PlanExp: intuitive integration of complex RNA-seq datasets with planarian omics resources.

MOTIVATION: There is an increasing amount of transcriptomic and genomic data available for planarians with the advent of both traditional and single-cell RNA sequencing technologies. Therefore, exploring, visualizing and making sense of all these data in order to understand planarian regeneration and development can be challenging. RESULTS: In this work, we present PlanExp, a web-application to explore and visualize gene expression data from different RNA-seq experiments (both traditional and single-cell RNA-seq) for the planaria Schmidtea mediterranea. PlanExp provides tools for creating different interactive plots, such as heatmaps, scatterplots, etc. and links them with the current sequence annotations both at the genome and the transcript level thanks to its integration with the PlanNET web application. PlanExp also provides a full gene/protein network editor, a prediction of genetic interactions from single-cell RNA-seq data, and a network expression mapper that will help researchers to close the gap between systems biology and planarian regeneration. AVAILABILITY AND IMPLEMENTATION: PlanExp is freely available at https://compgen.bio.ub.edu/PlanNET/planexp. The source code is available at https://compgen.bio.ub.edu/PlanNET/downloads. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PPaxe: easy extraction of protein occurrence and interactions from the scientific literature.

MOTIVATION: Protein-protein interactions (PPIs) are very important to build models for understanding many biological processes. Although several databases hold many of these interactions, exploring them, selecting those relevant for a given subject and contextualizing them can be a difficult task for researchers. Extracting PPIs directly from the scientific literature can be very helpful for providing such context, as the sentences describing these interactions may give insights to researchers in helpful ways. RESULTS: We have developed PPaxe, a python module and a web application that allows users to extract PPIs and protein occurrence from a given set of PubMed and PubMedCentral articles. It presents the results of the analysis in different ways to help researchers export, filter and analyze the results easily. AVAILABILITY AND IMPLEMENTATION: PPaxe web demo is freely available at https://compgen.bio.ub.edu/PPaxe. All the software can be downloaded from https://compgen.bio.ub.edu/PPaxe/download, including a command-line version and docker containers for an easy installation. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PlanNET: homology-based predicted interactome for multiple planarian transcriptomes.

Motivation: Planarians are emerging as a model organism to study regeneration in animals. However, the little available data of protein-protein interactions hinders the advances in understanding the mechanisms underlying its regenerating capabilities. Results: We have developed a protocol to predict protein-protein interactions using sequence homology data and a reference Human interactome. This methodology was applied on 11 Schmidtea mediterranea transcriptomic sequence datasets. Then, using Neo4j as our database manager, we developed PlanNET, a web application to explore the multiplicity of networks and the associated sequence annotations. By mapping RNA-seq expression experiments onto the predicted networks, and allowing a transcript-centric exploration of the planarian interactome, we provide researchers with a useful tool to analyse possible pathways and to design new experiments, as well as a reproducible methodology to predict, store, and explore protein interaction networks for non-model organisms. Availability and implementation: The web application PlanNET is available at https://compgen.bio.ub.edu/PlanNET. The source code used is available at https://compgen.bio.ub.edu/PlanNET/downloads. Contact: jabril@ub.edu. Supplementary information: Supplementary data are available at Bioinformatics online.