PSTk-Classifier – Classify DNA using a Bayesian approach

PSTk-Classifier

:: DESCRIPTION

PSTk-Classifier is a software written in C++ for classifying DNA using a Bayesian approach. Different underlying models can be selected — Naive (Nk), Markov (Mk) and Variable Length Markov (VLMK). The classifier works by first constructing profiles for all groups using fasta-files directly. The profiles are kept in a directory. Then sample sequences (in a multifasta file) can be scored against the profiles and a high-score list will be presented.

::DEVELOPER

Daniel Dalevi (daniel.dalevi@gmail.com)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

  PSTk-Classifier

:: MORE INFORMATION

Citation

Dalevi D, Dubhashi D, Hermansson M (2006)
Bayesian classifiers for detecting HGT using fixed and variable order markov models of genomic signatures.
Bioinformatics. 2006 Mar 1;22(5):517-22.

Sequedex 2.1.1 – Classifies DNA Sequences by Analyzing Collections of Sequences

Sequedex 2.1.1

:: DESCRIPTION

Sequedex classifies DNA sequences by analyzing collections of sequences in new ways.

::DEVELOPER

The Sequedex Team

:: SCREENSHOTS

Sequedex

:: REQUIREMENTS

  • Linux/ MacOsX/Windows
  • Java
  • Python
  • Biopython

:: DOWNLOAD

 Sequedex

:: MORE INFORMATION

Citation

Joel Berendzen et al.
Rapid phylogenetic and functional classification of short genomic fragments with signature peptides
BMC Research Notes 2012, 5:460 doi:10.1186/1756-0500-5-460

HyperPrior – Classify Gene Expression and ArrayCGH data with Prior knowledge

HyperPrior

:: DESCRIPTION

HyperPrior is a hypergraph-based semi-supervised learning algorithm to classify gene expression and arrayCGH data using biological knowledge as constraints on graph-based learning. HyperPrior is a robust two-step iterative method that alternatively finds the optimal labeling of the samples and the optimal weighting of the features, guided by constraints encoding prior knowledge. The prior knowledge for analyzing gene expression data is that cancer-related genes tend to interact with each other in a protein-protein interaction network. Similarly, the prior knowledge for analyzing arrayCGH data is that probes that are spatially nearby in their layout along the chromosomes tend to be involved in the same amplification or deletion event. Based on the prior knowledge, HyperPrior imposes a consistent weighting of the correlated genomic features in graph-based learning.

::DEVELOPER

Kuang Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / WIndows / MacOsX
  • Matlab

:: DOWNLOAD

  HyperPrior

:: MORE INFORMATION

Citation

Bioinformatics. 2009 Nov 1;25(21):2831-8. doi: 10.1093/bioinformatics/btp467. Epub 2009 Jul 30.
A hypergraph-based learning algorithm for classifying gene expression and arrayCGH data with prior knowledge.
Tian Z, Hwang T, Kuang R.

AIS – Classify Amino Acids

AIS

:: DESCRIPTION

AIS (Almost Invariant Sets) is a program which uses a novel criterion and method to classify amino acids. The goal is to identify sets of amino acids with a high probability of change between elements of the set but small probability of change between different sets by using amino acid replacement matrices and their eigenvectors. After identification of the subsets the quality of the partition is assessed with a conductance measure

::DEVELOPER

Goldman Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 AIS

:: MORE INFORMATION

Citation:

Kosiol et al. (2004),
A new criterion and method for amino acid classification
Journal of Theoretical Biology 228:97-106.

MVQueries – Classify Short Gene Expression Time-courses

MVQueries

:: DESCRIPTION

MVQueries is a software of classifying short gene expression time-courses. Short gene expression time-courses monitoring response to toxins are represented as piecewise constant functions, which are modeled as left–right Hidden Markov Models. Our software implements a Bayesian approach to parameter estimation and in inference. Compared to previously published work, we improve prediction accuracy by 7 and 4%, respectively, when classifying toxicology and stress response data and e also reduce running times by at least a factor of 140.

::DEVELOPER

Alexander Schliep’s group for bioinformatics

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

  MVQueries

:: MORE INFORMATION

Citation

Bioinformatics. 2011 Apr 1;27(7):946-52. Epub 2011 Jan 25.
Classifying short gene expression time-courses with Bayesian estimation of piecewise constant functions.
Hafemeister C, Costa IG, Sch?nhuth A, Schliep A.