Software

All our software are free and open source.

STAN

Model-based Gene Set Analysis (MGSA, Bauer et al. [1]) is a Bayesian modeling
approach for gene set enrichment.Model-based Gene Set Analysis (MGSA) is a Bayesian modeling approach for gene set enrichment. 

STAN implements bidirectional Hidden Markov Models (bdHMM), which are designed for studying directed genomic processes, such as gene transcription or DAN replication.

bdHMMs model a sequence of successive observations (e.g. ChIP or RNA measurements along the genome) by a discrete number of 'directed genomic states', which e.g. reflect distinct genome-associated complexes. Unlike standard HMM approaches, bdHMMs allow the integration of strand-specific (e.g. RNA) and non strand-specific data (e.g. ChIP).

http://bioconductor.org/packages/release/bioc/html/STAN.html

 

mgsa

Model-based Gene Set Analysis (MGSA, Bauer et al. [1]) is a Bayesian modeling
approach for gene set enrichment.Model-based Gene Set Analysis (MGSA) is a Bayesian modeling approach for gene set enrichment. 

MGSA is an effective alternative to classical gene set enrichment analysis.

Classical methods analyze each set in isolation. Because sets such as biological pathways often share genes with each other, the returned list of enriched sets is usually long and redundant. In contrast, MGSA takes set overlap into account by working on all sets simultaneously and substantially reduces the number of redundant sets.

http://www.bioconductor.org/packages/release/bioc/html/mgsa.html

 

genomeIntervals

An intuitive R package to perform operations on genomic intervals such as merging, detecting overlap, or computing distances between intervals.

http://www.bioconductor.org/packages/release/bioc/html/genomeIntervals.html

 

cellGrowth

Non-parametric growth curve fitting. Because text book parametric models just fail on real data. This R package allows estimation of physiologically relevant parameters (exponential growth rate, plateau) and provides convenient plotting function for 96-well plate format.

http://www.bioconductor.org/packages/release/bioc/html/cellGrowth.html

 

 

 

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