EFFECT 2013 – Automated Construction and Extraction of Features for Classification of Biological Sequences

EFFECT 2013

:: DESCRIPTION

EFFECT is an algorithmic framework for automated detection of functional signals in biological sequences.

::DEVELOPER

Computational Biology lab, George Mason University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • Java
  • BioJava

:: DOWNLOAD

 EFFECT

:: MORE INFORMATION

Citation

Kamath U, De Jong K, Shehu A.
Effective automated feature construction and selection for classification of biological sequences.
PLoS One. 2014 Jul 17;9(7):e99982. doi: 10.1371/journal.pone.0099982. PMID: 25033270; PMCID: PMC4102475.

BioSAVE 0.11 – Biological Sequence Annotation Viewer

BioSAVE 0.11

:: DESCRIPTION

BioSAVE is a program for visualising DNA or protein sequences and annotations thereof. Annotations is used in a very broad sense here, encompassing any annotation that may be expressed in GFF format.

::DEVELOPER

Richard Pollock and Boris Adryan

:: SCREENSHOTS

BioSAVE

:: REQUIREMENTS

  • MacOsX

:: DOWNLOAD

 BioSAVE

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2008 Mar 20;9:157. doi: 10.1186/1471-2105-9-157.
BioSAVE: display of scored annotation within a sequence context.
Pollock RF1, Adryan B.

ProBias/BIAS – Detect Compositional Bias in Biological Sequences

ProBias/BIAS

:: DESCRIPTION

BIAS: a novel sensitive method for the detection of user-defined compositional bias in biological sequences.
BIAS automatically searches a user-supplied protein sequence for segments that contain unusually dense clusters of user-specified amino acid types and computes analytical estimates of the statistical significance of each cluster. These estimates are based on the discrete scan statistics that allows one to detect segments that exhibit even subtle local deviations from the random independence model.

ProBias: a web-server for the identification of user-specified types of compositionally biased segments in protein sequences

::DEVELOPER

Igor B. Kuznetsov

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/MacOsX/Windows

:: DOWNLOAD

 BIAS

:: MORE INFORMATION

Citation

I.Kuznetsov and S.Hwang (2006)
A novel sensitive method for the detection of user-defined compositional bias in biological sequences.
Bioinformatics, 22(9):1055-1063

I.Kuznetsov (2008)
ProBias: a web-server for the identification of user-specified types of compositionally biased segments in protein sequences
Bioinformatics 24(13): 1534-1535

BATS – A Basic Analysis Toolkit for Biological Sequences

BATS

:: DESCRIPTION

BATS consists of a collection of libraries that can be used to run basic sequence analysis tasks. Routines for global alignment (LCS from Fragments, Edit Distance with Gaps), local alignment (approximata string matching with K mismateches or differences), and statistical analysis (Filter, Z-Score and Model Generation ) are included.

::DEVELOPER

Raffaele Giancarlo

:: SCREENSHOTS

BATS2

:: REQUIREMENTS

  • Linux / MacOSX / Windows
  • Perl

:: DOWNLOAD

 BATS

:: MORE INFORMATION

Citation

Algorithms Mol Biol. 2007 Sep 18;2:10.
A basic analysis toolkit for biological sequences.
Giancarlo R1, Siragusa A, Siragusa E, Utro F.

Kolmogorov – Compression-based Classification of Biological Sequences and Structures

Kolmogorov

:: DESCRIPTION

Kolmogorov is a multistep approach to classify and cluster Biological Sequences and Structures, via Compression.

::DEVELOPER

Raffaele Giancarlo

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOSX / Windows
  • Perl
  • BioPerl

:: DOWNLOAD

 Kolmogorov

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2007 Jul 13;8:252.
Compression-based classification of biological sequences and structures via the Universal Similarity Metric: experimental assessment.
Ferragina P1, Giancarlo R, Greco V, Manzini G, Valiente G.

Jstacs 2.3 – Java Framework for Statistical Analysis and Classification of Biological Sequences

Jstacs 2.3

:: DESCRIPTION

Jstacs is an open source Java library, which focuses on the statistical analysis of biological sequences instead. Jstacs comprises an efficient representation of sequence data and provides implementations of many statistical models with generative and discriminative approaches for parameter learning. Using Jstacs, classifiers can be assessed and compared on test datasets or by cross-validation experiments evaluating several performance measures.

::DEVELOPER

Jstacs Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux/  MacOSX
  • Java

:: DOWNLOAD

 Jstacs

:: MORE INFORMATION

Citation

Michael Seifert, Marc Strickert, Alexander Schliep and Ivo Grosse
Exploiting prior knowledge and gene distances in the analysis of tumor expression profiles with extended Hidden Markov Models
Bioinformatics (2011) 27 (12): 1645-1652.

Seqinr 3.4-5 – Biological Sequences Retrieval and Analysis in R

Seqinr 3.4-5

:: DESCRIPTION

The seqinR package for the R environment is a library of utilities to retrieve and analyse biological sequences.

::DEVELOPER

Leonor Palmeira.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

  Seqinr

:: MORE INFORMATION

D. Charif, J. Thioulouse, J. R. Lobry and G. Perrière
Online synonymous codon usage analyses with the ade4 and seqinR packages
Bioinformatics (2005) 21 (4): 545-547

motif-x 1.2 – Biological Sequence Motif Discovery

motif-x 1.2

:: DESCRIPTION

motif-x (short for motif extractor) is a software tool designed to extract overrepresented patterns from any sequence data set. The algorithm is an iterative strategy which builds successive motifs through comparison to a dynamic statistical background.

::DEVELOPER

Schwartz Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

  NO

:: MORE INFORMATION

Citation

Curr Protoc Bioinformatics. 2011 Sep;Chapter 13:Unit 13.15-24. doi: 10.1002/0471250953.bi1315s35.
Biological sequence motif discovery using motif-x.
Chou MF, Schwartz D.