Open Source Python Data Analytics Tools

A data visualization and processing tool for scanning probe microscopy (SPM, i.e. AFM, STM, MFM, SNOM/NSOM, ...) and profilometry data, useful also for general image and 2D data analysis.

This is a sophisticated & integrated simulation and analysis environment for dynamical systems models of physical systems (ODEs, DAEs, maps, and hybrid systems). It supports symbolic math, optimization, continuation, data analysis, biological apps...

The software package 'relax' is designed for the study of molecular dynamics through the analysis of experimental NMR data. Organic molecules, proteins, RNA, DNA, sugars, and other biomolecules are all supported. It supports exponential curve fitting for the calculation of the R1 and R2 relaxation rates, calculation of the NOE, reduced spectral density mapping, the Lipari and Szabo model-free analysis, study of domain motions via the N-state model and frame order dynamics theories using anisotropic NMR parameters such as RDCs and PCSs, the investigation of stereochemistry in dynamic ensembles, and the analysis of relaxation dispersion data.

A collection of tools for working with the comparative data analysis ontology including import/export facilities for common phylogenetic file formats, and also a triple-store framework.

The Collaborative Computational Project for NMR (CCPN) provides tools and knowledge to maximise the impact of the biological NMR studies. The CCPN software facilitates data analysis and software integration. The project actively promotes the exchange of knowledge and provides training and best practices for the NMR community. CCPN also has a leading role in the development of a NMR data-sharing standard and coordination of NMR instrumentation proposals for RCUK and BIS. The 28 partners of CCPN jointly cover all aspects of biomolecular NMR and together they promote excellence in science in their respective fields.

Capabilities of CorNetMap: 1. Read data as tab-delimited text file. Can be used for analysis of any data set beyond gene expression. 2. Capable of both two-dimensional and multidimensional data analysis. 3. Calculate Pearson correlation and cross-correlation for analysis data with phase difference. 4. Generate correlation Heat-map and draws network map. 5. Save correlation data as text file. How to use and doccumentation: https://sourceforge.net/projects/cornetmap/files/Documentation_corNetMap.pdf Sample data for testing: https://sourceforge.net/projects/cornetmap/files/Test%20Data/ Citation: Cite CornetMap as " Khaund, A. Bose, B. CorNetMap. https://sourceforge.net/projects/cornetmap " Credit: Abhigyan Khaund, Biplab Bose, IIT Guwahati

DataPrep v0.2 is a Tkinter-based GUI application/tool designed to assist users in data preprocessing, multicollinearity removal, and feature selection for a wide range of applications in Cheminformatics, Bioinformatics, Data Analysis, Feature Selection, Molecular Modeling, Machine Learning, and Quantitative-structure-property relationship (QSPR) studies. It includes functionality to load, process, and save datasets with support for different preprocessing & multicollinearity removal strategies with customizable parameter setting options.

During the past few years, whole exome sequencing has imposed itself for genetic research, largely due to its use for detection of causative mutations responsible for Mendelian disorders. As a consequence of their power and of the rapidly decreasing cost of these technologies, massive amount of exome sequencing data are generated and becoming available to a broadening community of scientists. However, these data remain difficult to analyze and interpret by the general scientific community, due to the limited bioinformatics resources that are currently freely available for their analysis and the partial information that they provide. Here, we present an interactive and free analysis package, ExAM, dedicated to whole exome sequencing data analysis. It is accessible to non-computer scientists and provides a complete set of information, as well as decision support to prioritize candidate disease causing variants.

<This project has been completely rewrote and transformed into a new one: https://sourceforge.net/projects/deday/. 2013/06/26> The graphic user interface for DEDA (DEmography Data Analysis), a scientific software package fitting survivalship data to a number of distributions using maximum likelihood (ML) method. Currently, Weibull (2p), Gompertz and Gompertz-Makeham are supported. IMPORTANT NOTICE: Only the GUI is provided here. In order to perform the analysis, one also need the DEDA computation core program. Please email a request to me, entitled: 'Request for DEDA computation core', if you wish to have a copy.

Pathomx is a workflow-based tool for the analysis and visualisation of experimental data. Initially created as a tool for metabolomic data analysis is has been extended and can now be used for any scientific and non-scientific data analysis.

RStatServer is a system for quickly developing and deploying web applications that integrate statistical computations and graphics. It is built using the Zope web application develeopment system and the open-source statistical data analysis environment R

SWATH-Auto System Analyzer Tool, SASA Tool, is a novel SWATH platform for non-targeted metabolomics data analysis with an accurate mass spectral library for metabolite identification using SWATH acquisition mode.

Zherlock has now changed name to SciCraft and no longer hosted on sourceforge. Visit our new site at http://www.scicraft.org

Processing of smallRNA-Seq data to gather biologically relevant information requires application of multiple statistical and bioinformatics tools from different sources, each focusing on a specific step of the analysis pipeline. The analytical workflow can be challenging for the continuous interventions by the operator, a critical factor when large numbers of datasets need to be analyzed at once. To allow a flexible and comprehensive analysis of smallRNA-Seq data we designed a novel modular pipeline, called iMir, integrating multiple open source modules and resource in an automated workflow, devising different statistical approaches to analyze data rigorously. iMir comprises also a Graphical User Interface (GUI), so that the pipeline is particularly suited for biologist and early stage bioinformaticians and produces both graphics and text outputs.