GANN 2.0 – Machine Learning tool for the Detection of Conserved Features in DNA

GANN 2.0

:: DESCRIPTION

GANN (Genetic Algorithm Neural Networks) is a machine learning method designed with the complexities of transcriptional regulation in mind.The key principle is that regulatory regions are composed of features such as consensus strings, characterized binding sites, and DNA structural properties. GANN identifies these features in a set of sequences, and then identifies combinations of features that can differentiate between the positive set (sequences with known or putative regulatory function) and the negative set (sequences with no regulatory function). Once these features have been identified, they can be used to classify new sequences of unknown function.

::DEVELOPER

Beiko lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/Windows
  • Perl

:: DOWNLOAD

 GANN

:: MORE INFORMATION

Citation

Beiko, R.G. and Charlebois, R.L. (2005).
GANN: genetic algorithm neural networks for the detection of conserved combinations of features in DNA.
BMC Bioinformatics 6: 36.

GLIDE 0.1 – GPU-based Linear Regression for Detection of Epistasis

GLIDE 0.1

:: DESCRIPTION

GLIDE maps phenotypes to pairs of genetic loci and systematically searches for the epistatic interactions expected to reveal part of this missing heritability.

::DEVELOPER

Machine Learning and Computational Biology Research Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Python
  • R package

:: DOWNLOAD

 GLIDE

:: MORE INFORMATION

Citation

Hum Hered. 2012;73(4):220-36.
GLIDE: GPU-based linear regression for detection of epistasis.
Kam-Thong T, Azencott CA, Cayton L, Pütz B, Altmann A, Karbalai N, S?mann PG, Sch?lkopf B, Müller-Myhsok B, Borgwardt KM.

EPIBLASTER 1.0 – Two-locus Epistasis Detection Strategy using GPU

EPIBLASTER 1.0

:: DESCRIPTION

The purpose of EPIBLASTER is to compute the differences of correlation coefficients between Controls and Cases as a mean to isolate for significant SNPs Interactions using gpuCor function of the gputools package on a CUDA enabled graphic card

::DEVELOPER

Machine Learning and Computational Biology Research Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • R package
  • gputools R package
  • CUDA developer driver and Toolkit
  • GENABLE

:: DOWNLOAD

 EPIBLASTER

:: MORE INFORMATION

Citation

Eur J Hum Genet. 2011 Apr;19(4):465-71. doi: 10.1038/ejhg.2010.196.
EPIBLASTER-fast exhaustive two-locus epistasis detection strategy using graphical processing units.
Kam-Thong T, Czamara D, Tsuda K, Borgwardt K, Lewis CM, Erhardt-Lehmann A, Hemmer B, Rieckmann P, Daake M, Weber F, Wolf C, Ziegler A, Pütz B, Holsboer F, Sch?lkopf B, Müller-Myhsok B.

DIAL – 3-Dimensional RNA Structural Alignment and Motif Detection

DIAL

:: DESCRIPTION

DIAL is a web server for 3-dimensional RNA structural alignment (global and local) and for motif detection. DIAL (DIhedral ALignment) runs in time that is quadratic in input length by performing an alignment which accounts for (i) pseudo-dihedral and/or dihedral angle similarity, (ii) nucleotide sequence similarity, (iii) nucleotide base-pairing similarity.

::DEVELOPER

Clote Lab 

:: SCREENSHOTS

DIAL

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

F. Ferre; Y. Ponty; W. A. Lorenz; Peter Clote
DIAL: a web server for the pairwise alignment of two RNA three-dimensional structures using nucleotide, dihedral angle and base-pairing similarities ,
Nucleic Acids Res. 2007 Jul 1;35(Web Server issue):W659-68. Epub 2007 Jun 13.

SMURF / SMURFLite – Simplified / Structural Motifs Detection Using Random Fields

SMURFLite

:: DESCRIPTION

SMURF is a webserver for protein structural motifs dection using random fields

SMURFLite (simplified Structural Motifs Using Random Fields)  is a web application for protein remote homology detection, specifically in beta-structural proteins.

::DEVELOPER

Berger Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

M. Menke, B. Berger and L. Cowen,
Markov random fields reveal an N-terminal double beta-propeller motif as part of a bacterial hybrid two-component sensor system
PNAS March 2, 2010 vol. 107 no. 9 4069-4074.

Bioinformatics. 2012 May 1;28(9):1216-22. doi: 10.1093/bioinformatics/bts110.
SMURFLite: combining simplified Markov random fields with simulated evolution improves remote homology detection for beta-structural proteins into the twilight zone.
Daniels NM, Hosur R, Berger B, Cowen LJ.

RNAiCut – Automated Detection of Significant Genes from Functional Genomic Screens

RNAiCut

:: DESCRIPTION

RNAiCut is a web server of automated detection of significant genes from functional genomic screens.

::DEVELOPER

Berger Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

Nat Methods. 2009 Jul;6(7):476-7. doi: 10.1038/nmeth0709-476.
RNAiCut: automated detection of significant genes from functional genomic screens.
Kaplow IM, Singh R, Friedman A, Bakal C, Perrimon N, Berger B.

QuateXelero – Fast Motif Detection algorithm

QuateXelero

:: DESCRIPTION

QuateXelero is an extremely fast motif detection algorithm which has a Quaternary Tree data structure in the heart.

::DEVELOPER

Laboratory of Systems Biology & Bioinformatics (LBB)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ Windows

:: DOWNLOAD

 QuateXelero

:: MORE INFORMATION

Citation

PLoS One. 2013 Jul 18;8(7):e68073. doi: 10.1371/journal.pone.0068073. Print 2013.
QuateXelero: an accelerated exact network motif detection algorithm.
Khakabimamaghani S1, Sharafuddin I, Dichter N, Koch I, Masoudi-Nejad A.

ExomeCopy 1.32.0 – Copy Number Variant Detection from Exome Sequencing Read Depth

ExomeCopy 1.32.0

:: DESCRIPTION

ExomeCopy implements a hidden Markov model which uses positional covariates, such as background read depth and GC-content, to simultaneously normalize and segment the samples into regions of constant copy count.

::DEVELOPER

Department Computational Molecular Biology, Max-Planck-Institute for Molecular Genetics

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ Windows/ MacOsX
  • R package
  • BioConductor

:: DOWNLOAD

 ExomeCopy

:: MORE INFORMATION

Citation

Stat Appl Genet Mol Biol. 2011 Nov 8;10(1).
Modeling read counts for CNV detection in exome sequencing data.
Love MI, Myšičková A, Sun R, Kalscheuer V, Vingron M, Haas SA.

Emap2sec – Protein secondary structure detection in intermediate-resolution cryo-EM maps

Emap2sec

:: DESCRIPTION

Emap2sec is a deep learning-based tool for detecting protein secondary structures from intermediate resolution cryo-EM maps.

::DEVELOPER

Kihara Bioinformatics Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Python

:: DOWNLOAD

Emap2sec

:: MORE INFORMATION

Citation

Nat Methods. 2019 Sep;16(9):911-917. doi: 10.1038/s41592-019-0500-1. Epub 2019 Jul 29.
Protein secondary structure detection in intermediate-resolution cryo-EM maps using deep learning.
Maddhuri Venkata Subramaniya SR, Terashi G, Kihara D.

PSE-HMM v1 – Genome-wide CNV detection from Next Generation Sequencing data

PSE-HMM v1

:: DESCRIPTION

PSE-HMM is a tool for the genome-wide CNV detection from Next Generation Sequencing data (mate pair reads). PSE-HMM applies an HMM with Position-Specific Emission probabilities for modeling different aberrations in the mate pairs, after being mapped to the reference genome.

::DEVELOPER

School of Biological Sciences, Iran

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/ Linux / MacOsX
  • MatLab

:: DOWNLOAD

PSE-HMM

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2016 Nov 3;18(1):30. doi: 10.1186/s12859-016-1296-y.
PSE-HMM: genome-wide CNV detection from NGS data using an HMM with Position-Specific Emission probabilities.
Malekpour SA, Pezeshk H, Sadeghi M