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.

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.