Tag: Framework

SpeedSeq v0.1.2 – Framework for Rapid Genome Analysis and Interpretation

SpeedSeq v0.1.2

:: DESCRIPTION

SpeedSeq is an open-source genome analysis platform that accomplishes alignment, variant detection and functional annotation of a 50× human genome in 13 h on a low-cost server and alleviates a bioinformatics bottleneck that typically demands weeks of computation with extensive hands-on expert involvement.

::DEVELOPER

The Quinlan Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Python

:: DOWNLOAD

SpeedSeq

:: MORE INFORMATION

Citation:

Nat Methods. 2015 Oct;12(10):966-8. doi: 10.1038/nmeth.3505. Epub 2015 Aug 10.
SpeedSeq: ultra-fast personal genome analysis and interpretation.
C Chiang, R M Layer, G G Faust, M R Lindberg, D B Rose, E P Garrison, G T Marth, A R Quinlan, and I M Hall.

RNALYZER – RNA Structural Comparison Framework

RNALYZER

:: DESCRIPTION

RNAlyzer is a computational method for comparison of RNA 3D models with the reference structure and for discrimination between the correct and incorrect models.

::DEVELOPER

The Bioinformatics Group, Poznan University of Technology.

:: SCREENSHOTS

RNALYZER

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • Java
  • Java3D
  • OpenGL

:: DOWNLOAD

 RNALYZER

 :: MORE INFORMATION

Citation

Nucleic Acids Res. 2013 Jul;41(12):5978-90. doi: 10.1093/nar/gkt318.
RNAlyzer–novel approach for quality analysis of RNA structural models.
Lukasiak P1, Antczak M, Ratajczak T, Bujnicki JM, Szachniuk M, Adamiak RW, Popenda M, Blazewicz J.

Bio-jETI 1.0 – Framework for Bioinformatics Workflows

Bio-jETI 1.0

:: DESCRIPTION

Bio-jETI is a service platform for interdisciplinary work on biological application domains. It uses the jETI(Java Electronic Tool Integration Platform) service integration technology for remote tool integration and the jABC framework as a graphical workflow modeling tool. Bio-jETI, domain experts, like biologists who are not trained in computer science, can directly define complex service orchestrations as workflow models and use efficient and complex bioinformatics tools in a simple and intuitive way.

::DEVELOPER

the Chair for Programming Systems of Dortmund University of Technology.

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows/ Linux /MacOsX
  • Java

:: DOWNLOAD

 Bio-jETI

:: MORE INFORMATION

Citation

Bio-jETI: a framework for semantics-based service composition.
Lamprecht AL, Margaria T, Steffen B.
BMC Bioinformatics. 2009 Oct 1;10 Suppl 10:S8.

RedoxMech – Microscopic Kinetics Modeling framework for Oxidoreductases (EC 1)

RedoxMech

:: DESCRIPTION

RedoxMech is a new language extension of the xCellerator reaction modeling software based in Mathematica that allows easier creation of EC 1 model from its complex mathematical formalism.

::DEVELOPER

Institute for Genomics and Bioinformatics, University of California

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

  RedoxMech

:: MORE INFORMATION

Citation:

Bioinformatics. 2013 May 15;29(10):1299-307. doi: 10.1093/bioinformatics/btt140. Epub 2013 Apr 23.
A unifying kinetic framework for modeling oxidoreductase-catalyzed reactions.
Chang I, Baldi P.

GAEMR 1.0.1 – Assembly Analysis Framework

GAEMR 1.0.1

:: DESCRIPTION

GAEMR (Genome Assembly Evaluation Metrics and Reportin) is a complete genome analysis package that helps you evaluate and report on a genome assembly’s completeness, correctness, and contiguity.

::DEVELOPER

Broad Institute

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 GAEMR

:: MORE INFORMATION

Pygr 0.82 – Python Graph Database Framework for Bioinformatics

Pygr 0.82

:: DESCRIPTION

pygr is a bioinformatics toolkit for sequence analysis and comparative genomics. pygr is highly scalable (e.g. one can easily query multi-genome alignments) and easy to use

Pygr is open source software to develop graph database interfaces for the popular Python language  to make it easy to do powerful sequence and comparative genomics analyses, even with extremely large multi-genome data sets. pygr includes:

  • Code for interacting with sequence databases, search methods such as BLAST, repeat-masking, megablast, etc.;
  • Querying and working with sequence annotation databases and sequence alignment datasets;
  • A data namespace for accessing a given resource with seamless data relationship management.
  • Easy data sharing that includes transparent access over network protocols.
  • High performance graph representation and query of interval-based data.

::DEVELOPER

Chris Lee, Dept. of Chemistry & Biochemistry,UCLA

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/Windows/Mac OsX
  • Python

:: DOWNLOAD

Pygr

:: MORE INFORMATION

GUILD – Genes Underlying Inheritance Linked Disorders framework

GUILD

:: DESCRIPTION

GUILD (Genes Underlying Inheritance Linked Disorders) is a network-based prioritization framework to unveil genes associated with a disease phenotype (disease-genes). The sofware exploits several communication mechanisms between disease-genes emerging from the topology of the interaction network. GUILD consists of implementations of 8 algorithms: NetScore, NetZcore, NetShort, NetCombo, fFlow, NetRank, NetWalk and NetProp.

::DEVELOPER

Structural BioInformatics Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • R package

:: DOWNLOAD

  GUILD

:: MORE INFORMATION

Citation

Guney E, Oliva B.
Exploiting Protein-Protein Interaction Networks for Genome-wide Disease-Gene Prioritization.
PLoS ONE, 2012, 7(9): e43557.

FRED 1.0 – Framework for T-cell Epitope Detection

FRED 1.0

:: DESCRIPTION

FRED is a framework for T-cell epitope detection that offers consistent, easy, and simultaneous access to well established prediction methods for MHC binding and antigen processing. FRED can handle polymorphic proteins and offers analysis tools to combine, benchmark, or compare different methods. It is implemented in Python in a modular way and can easily be extended by user defined methods.

DEVELOPER

the Kohlbacher lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ Windows with cygwin / MacOsX
  • Python

:: DOWNLOAD

 FRED

:: MORE INFORMATION

Citation:

Feldhahn, M, Dönnes, P, Thiel, P, and Kohlbacher, O (2009).
FRED – A Framework for T-cell Epitope Detection
Bioinformatics, 25(20):2758-9.

CoaCC 1.0.1 – Simulate Case-control Study using Coalescent Framework

CoaCC 1.0.1

:: DESCRIPTION

CoaCC simulates a case-control study using a coalescent framework. It assumes a haploid sample of cases and a second haploid sample of controls. Of these two samples the genealogy is generated, dependent on the user-specified population history. From this genealogy a distribution of marker-haplotypes is generated by allowing for marker-mutation and recombinations between marker and gene as well as between markers.

::DEVELOPER

Sebastian Zöllner @ the Center for Statistical Genetics

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux /  Windows / MacOsX
  • C Complier

:: DOWNLOAD

 CoaCC

:: MORE INFORMATION

If you use CoaCC please e-mail szoellne@umich.edu or fill out the registration form.

BioCocoa 2.2.2 – Cocoa Framework for Bioinformatics

BioCocoa 2.2.2

:: DESCRIPTION

BioCocoa is an open source Cocoa framework for bioinformatics written in Objective-C. We intend to provide Cocoa programmers with a full suite of tools for handling and manipulating biological sequences. Cocoa is a great framework for rapid application development and it is therefore often used to create innovative bioscientific apps. To speed up development even more, BioCocoa wants to offer reusable Cocoa classes that are specific for molecular biology and biofinformatics. At this time, BioCocoa offers model classes for biological sequences, controller classes for alignment, sequence manipulation and I/O, interfacing with ENTREZ and view classes that let you easily display and work with sequences in your own applications.

::DEVELOPER

BioCocoa Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

BioCocoa

:: MORE INFORMATION

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