BioSoftwares

Bioclipse is a free, open source, workbench for chemo- and bioinformatics with powerful editing and visualization capabilities for molecules, sequences, proteins, spectra etc.

The Bioclipse project is aimed at creating a Java-based, open source, visual platform for chemo- and bioinformatics based on the Eclipse Rich Client Platform (RCP). Bioclipse, as any RCP application, is based on a plugin architecture that inherits basic functionality and visual interfaces from Eclipse, such as help system, software updates, preferences, cross-platform deployment etc.

Bioclipse will provide functionality for chemo- and bioinformatics, and extension points that easily can be extended by plugins to provide added functionality. The first version of Bioclipse includes a CDK-plugin (bc_cdk) to provide a chemoinformatic backend, a Jmol-plugin (bc_jmol) for 3D-visualization and a general logging plugin.

The major features are:

• Import and export in various file formats
• Visual editing of molecular 2D-structures
• 3D-visualization of molecules and proteins
• Editing and visualization of sequences and features (DNA, RNA, proteins etc)
• Graphing and editing of various types of spectra, e. g. NMR, MS
• Retrieval of resources (sequences, proteins, etc) from public data repositories
• Scripting of 3D-visualizations with syntax highlighting and content assistance
• PDB-editor with syntax highlighting for working with PDB files
• CMLRSS-viewer for downloading chemical content published on the web using RSS-feeds
• Chemtree for displaying a hierarchical view of molecular and macromolecular substructures
• Visualization of syandard chemical properties
• Powerful scripting language based on Mozilla Rhino for automating tasks
  Integrated, searchable help-system
• Connection with external programs, e. g. PyMol

Bioclipse is a rich client, which means it is run on your local computer but also gives the possibility to communicate with servers for data retrieval and computational services. The powerful plugin architecture is based on Eclipse[2], and results in a responsive, integrated user interface designed for simple and intuitive operations that at the same time is easy to extend with custom functionality.

OS: Linux, Unix and Windows

Licence: Open Source (EPL)

Home Page

Download Page

Homologous protein families share highly conserved sequence and structure regions that are frequent targets for comparative analysis of related proteins and families. Many protein families, such as the curated domain families in the Conserved Domain Database (CDD), exhibit similar structural cores. To improve accuracy in aligning such protein families, we propose a profile–profile method CORAL that aligns individual core regions as gap-free units.

CORAL computes optimal local alignment of two profiles with heuristics to preserve continuity within core regions. We benchmarked its performance on curated domains in CDD, which have pre-defined core regions, against COMPASS, HHalign and PSI-BLAST, using structure superpositions and comprehensive curator-optimized alignments as standards of truth. CORAL improves alignment accuracy on core regions over general profile methods, returning a balanced score of 0.57 for over 80% of all domain families in CDD, compared with the highest balanced score of 0.45 from other methods. Further, CORAL provides E-values to aid in detecting homologous protein families and, by respecting block boundaries, produces alignments with improved ‘readability’ that facilitate manual refinement.

OS: Windows, Mac OS

Licence: Freeware

Home Page

Download Page

Bioconductor is an open source and open development software project to provide tools for the analysis and comprehension of genomic data.

Bioconductor is based primarily on the R programming language, but does contain contributions in other programming languages. It has two releases each year that follow the biannual releases of R. At any one time there is a release version, which corresponds to the released version of R, and a development version, which corresponds to the development version of R. Most users will find the release version appropriate for their needs. In addition there are a large number of meta-data packages available that are mainly, but not solely, oriented towards different types of microarrays.

Most Bioconductor components are distributed as R packages, which are add-on modules for R. Initially most of the Bioconductor software packages focused primarily on DNA microarray data analysis. As the project has matured, the functional scope of the software packages broadened to include the analysis of all types of genomic data, such as SAGE, sequence, or SNP data.

Main Features:

# The R Project for Statistical Computing. R and the R package system provides a broad range of advantages to the Bioconductor project including:

* It contains a high-level interpreted language in which one can easily and quickly prototype new computational methods.
* It includes a well established system for packaging together software components and documentation.
* It can address the diversity and complexity of computational biology and bioinformatics problems in a common object-oriented framework.
* It provides to on-line computational biology and bioinformatics data sources.
* It supports a rich set of statistical simulation and modeling activities.
* It contains cutting edge data and model visualization capabilities.
* It has been the basis for pathbreaking research in parallel statistical computing.
* It is under very active development by a dedicated team of researchers with a strong commitment to good documentation and software design.

# Documentation and reproducible research.  Each Bioconductor package contains at least one vignette, which is a document that provides a textual, task-oriented description of the package’s functionality. These vignettes come in several forms. Many are simple “HowTo”s that are designed to demonstrate how a particular task can be accomplished with that package’s software. Others provide a more thorough overview of the package or might even discuss general issues related to the package. In the future, we are looking towards providing vignettes that are not specifically tied to a package, but rather are demonstrating more complex concepts. As with all aspects of the Bioconductor project, users are encouraged to participate in this effort.

# Statistical and graphical methods. The Bioconductor project aims to provide access to a wide range of powerful statistical and graphical methods for the analysis of genomic data. Analysis packages are available for: pre-processing Affymetrix and cDNA array data; identifying differentially expressed genes; graph theoretical analyses; plotting genomic data. In addition, the R package system itself provides implementations for a broad range of state-of-the-art statistical and graphical techniques, including linear and non-linear modeling, cluster analysis, prediction, resampling, survival analysis, and time-series analysis.

# Annotation. The Bioconductor project provides software for associating microarray and other genomic data in real time to biological metadata from web databases such as GenBank, LocusLink and PubMed (annotate package). Functions are also provided for incorporating the results of statistical analysis in HTML reports with links to annotation WWW resources.
Software tools are available for assembling and processing genomic annotation data, from databases such as GenBank, the Gene Ontology Consortium, LocusLink, UniGene, the UCSC Human Genome Project (AnnotationDbi package).
Data packages are distributed to provide mappings between different probe identifiers (e.g. Affy IDs, LocusLink, PubMed). Customized annotation libraries can also be assembled.

# Bioconductor short courses. The Bioconductor project has developed a program of short courses on software and statistical methods for the analysis of genomic data. Courses have been given for audiences with backgrounds in either biology or statistics. All course materials (lectures and computer labs) are available on the WWW. Customized short courses may also be designed for interested parties.

# Open source. The Bioconductor project has a commitment to full open source discipline, with distribution via a SourceForge-like platform. All contributions are expected to exist under an open source license such as Artistic 2.0, GPL2, or BSD. There are many different reasons why open–source software is beneficial to the analysis of microarray data and to computational biology in general. The reasons include:
* To provide full access to algorithms and their implementation
* To facilitate software improvements through bug fixing and software extension
* To encourage good scientific computing and statistical practice by providing appropriate tools and instruction
* To provide a workbench of tools that allow researchers to explore and expand the methods used to analyze biological data
* To ensure that the international scientific community is the owner of the software tools needed to carry out research
* To lead and encourage commercial support and development of those tools that are successful
* To promote reproducible research by providing open and accessible tools with which to carry out that research (reproducible research is distinct from independent verification)

OS: Linux, OS X, and Windows

Licence: Open Source (Artistic 2.0, GPL2, or BSD contributions)

Home Page

Download Page