This section is dedicated to the output and results of research activities within the PrECISE project. For any requests or questions, please contact the coordinator.

Below we list some computational tools developed and/or applied in PrECISE.

TOOLBOX: TUDA has made a toolbox for network reconstruction publicly available via GitHub:

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

TOOLBOX: CellNetOptimizer (CellNOpt): 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.

TOOL: PHONEMeS: a tool to build logic models from discovery mass-spectrometry based Phosphoproteomic data.

SERVICE: PIMKL - Pathway Induced Multiple Kernel Learning: a novel methodology to reliably classify samples that can also help gain insights into the molecular mechanisms that underlie the classification. PIMKL exploits prior knowledge in the form of a molecular interaction network and annotated gene sets, by optimizing a mixture of pathway-induced kernels using a Multiple Kernel Learning (MKL) algorithm, an approach that has demonstrated excellent performance in different machine learning applications. Credentials can be requested via the login page.

SERVICE: INtERAcT - Interaction Network Inference from Vector Representations of Words: a novel approach to extract protein-protein interactions from a corpus of biomedical articles related to a broad range of scientific domains in a completely unsupervised way. INtERAcT exploits vector representation of words, computed on a corpus of domain specific knowledge, and implements a new metric that estimates an interaction score between two molecules in the space where the corresponding words are embedded. Credentials can be requested via the login page.

SERVICE: Chimæra - clonality inference from mutations across biopsies: is an optimization algorithm that accounts for the effects of copy number variations (CNVs) in multiple same-tumor biopsies to estimate both mutation frequencies and copy number of mutated alleles. We show that mutation-frequency estimates by Chimæra are consistently more accurate in unstable genomes. When studying profiles of multiple biopsies of a high-risk prostate tumor, we show that Chimæra inferences allow for reconstructing its clonal evolution. Credentials can be requested via the login page.

SERVICE: COSIFER: is a web based platform providing a service for inference of molecular networks using a consensus between state-of-the-art methodologies given molecular measurements and a list of molecular entities of interest. Intracellular networks regulate every kind of cellular decision such as differentiation, proliferation and apoptosis and when these control mechanisms fail, cancer and other diseases may arise. Complexity of these networks originates from the large number and various interactions of molecules involved. High-throughput technologies such as microarrays and RNA sequencing provide snapshots of the transcriptome and enable insights into the internal regulatory apparatus of a cell. However, inferring the topology of these networks and identifying its key regulators is a challenging task and international consortia have intensively worked on the development of computational methods tackling this problem. Despite the effort of comparison and development of gene regulatory network inference methods, the research community still lacks easy to access inference tools available to everyone. Credentials can be requested via the login page.

SERVICE: LongHorn predicts modulation of canonical regulators (or effectors), including miRNA, RBP, and TF, by lncRNAs. Leveraging large-scale cancer genomics datasets from TCGA, LongHorn identifies four models for lncRNA regulation (1) Decoy: binds and inhibits the activity of effectors by affecting their availability to regulate their protein-coding targets; (2) Co-factor: binds proximal promoters of protein-coding genes and alter their regulation by TFs; (3) Guide: facilitates regulation of protein-coding genes by TFs; (4) Switch: alters the activity of TFs and RBPs across multiple targets. LongHorn has been implemented in MATLAB. Please download it from here.