MOTIVATION: Target discovery and drug evaluation for diseases with complex mechanisms call for a streamlined chemical systems analysis platform. Currently available tools lack the emphasis on reaction kinetics, access to relevant databases, and algorithms to visualize perturbations on a chemical scale providing quantitative details as well streamlined visual data analytics functionality. RESULTS: CytoCopasi, a Maven-based application for Cytoscape that combines the chemical systems analysis features of COPASI with the visualization and database access tools of Cytoscape and its plugin applications has been developed. The diverse functionality of CytoCopasi through ab initio model construction, model construction via pathway and parameter databases KEGG and BRENDA is presented. The comparative systems biology visualization analysis toolset is illustrated through a drug competence study on the cancerous RAF/MEK/ERK pathway. AVAILABILITY AND IMPLEMENTATION: The COPASI files, simulation data, native libraries, and the manual are available on https://github.com/scientificomputing/CytoCopasi.

MOTIVATION: The pathway from genomics through proteomics and onto a molecular description of biochemical processes makes the discovery of drugs and biomaterials possible. A research framework common to genomics and proteomics is needed to conduct biomolecular simulations that will connect biological data to the dynamic molecular mechanisms of enzymes and proteins. Novice biomolecular modelers are faced with the daunting task of complex setups and a myriad of possible choices preventing their use of molecular simulations and their ability to conduct reliable and reproducible computations that can be shared with collaborators and verified for procedural accuracy. RESULTS: We present the foundations of Biomolecular Reaction and Interaction Dynamics Global Environment (BRIDGE) developed on the Galaxy platform that makes possible fundamental molecular dynamics of proteins through workflows and pipelines via commonly used packages, such as NAMD, GROMACS and CHARMM. BRIDGE can be used to set up and simulate biological macromolecules, perform conformational analysis from trajectory data and conduct data analytics of large scale protein motions using statistical rigor. We illustrate the basic BRIDGE simulation and analytics capabilities on a previously reported CBH1 protein simulation. AVAILABILITY AND IMPLEMENTATION: Publicly available at https://github.com/scientificomputing/BRIDGE and https://usegalaxy.eu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In this report, we address the question of combining nonlinearities of neurons into networks for modeling increasingly varying and progressively more complex functions. A fundamental approach is the use of higher-level representations devised by restricted Boltzmann machines and (denoising) autoencoders. We present the Denoising Autoencoder Self-Organizing Map (DASOM) that integrates the latter into a hierarchically organized hybrid model where the front-end component is a grid of topologically ordered neurons. The approach is to interpose a layer of hidden representations between the input space and the neural lattice of the self-organizing map. In so doing the parameters are adjusted by the proposed unsupervised learning algorithm. The model therefore maintains the clustering properties of its predecessor, whereas by extending and enhancing its visualization capacity enables an inclusion and an analysis of the intermediate representation space. A comprehensive series of experiments comprising optical recognition of text and images, and cancer type clustering and categorization is used to demonstrate DASOM's efficiency, performance and projection capabilities.

MOTIVATION: Complex carbohydrates play a central role in cellular communication and in disease development. O- and N-glycans, which are post-translationally attached to proteins and lipids, are sugar chains that are rooted, tree structures. Independent efforts to develop computational tools for analyzing complex carbohydrate structures have been designed to exploit specific databases requiring unique formatting and limited transferability. Attempts have been made at integrating these resources, yet it remains difficult to communicate and share data across several online resources. A disadvantage of the lack of coordination between development efforts is the inability of the user community to create reproducible analyses (workflows). The latter results in the more serious unreliability of glycomics metadata. RESULTS: In this paper, we realize the significance of connecting multiple online glycan resources that can be used to design reproducible experiments for obtaining, generating and analyzing cell glycomes. To address this, a suite of tools and utilities, have been integrated into the analytic functionality of the Galaxy bioinformatics platform to provide a Glycome Analytics Platform (GAP).Using this platform, users can design in silico workflows to manipulate various formats of glycan sequences and analyze glycomes through access to web data and services. We illustrate the central functionality and features of the GAP by way of example; we analyze and compare the features of the N-glycan glycome of monocytic cells sourced from two separate data depositions.This paper highlights the use of reproducible research methods for glycomics analysis and the GAP presents an opportunity for integrating tools in glycobioinformatics. AVAILABILITY AND IMPLEMENTATION: This software is open-source and available online at https://bitbucket.org/scientificomputing/glycome-analytics-platform CONTACTS: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.