Resources

Software & Tools

BioServices

BioServices is a Python package that provides access to many Bioinformatics Web Services (e.g., UniProt) and a framework to easily implement Web Service wrappers (based on WSDL/SOAP or REST protocols).

Bioservices is described in (Cokelaer et al., Bioinformatics 2013) and the package is available on the Pypi repository:  BioServices

 

CellNetOptimizer

CellNetOptimizer (CellNOpt) is a toolbox for creating logic-based models of signal transduction networks, and training them against high-throughput biochemical data, and is freely available both for R and matlab.

CellNOpt is described in (Terfve et al., BMC Sys Bio, 2012) and hosted at www.cellnopt.org

 

Cyrface

Cyrface establishes an interface between R and Cytoscape by using different Java-R libraries, e.g. Rserve, RCaller. Cyrface can be used as a Cytos cape plug-in, e.g. to run R commands within Cytoscape, or used as a library to allow your plug-in to connect to R.

Cyrface is described in (Gonçalves et al., F1000Research, 2013) and hosted at http://www.ebi.ac.uk/saezrodriguez/cno/cyrface/

 

CySBGN

CySBGN is a Cytoscape plug-in that extends the use of Cytoscape visualization and analysis features to SBGN maps. CySBGN adds support to Cytoscape to import, export, visualize, validate and analyse SBGN maps.

CySBGN is described in (Gonçalves et al, BMC Bioinformatics, 2013), and hosted at http://www.ebi.ac.uk/saezrodriguez/cysbgn/

 

DREAMTools

DREAMTools provides  the code used in the scoring of DREAM challenges that pose fundamental questions about system biology and translational medicine.
 
DREAMTools is described in (Cokelaer et al., F1000 Research 2016); the code is hosted at github.com/dreamtools and documented at http://dreamtools.readthedocs.io

 

DrugVsDisease

DrugVsDisease (DvD) provides a pipeline, available through R or Cytoscape, for the comparison of drug and disease gene expression profiles from public microarray repositories.

DvD is described in (Pacini et al., Bioinformatics 2013) and hosted at http://www.ebi.ac.uk/saezrodriguez/DVD/

 

GDSCTools

GDSCTools is an open-source Python library dedicated to the study pharmacogenomic relationships in the context of the GDSC (Genomics of Drug Sensitivity in Cancer) project. The main developer is Thomas Cokelaer (Institut Pasteur), and it is a joint effort with the groups of Mathew Garnett (Sanger Institute) and Julio Saez-Rodriguez.

GDSCTools is hosted at https://github.com/CancerRxGene/gdsctools and documented at http://gdsctools.readthedocs.io/en/master/.

 

KinAct

KinAct is a python package with different computational methods to infer kinase activities from phosphoproteomics datasets.

KinAct is described in (Wirbel et al. Meth. Mol. Biol., in press; available also in Biorxiv) and hosted at https://github.com/saezlab/kinact

 

MEIGO

MEIGO is a global optimization toolbox that includes a number of metaheuristic methods as well as a Bayesian inference method for parameter estimation. It is developed jointly with the group of Julio Banga.

MEIGO is described in (Egea et al, BMC Bioinformatics, 2014), and hosted at http://gingproc.iim.csic.es/meigo.html

 

OmniPath

OmniPath is a comprehensive collection of literature curated human signaling pathways accompanied by pypath, a powerful Python module for molecular networks and pathways analysis.

OmniPath is hosted at http://www.omnipathdb.org.

 

Plant PhysioSpace

Plant PhysioSpace enables the utilization of massive gene expresion data sets from heterogeneous soures to assess the type and quality of lab data derived from cell cultures, tissues or plants.

Plant PhysioSpace is hosted at http://plabipd.de/physiospace/.

 

Proteostasis Profiler (Pro2

Pro2 provides tools for the quantitative visual exploration of Proteostasis Network (PN) gene expression changes in human diseases, including heat maps, polar plots, and interactome-guided 3D topographic maps. 

Pro2 is hosted at http://www.proteostasys.org.

Data & Scripts

Lenz et al, Scientific Reports 2016

Datasets and R scripts accompanying Lenz et al. “Principal components analysis and the reported low intrinsic dimensionality of gene expression microarray data”, Sci Rep. 2016 Jun 2;6:25696. doi: 10.1038/srep25696

The file “Lenz_2016_Rcode.zip” is available for download here and includes:

  • Preprocessed dataset of 7100 microarray samples (Affymetrix Human U133 Plus 2.0) from 192 different tissues, cell types, disease states, or cell lines
  • Script for reproduction of all figures included in the paper.