All our software are free and open source.

CONCISE

CONCISE (COnvolutional neural Network for CIS-regulatory Elements) is a modeling framework based on Google deep learning framework Tensorflow to model cis-regulatory elements, with a focus on post-transcriptional regulation (RNA stability, translation, etc.).

https://github.com/Avsecz/concise

GenoGAM

GenoGAM allows statistical analysis of genome-wide data with smooth functions using generalized additive models. It provides methods for the statistical analysis of ChIP-Seq data including inference of protein occupancy, and pointwise and region-wise differential analysis. Estimation of dispersion and smoothing parameters is performed by cross-validation. Scaling of generalized additive model fitting to whole chromosomes is achieved by parallelization over overlapping genomic intervals.

http://bioconductor.org/packages/devel/bioc/html/GenoGAM.html

STAN

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

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