4509590
Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K, Hothorn T, Huber W, Iacus S, Irizarry R, Leisch F, Li C, Maechler M, Rossini AJ, Sawitzki G, Smith C, Smyth G, Tierney L, Yang JY, Zhang J. "Bioconductor: open software development for computational biology and bioinformatics." Genome Biol. 2004;5(10):R80. Epub 2004 Sep 15. https://doi.org/10.1186/gb-2004-5-10-r80
PMID: 15461798
PMCID: PMC545600
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