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Nov 222018
 

BioPerl 1.7.2

:: DESCRIPTION

Bioperl is a collection of Perl modules that facilitate the development of Perl scripts for bioinformatics applications. As such, it does not include ready to use programs in the sense that many commercial packages and free web-based interfaces do (e.g. Entrez, SRS). On the other hand, Bioperl does provide reusable Perl modules that facilitate writing Perl scripts for sequence manipulation, accessing of databases using a range of data formats and execution and parsing of the results of various molecular biology programs including Blast, clustalw, TCoffee, genscan, ESTscan and HMMER. Consequently, bioperl enables developing scripts that can analyze large quantities of sequence data in ways that are typically difficult or impossible with web based systems.

::DEVELOPER

BioPerl History

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows / Mac OsX
  • Perl

:: DOWNLOAD

BioPerl

:: MORE INFORMATION

Citation

An Introduction to BioPerl.
Stajich JE.
Methods Mol Biol. 2007;406:535-48.

Nov 222018
 

JABAWS 2.2

:: DESCRIPTION

JABAWS is a collection of web services for bioinformatics, and currently provides services that make it easy to access well-known multiple sequence alignment and protein disorder prediction programs from Jalview. JABAWS is free software which provides web services conveniently packaged to run on your local computer, server, cluster or Amazon EC2 instance. Services for multiple sequence alignment include Clustal Omega, Clustal W, MAFFT, MUSCLE, TCOFFEE and PROBCONS. Analysis services allow prediction of protein disorder with DisEMBL, IUPred, Ronn and GlobPlot; and calculation of amino acid alignment conservation with AACon.

::DEVELOPER

The Barton Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux/MacOsX/ Amazon EC2
  • Java

:: DOWNLOAD

 JABAWS

:: MORE INFORMATION

Citation

Bioinformatics. 2011 Jul 15;27(14):2001-2. doi: 10.1093/bioinformatics/btr304. Epub 2011 May 18.
Java bioinformatics analysis web services for multiple sequence alignment–JABAWS:MSA.
Troshin PV, Procter JB, Barton GJ.

Nov 222018
 

EMBOSS 6.6.0 / Jemboss 1.5

:: DESCRIPTION

EMBOSS (European Molecular Biology Open Software Suite) is a free Open Source software analysis package specially developed for the needs of the molecular biology (e.g. EMBnet) user community. The software automatically copes with data in a variety of formats and even allows transparent retrieval of sequence data from the web. Also, as extensive libraries are provided with the package, it is a platform to allow other scientists to develop and release software in true open source spirit. EMBOSS also integrates a range of currently available packages and tools for sequence analysis into a seamless whole.

Jemboss is a graphical user interface to EMBOSS.

EMBOSS Online Version

::DEVELOPER

EMBOSS team

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows/Mac OsX/ Linux
  • JAVA

:: DOWNLOAD

EMBOSS ; Jemboss

:: MORE INFORMATION

Citation:

EMBOSS: The European Molecular Biology Open Software Suite (2000)
Rice,P. Longden,I. and Bleasby,A.
Trends in Genetics 16, (6) pp276–277

Nov 222018
 

NCBI C++ Toolkit 21.0

:: DESCRIPTION

NCBI C++ Toolkit is a collection of C++ modules developed by the NCBI for writing bioinformatics software and applications. NCBI C++ Toolkit was developed for the production and distribution of GenBank, Entrez, BLAST, and related services by NCBI.

::DEVELOPER

NCBI

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

NCBI C++ Toolkit

:: MORE INFORMATION

Citation

The NCBI C++ Toolkit Book [Internet].
Vakatov D, editor.

Nov 222018
 

Chinook 1.2.2

:: DESCRIPTION

Chinook is a peer-to-peer (P2P) bioinformatics service. The goal of the Chinook platform is to facilitate exchange of analysis techniques within a local community and/or worldwide. Chinook operates by turning command-line applications into services which are broadcast over a virtual network. Currently, there are over 10 analysis services that have been made “Chinook-ready”. These range from alignment to regulation prediction algorithms. Furthermore, Chinook is designed to make it extremely easy to add new services.

::DEVELOPER

Canada’s Michael Smith Genome Sciences Centre

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows/MacOsX/Linux

:: DOWNLOAD

 Chinook

:: MORE INFORMATION

Nov 222018
 

Bioclipse 2.6.2

:: DESCRIPTION

Bioclipse is a free and open source workbench for the life sciences with a state-of-the-art plugin architecture, enabling powerful integration of existing software, and making it easy to customize for different needs.

Bioclipse has advanced functionality in fields such as cheminformatics, bioinformatics, semantic web, spectrum analysis, drug discovery, safety assessment and general chemistry education.

Features

  • The Bioclipse-QSAR feature makes descriptor calculation and formation of QSAR datasets easy and reproducible, adhering to the open QSAR-ML standard. Bioclipse is equipped with graphical editors and wizards, and supports a wide range of chemical descriptors.
  • Bioclipse contains advanced functionality to import, edit, and save chemical structures in various formats. Single molecules can be edited in an advanced 2D-editor, and the Chemistry Development Kit provides calculation of a wide range of properties, as well as generation of 2D and 3D structure, various fingerprints, InChI, and SMILES.
  • Bioclipse can process large collections of molecules, primarily SD-files, in the range of GB’s. Bioclipse not only allows for browsing such large libraries, but also has means to edit individual compounds and then continue browsing.
  • Bioclipse can calculate various properties on SD-files without opening them in the GUI. Such batch-calculations are faster, and can be carried out in teh background while continuing with other tasks.
  • Bioclipse has advanced, interactive 3D-visualization via the integrated Jmol application. Proteins and chemical compounds with 3D coordinates can be visualized with a multitude of rendering options.
  • Predicting metabolic sites is important in the drug discovery process to aid in rapid compound optimization.
  • The Bioclipse MetaPrint2D feature allows for rapid and accurate ranking of likely sites-of-metabolism, trained on phase-1 reactions in the Symyx Metabolite database. The predictions are very fast, less than 50 ms per molecule, and opens up for new ways of hypothesis testing in compound optimization. Calculations can be carried out on both individual molecules and on collections of compounds.
  • Writing scripts in the Bioclipse Scripting Language (BSL) to create reusable functions that can be used to automate repetitive tasks and shared with others. BSL is based on JavaScript, and is very powerful in chemical and biological analyses. Bioclipse makes use of Gist and MyExperiment for sharing scripts.
  • Easily download chemical structures from public repositories, such as PubChem, ChemSpider, and PDB.
  • Use one of the available Wizards or include a snippet in your script to access chemical resources online.

::DEVELOPER

Proteochemometric Group , Dept. of Pharmaceutical Biosciences, Uppsala University, Sweden, and the Cheminformatics and Metabolism Team at the European Bioinformatics Institute (EBI).

Bioclipse Developers

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows
  • Linux
  • MacOsX

:: DOWNLOAD

Bioclipse

:: MORE INFORMATION

If you use Bioclipse in your research, please cite:

Bioclipse: An open source workbench for chemo- and bioinformatics
Ola Spjuth, Tobias Helmus, Egon L Willighagen, Stefan Kuhn, Martin Eklund, Johannes Wagener, Peter Murray-Rust, Christoph Steinbeck, Jarl E.S. Wikberg
BMC Bioinformatics 2007, 8:59          doi:10.1186/1471-2105-8-59            Fulltext

and

Bioclipse 2: A scriptable integration platform for the life sciences
Ola Spjuth, Jonathan Alvarsson, Arvid Berg, Martin Eklund, Stefan Kuhn, Carl Mäsak, Gilleain Torrance, Johannes Wagener, Egon L Willighagen, Christoph Steinbeck and Jarl ES Wikberg
BMC Bioinformatics 2009, 10:397      doi:10.1186/1471-2105-10-397        Fulltext

 Posted by at 7:10 am

SQANTI 1.2 – Structural and Quality Annotation of Novel Transcript Isoforms

 Miscellaneous 9 views Comments Off on SQANTI 1.2 – Structural and Quality Annotation of Novel Transcript Isoforms
Nov 212018
 

SQANTI 1.2

:: DESCRIPTION

SQANTI is a pipeline for the in-depth characterization of isoforms obtained by full-length transcript sequencing, which are commonly returned in a fasta file format without any extra information about gene/transcript annotation or attribute description. SQANTI provides a wide range of descriptors of transcript quality and generates a graphical report to aid in the interpretation of the sequencing results.

::DEVELOPER

The Genomics of Gene Expression Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows/ MacOsX
  • Python
  • Perl
  • R package

:: DOWNLOAD

 SQANTI

:: MORE INFORMATION

Citation

SQANTI: extensive characterization of long-read transcript sequences for quality control in full-length transcriptome identification and quantification.
Tardaguila M, de la Fuente L, Marti C, Pereira C, Pardo-Palacios FJ, Del Risco H, Ferrell M, Mellado M, Macchietto M, Verheggen K, Edelmann M, Ezkurdia I, Vazquez J, Tress M, Mortazavi A, Martens L, Rodriguez-Navarro S, Moreno-Manzano V, Conesa A.
Genome Res. 2018 Feb 9. doi: 10.1101/gr.222976.117. [Epub ahead of print] Erratum in: Genome Res. 2018 Jul;28(7):1096.
PMID: 29440222

tappAS 0.99.09 – Understand the Functional Implications of Alternative Splicing

 Miscellaneous 6 views Comments Off on tappAS 0.99.09 – Understand the Functional Implications of Alternative Splicing
Nov 212018
 

tappAS 0.99.09

:: DESCRIPTION

tappAS is a Java GUI application for the analysis of RNA-Seq data down to the isoform level.It provides a comprehensive set of data analysis, visualization, filtering, and hoc query tools.It will run on most modern computers, provided they have enough computational resources and storage.

::DEVELOPER

The Genomics of Gene Expression Lab

:: SCREENSHOTS

:: REQUIREMENTS

  • Linux / Windows/ MacOsX
  • Java
  • R package

:: DOWNLOAD

 tappAS

:: MORE INFORMATION

 

CREAM 1.1.1 – Clustering of Genomic Regions Analysis Method

 Cluster Analysis 6 views Comments Off on CREAM 1.1.1 – Clustering of Genomic Regions Analysis Method
Nov 212018
 

CREAM 1.1.1

:: DESCRIPTION

CREAM (Clustering of Genomic Regions Analysis Method) provides a new method for identification of clusters of genomic regions within chromosomes. Primarily, it is used for calling clusters of cis-regulatory elements (COREs). ‘CREAM’ uses genome-wide maps of genomic regions in the tissue or cell type of interest, such as those generated from chromatin-based assays including DNaseI, ATAC or ChIP-Seq. ‘CREAM’ considers proximity of the elements within chromosomes of a given sample to identify COREs in the following steps: 1) It identifies window size or the maximum allowed distance between the elements within each CORE, 2) It identifies number of elements which should be clustered as a CORE, 3) It calls COREs, 4) It filters the COREs with lowest order which does not pass the threshold considered in the approach.

::DEVELOPER

Princess Margaret Bioinformatics and Computational Genomics Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • R

:: DOWNLOAD

 CREAM

:: MORE INFORMATION

 

Nov 212018
 

QualiMap 2.2.1

:: DESCRIPTION

Qualimap is a platform-independent application written in Java and R that provides both a Graphical User Inteface (GUI) and a command-line interface to facilitate the quality control of alignment sequencing data.

::DEVELOPER

QualiMap Team

:: SCREENSHOTS

QualiMap

:: REQUIREMENTS

  • Linux / Windows/ MacOsX
  • Java
  • R package

:: DOWNLOAD

 Qualimap

:: MORE INFORMATION

Citation

Qualimap 2: advanced multi-sample quality control for high-throughput sequencing data.
Okonechnikov K, Conesa A, García-Alcalde F.
Bioinformatics. 2015 Oct 1. pii: btv566

Bioinformatics. 2012 Oct 15;28(20):2678-9. doi: 10.1093/bioinformatics/bts503. Epub 2012 Aug 22.
Qualimap: evaluating next-generation sequencing alignment data.
García-Alcalde F, Okonechnikov K, Carbonell J, Cruz LM, Götz S, Tarazona S, Dopazo J, Meyer TF, Conesa A.