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Bioinformatics. 2014 Sep 15;30(18):2598-602. doi: 10.1093/bioinformatics/btu333. Epub 2014 Jun 3.

Next maSigPro: updating maSigPro bioconductor package for RNA-seq time series.

Author information

1
Statistics and Operational Research Department, University of Alicante, 03690, Alicante, Spain, Genomics of Gene Expression Laboratory, Prince Felipe Research Centre, 46012 Valencia, Spain and Applied Statistics, Operational Research and Quality Department, Polytechnic University of Valencia, 46020 Valencia, Spain.
2
Statistics and Operational Research Department, University of Alicante, 03690, Alicante, Spain, Genomics of Gene Expression Laboratory, Prince Felipe Research Centre, 46012 Valencia, Spain and Applied Statistics, Operational Research and Quality Department, Polytechnic University of Valencia, 46020 Valencia, Spain Statistics and Operational Research Department, University of Alicante, 03690, Alicante, Spain, Genomics of Gene Expression Laboratory, Prince Felipe Research Centre, 46012 Valencia, Spain and Applied Statistics, Operational Research and Quality Department, Polytechnic University of Valencia, 46020 Valencia, Spain.

Abstract

MOTIVATION:

The widespread adoption of RNA-seq to quantitatively measure gene expression has increased the scope of sequencing experimental designs to include time-course experiments. maSigPro is an R package specifically suited for the analysis of time-course gene expression data, which was developed originally for microarrays and hence was limited in its application to count data.

RESULTS:

We have updated maSigPro to support RNA-seq time series analysis by introducing generalized linear models in the algorithm to support the modeling of count data while maintaining the traditional functionalities of the package. We show a good performance of the maSigPro-GLM method in several simulated time-course scenarios and in a real experimental dataset.

AVAILABILITY AND IMPLEMENTATION:

The package is freely available under the LGPL license from the Bioconductor Web site (http://bioconductor.org).

PMID:
24894503
PMCID:
PMC4155246
DOI:
10.1093/bioinformatics/btu333
[Indexed for MEDLINE]
Free PMC Article

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