Methods for time series analysis of RNA-seq data with application to human Th17 cell differentiation | Zendy

Tarmo Äijö | Zendy; Vincent L. Butty | Zendy; Zhi Chen | Zendy; Verna Salo | Zendy; Subhash Tripathi | Zendy; Christopher B. Burge | Zendy; Riitta Lahesmaa | Zendy; Harri Lähdesmäki | Zendy

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Methods for time series analysis of RNA-seq data with application to human Th17 cell differentiation

Author(s) -

Tarmo Äijö,

Vincent L. Butty,

Zhi Chen,

Verna Salo,

Subhash Tripathi,

Christopher B. Burge,

Riitta Lahesmaa,

Harri Lähdesmäki

Publication year - 2014

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btu274

Subject(s) - rna seq , computational biology , gene expression , biology , inference , negative binomial distribution , gene expression profiling , gene , rna , cellular differentiation , computer science , genetics , transcriptome , poisson distribution , mathematics , statistics , artificial intelligence

Gene expression profiling using RNA-seq is a powerful technique for screening RNA species' landscapes and their dynamics in an unbiased way. While several advanced methods exist for differential expression analysis of RNA-seq data, proper tools to anal.yze RNA-seq time-course have not been proposed.

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