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... (EFA) or the regularized alternating least squares (REGALS) methods. Active source code is now maintained on github: https://github.com/jbhopkins/bioxtasraw To install: Check the instructions available at: http://bioxtas-raw.readthedocs.io/en/latest/install.html and in the Files tab. User guides: RAW guides are available at: http://bioxtas-raw.readthedocs.io/ and in the Files tab. To contact us, see: https://bioxtas-raw.readthedocs.io/en/latest/help.html

An open source framework for LC-MS based proteomics and metabolomics. OpenMS offers data structures and algorithms for the processing of mass spectrometry data. The library is written in C++. Our source code and wiki lives on GitHub (https://github.com/OpenMS/OpenMS).

Turku Event Extraction System (TEES) is a free and open source natural language processing system developed for the extraction of events and relations from biomedical text. It is written mostly in Python, and should work in generic Unix/Linux environments. Currently, the TEES source code repository still remains on GitHub at http://jbjorne.github.com/TEES/ where there is also a wiki with more information.

Biskit is a python library for structural bioinformatics research. It simplifies the analysis of macromolecular structures, protein complexes, and molecular dynamics trajectories and offers a platform for the rapid integration of external programs. PLEASE NOTE: The Biskit source code as well as any later releases are now hosted on https://github.com/graik/biskit The sourceforge repo is only kept here for reference.

Moose is the core of a modern software platform for the simulation of neural systems ranging from subcellular components and biochemical reactions to complex models of single neurons, large networks, and systems-level processes. We have moved Github.com. This should be your source for the latest version of the code.

The Structural Proteomics Application Development Environment is a Python tool kit for developing and deploying bioinformatics applications. Handles graphics, analysis, and modeling of protein sequence and structure. Source and Win installers available. SPADE source code can be cloned from http://www.github.com/deaconjs/SPADE.

This is the backend source code of the Atlas2-Cloud pipeline on Amazon which starts and terminates worker nodes, runs analysis and monitors worker instances. It is PRIMARILY meant to be used through Amazon Web Services (AWS) management console by looking for the public Atlas2-Cloud machine image. Alternatively, the code can also be used for running the pipeline from your computer. By installing this software on your computer you will be setting up your computer to act as the head node...

Biologic Institute's Stylus simulation software suite. (See the SVN repository for source code.)

The Protein Geometry Database hosts the development code for a flexible database for searching protein geometry, as well as a library for accessing this data for protein modeling & refinement programs.

""" E-Cell's source code repository has moved to GitHub! https://github.com/ecell The development of E-Cell now continues on GitHub. The Subversion repository was abandoned. """ E-Cell System is an object-oriented software suite for modeling, simulation, and analysis of large scale complex systems such as biological cells. It allows many components driven by multiple algorithms with different timescales to coexist in a model.

Pymerase is a tool intended to generate a python object model, relational database, and an object-relational model connecting the two. However it has been extended to also output webpages and can be easily extended to output whatever else you might like.

The purpose of this project is to make available a variety of tools written in Python.

Virtual machine/emulator; "holding pen" for self-replicating programs written in custom RISC assembly-like language, evolving via random point mutations and periodic fitness-based cullings. Inspired (like Avida) by Thomas Ray's alife simulator, Tierra

Code repository for the Laboratory for Genome Bioinformatics at Texas A&M. The LGB project was initiated primarily to support biologists at Texas A&M needing help with bioinformatics in order to use new genomic technologies.