Tag: Feature

FEATURE 3.1 – Examine Biological Structures

FEATURE 3.1

:: DESCRIPTION

FEATURE is an automated tools that examines biological structures and produces useful representations of the key biophysical and biochemical features of these structures that are critical for understanding function. The utility of this system extends from medical/pharmaceutical applications (model-based drug design, comparing pharmacological activities) to industrial applications (understanding structural stability, protein engineering).

::DEVELOPER

FEATURE Team

:: SCREENSHOTS

 

:: REQUIREMENTS

  • Windows/ Linux / Mac OsX

:: DOWNLOAD

 FEATURE

:: MORE INFORMATION

Citation:

Halperin I, Glazer DS, Wu S, Altman RB.
The FEATURE framework for protein function annotation: modeling new functions, improving performance, and extending to novel applications.”
BMC Genomics. 9 Suppl 2 S2.

BANFF 2.0 – Gene Network Feature Selection

BANFF 2.0

:: DESCRIPTION

BANFF (Bayesian Network Feature Finder) is an R package for gene network feature selection.

::DEVELOPER

Tianwei Yu

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows / MacOsX
  • R

:: DOWNLOAD

BANFF

:: MORE INFORMATION

Citation

Bayesian network feature finder (BANFF): an R package for gene network feature selection.
Lan Z, Zhao Y, Kang J, Yu T.
Bioinformatics. 2016 Dec 1;32(23):3685-3687.

gkm-SVM 2.0 – Enhanced Regulatory Sequence Prediction Using Gapped k-mer Features

gkm-SVM 2.0

:: DESCRIPTION

gkm-SVM is a new classifier and a general method for robust estimation of k-mer frequencies.

::DEVELOPER

BeerLab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux
  • C++ COmpiler

:: DOWNLOAD

 gkm-SVM

:: MORE INFORMATION

Citation

PLoS Comput Biol. 2014 Jul 17;10(7):e1003711. doi: 10.1371/journal.pcbi.1003711. eCollection 2014.
Enhanced regulatory sequence prediction using gapped k-mer features.
Ghandi M, Lee D, Mohammad-Noori M, Beer MA

Gee Fu 0.1.2 – Database tool for Genomic Assembly and Feature data

Gee Fu 0.1.2

:: DESCRIPTION

Gee Fu is an application that holds Gene Feature data. It has been designed with the needs of researchers wanting to keep, share and annotate sequence and feature data. Gee Fu is a Ruby on Rails based RESTful web-service application that stores and serves sequence assembly and genome feature data on request.

::DEVELOPER

Dan MacLean

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Mac OsX
  • Ruby
  • Rails
  • AnnoJ

:: DOWNLOAD

 Gee Fu

:: MORE INFORMATION

Citation

Bioinformatics. 2011 Oct 1;27(19):2754-5. doi: 10.1093/bioinformatics/btr442. Epub 2011 Jul 29.
Gee Fu: a sequence version and web-services database tool for genomic assembly, genome feature and NGS data.
Ramirez-Gonzalez R, Caccamo M, MacLean D.

SRS3D 1.4 – System for Displaying 3D Structures integrated with Sequences and Features

SRS3D 1.4

:: DESCRIPTION

SRS 3D is a system for displaying 3D structures integrated with sequences and features.

::DEVELOPER

Seán O’Donoghue

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux /MacOsX
  • Java
  • Java 3D

:: DOWNLOAD

 SRS3D

:: MORE INFORMATION

Citation

Seán I. O’Donoghue, Joachim E. W. Meyer, Andrea Schafferhans and Karsten Fries
The SRS 3D module: integrating structures, sequences and features
Bioinformatics (2004) 20 (15): 2476-2478.

PexSPAM 1.2 – Protein Sequence Feature Extraction

PexSPAM 1.2

:: DESCRIPTION

PexSPAM is a Java standalone program that I wrote for protein sequence feature extraction. PexSPAM was originally designed to be a “feature factory” for secondary structure classification problem in integral membrane proteins. PexSPAM extends the SPAM (Ayres et al, 2002) method by incorporating gap and regular expression constraints into mining procedure.

::DEVELOPER

Joshua Ho

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows / MacOs / Linux / Unix
  • Java

:: DOWNLOAD

 PexSPAM for Win , Source Code

:: MORE INFORMATION

Citation:

Ho J, Lukov L, Chawla S (2005)
Sequential Pattern Mining with Constraints on Large Protein Databases.
In Chakrabarti S, Sudarshan S, Radha Krishnan P (Eds) Proceedings of the 12th International Conference on Management of Data (COMAD 2005b), 89-100.

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