##### RKWard has moved! ##### Find the most recent news and downloads at http://rkward.kde.org . RKWard aims to provide an easily extensible, easy to use IDE/GUI for R. RKWard tries to combine the power of the R-language with the (relative) ease of use of commercial statistics tools.
ANDES. This is a library and a set of applications that can be used to analyze the results of deep sequencing results. (See Li et al.: ANDES: Statistical tools for the ANalyses of DEep Sequencing. BMC Research Notes 2010 3:199.)
YANG (Yet Another Network Generator - Java) enables you to generate social networks given various social rules observed in the real population. Uses: generate realistic networks to be used in individual-centric models, teaching or benchmarking.
Differential Expression Analysis for Pathways
This project contains the source code associated with the PLoS Computational Biology publication: "Differential Expression Analysis for Pathways". The paper text can be found here: http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002967
Open, extensible web-based collaborative platform for microarray gene expression, sequence and PPI data analysis, exposing distinct chainable components for clustering, pattern discovery, statistics (thru R), machine-learning algorithms and visualization
A comprehensive and flexible quantification tool for proteomics data
PANDA is a comprehensive and flexib tool for quantitative proteomics data analysis, which is developed based on our solid foundations in quantitative proteomics for years. Several novelties have been implemented in it. First, we implement the advantage algorithms of LFQuant (Proteomics 2012, 12, (23-24), 3475-84) and SILVER (Bioinformatics 2014, 30, (4), 586-7) into PANDA. Second, we consider the state-of-art concept of quantification reliability in this quantitative workflow. On the levels of spectra, peptides and proteins, PANDA works out a few quantitative filters and new scores for quantification confidence. Third, PANDA is designed for processing proteomics big data in parallel.
ANOSE is a web application (a web site) that allows software engineers to collect information about software projects and perform statistical analysis on the data collected. A key goal in ANOSE is to provide an easy to use user interface and efficient da
BigBang/Horizon is a proteomics data analysis pipeline with focus on the shotgun LC/MSMS workflow.
Toolchain for quantification of fluorescence intensity and morphological parameters in single cells using microscope based cytometry.
Units for computational cybernetics
CyberUnits is a cross-platform class library for rapid development of high-performance computer simulations in life sciences. It supports modelling for biomedical cybernetics and systems biology with Object Pascal, S and Matlab.
The Dataverse Network is a Java EE5 app that enables researchers to share data on-line. It provides data citation standards, facilitates preservation, distribution and replication of data, and includes statistical analysis. More at: http://thedata.org
We have moved, the project is now at https://github.com/jlaake/mrds
Hoea is a python module for hierarchical ontology enrichment analysis, which facilitated GO (Gene Ontology)/KO (KEGG Orthology) enrichment analysis at any desktop.
MICROSATELIGHT is a Perl/Tk graphical user interface (GUI) that facilitates several tasks when scoring microsatellites.
This is an R package for the Mass spectrometry Research. The MrR can be used to analyze the mass spectrometry data for baseline correction, denoising, peak detection, peak alignment, peak normalization, and biomarker discovery.
Oce, an R package for oceanographic analysis, has moved to github, at http://dankelley.github.com/oce/
Open Metaheuristic (oMetah) is a library aimed at the conception and the rigourous testing of metaheuristics (i.e. genetic algorithms, simulated annealing, ...). The code design is separated in components : algorithms, problems and a test report generator
TCP Experiment Automation Controlled Using Python
TEACUP automates many aspects of running TCP performance experiments in a specially-constructed physical testbed. TEACUP enables repeatable testing of different TCP algorithms over a range of emulated network path conditions, bottleneck rate limits and bottleneck queuing disciplines. TEACUP utilises a text-based configuration file to define experiments as combinations of parameters specifying desired network path and end host conditions. When multiple values are provided (e.g. for TCP congestion control algorithm), an experiment is made up of multiple tests. For each experiment and test, TEACUP collects a range of data, such as tcpdump files of traffic seen or TCP stack information (e.g. using Web10G). TEACUP also collects a variety of metadata from the end hosts and bottleneck router, such as the actual OS/kernel version(s) used. TEACUP also provides some simple tools for analysing the results of experiments, such as plotting a flow's experienced RTT over time.
Decima is a database that was designed to support time-series data mining. It consists of PostgreSQL custom type definition, implementation of GiST index for that type and snowflake database schema.
dynGraph est un module de FactoMineR qui permet de manipuler des graphiques interactifs.
kinfit and mkin are R extensions that perform kinetic evaluation of chemical degradation data for deriving modelling and persistence parameters in the context of risk assessment of chemical substances. Development has moved to R-Forge.
JAGS module for the piecewise exponential distribution
New module built for those users interested in the BUGS language to develop a Bayesian analysis for a model assuming the piecewise exponential distribution. The module is an extension to the open-source program JAGS. The PE distribution is widely used in survival and reliability researches, and currently can only be implemented in JAGS through methods to indirectly specify likelihoods. This module provides a more straightforward implementation. Authors: Vinícius D. Mayrink, João D.N. Duarte and Fábio N. Demarqui.
vipR is a program to screen for sequence variants (SNPs, deletions) in sequence data generated by high-throughput-sequencing platforms. Information on this and other projects can be found on: http://www.altmann.eu