All our software are free and open source.
Spectralis
Spectralis is a method for de novo peptide sequencing building upon the task of bin reclassification, which assigns ion series to discretized m/z values even in the absence of a peak. This is implemented with a convolutional neural network layer connecting peaks in spectra spaced by amino acid masses. Based on bin reclassification, Spectralis predicts scores to assess the quality of peptide-spectrum matches using Levenshtein distance estimates. Furthermore, Spectralis consists of an evolutionary algorithm to fine-tune peptide-spectrum matches.
It is available on GitHub: https://github.com/gagneurlab/spectralis
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.
It is available on GitHub: 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.
It is available on GitHub: 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.).
It is available on GitHub: https://github.com/gagneurlab/concise
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.
It is available on Bioconductor: 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.
It is available on Bioconductor: http://bioconductor.org/packages/release/bioc/html/genomeIntervals.html