All our software are free and open source.

AbSplice

AbSplice predicts aberrant splicing across tissues. It combines sequence based machine learning models for variant effect prediction in splicing (i.e. MMSplice and SpliceAI) together with SpliceMap, a tissue-specific splice site annotation. If available, RNA-seq from accessible tissues such as blood or skin can be integrated for improved predictions.

https://github.com/gagneurlab/absplice

DROP

DROP is an integrative workflow to help researchers use RNA-Seq data in order to detect genes with aberrant expression (using OUTRIDER), aberrant splicing (using FRASER), and mono-allelic expression. It consists of three independent modules for each of those strategies.

It is available on GitHub: https://github.com/gagneurlab/drop

FRASER

FRASER identifies aberrant splicing events from an RNA-seq dataset. It is applied in clinical research to identify candidate disease-causing genes for patients affected with a rare disorder of unknown cause. It implements a denoising autoencoder for count fraction data.

It is available on GitHub: https://github.com/gagneurlab/FRASER

MMSplice

MMSplice is a machine learning model that predicts effects of genetic variants on splicing. It won the CAGI 5 exon skipping challenge (2018). It implements a modular modeling approach where modules are neural networks modeling individual gene regions.

It is available in Kipoi: https://github.com/kipoi/models/tree/master/MMSplice 

OUTRIDER

OUTRIDER identifies gene expression outliers from an RNA-seq dataset. It is applied in clinical research to identify candidate disease-causing genes for patients affected with a rare disorder of unknown cause. It implements a denoising autoencoder for count data.

It is available on Bioconductor: https://bioconductor.org/packages/release/bioc/html/OUTRIDER.html

Kipoi 

Kipoi (pronounce: kípi; from the Greek κήποι: gardens) is an API and a repository of ready-to-use trained models for regulatory genomics. It contains >2,000 different models, covering canonical predictive tasks in transcriptional and post-transcriptional gene regulation. Kipoi's API is implemented as a python package (github.com/kipoi/kipoi) and it is also accessible from the command line or R.

Main web page: https://kipoi.org

OCR-Stats 

Statistical analysis of Oxygen Consumption Rate measured by the Seahorse XF Analyzer. Automatic detection of outlier data points, estimation of bioenergetics measures, statistical testing for multi-plate experimental designs.   

https://github.com/gagneurlab/OCR-Stats

wBuild

"workflow Build" (or maybe Wachutka build?). Data analysis and reporting workflow management.

All R-markdown scripts of a project get compiled and rendered into a navigable web-page. Data and scripts dependencies are handled using snakemake, whereby the programmer enters snakemake rules in the header of the R-markdown scripts.

https://pypi.python.org/pypi/wbuild/1.0

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/gagneurlab/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