PutGaps Beta – DNA Gapped File from Amino Acid Alignment

PutGaps Beta

:: DESCRIPTION

PutGaps is a software to add gaps to a DNA alignment file based on its Amino Acid equivalent.

::DEVELOPER

McInerney lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / MacOS
  • Java

:: DOWNLOAD

PutGaps

:: MORE INFORMATION

Multi-VORFFIP / VORFFIP – Predicts protein-, peptide-, DNA- and RNA-binding sites in Proteins

Multi-VORFFIP / VORFFIP

:: DESCRIPTION

Multi-VORFFIP is a structure-based, machine learning, computational method designed to predict protein-protein, protein-peptide, protein-DNA and protein-RNA binding sites. M-VORFFIP integrates a wide and heterogeneous set of residue- and environment-based information using a two-step Random Forest ensemble classifier.

VORFFIP (Voronoi Random Forest Feedback Interface Predictor) is structure-based computational method for prediction of protein binding sites.

::DEVELOPER

 Bioinformatics Lab :: IBERS :: Aberystwyth University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Server

:: DOWNLOAD

  NO

:: MORE INFORMATION

Citation

Bioinformatics. 2012 Jul 15;28(14):1845-50. doi: 10.1093/bioinformatics/bts269. Epub 2012 May 4.
A holistic in silico approach to predict functional sites in protein structures.
Segura J1, Jones PF, Fernandez-Fuentes N.

BMC Bioinformatics. 2011 Aug 23;12:352. doi: 10.1186/1471-2105-12-352.
Improving the prediction of protein binding sites by combining heterogeneous data and Voronoi diagrams.
Segura J1, Jones PF, Fernandez-Fuentes N.

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.

NetGene 2.42 – Intron Splice Sites in Human, C. Elegans & A. Thaliana DNA

NetGene 2.42

:: DESCRIPTION

NetGene2 is a service producing neural network predictions of splice sites in human, C. elegans and A. thaliana DNA.

::DEVELOPER

DTU Health Tech

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

NetGene2 Source Code

:: MORE INFORMATION

Citation

S.M. Hebsgaard, P.G. Korning, N. Tolstrup, J. Engelbrecht, P. Rouze, S. Brunak
Splice site prediction in Arabidopsis thaliana DNA by combining local and global sequence information
Nucleic Acids Research, 1996, Vol. 24, No. 17, 3439-3452.

Brunak, S., Engelbrecht, J., and Knudsen, S.
Prediction of Human mRNA Donor and Acceptor Sites from the DNA Sequence
Journal of Molecular Biology, 1991, 220, 49-65.

NetStart 1.0c – Translation Start in Vertebrate & A. Thaliana DNA

NetStart 1.0c

:: DESCRIPTION

NetStart produces neural network predictions of translation start in vertebrate and Arabidopsis thaliana nucleotide sequences. NetStart has been trained on cDNA-like sequences and will therefore presumably have better performance for cDNAs and ESTs. We have not tested the performance on genome data which may contain introns adjacent to the start codon.

::DEVELOPER

DTU Health Tech

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

NetStart

:: MORE INFORMATION

Citation

Neural network prediction of translation initiation sites in eukaryotes: perspectives for EST and genome analysis.
A. G. Pedersen and H. Nielsen, ISMB: 5, 226-233, 1997.

Promoter 2.0 – Transcription Start Sites in Vertebrate DNA

Promoter 2.0

:: DESCRIPTION

Promoter predicts transcription start sites of vertebrate PolII promoters in DNA sequences. It has been developed as an evolution of simulated transcription factors that interact with sequences in promoter regions. It builds on principles that are common to neural networks and genetic algorithms.

::DEVELOPER

DTU Health Tech

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

Promoter

:: MORE INFORMATION

Citation

Promoter 2.0: for the recognition of PolII promoter sequences.
Steen Knudsen
Bioinformatics 15, 356-361, 1999.

Gibbs Motif Sampler 3.2 – Identify Motifs, Conserved Regions, in DNA or Protein Sequences

Gibbs Motif Sampler 3.2

:: DESCRIPTION

The Gibbs Motif Sampler will allow you to identify motifs, conserved regions, in DNA or protein sequences.

::DEVELOPER

Wadsworth Center

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/Windows with Cygwin/MacOsX

:: DOWNLOAD

 Gibbs Motif Sampler

:: MORE INFORMATION

Citation

Neuwald AF, Liu JS, and Lawrence CE. (1995)
Gibbs motif sampling: detection of bacterial outer membrane protein repeats.
Protein Sci 4(8):1618-1632. PubMed: 8520488.

MUSA 0.5.6 – DNA Motif Discovery Tool for Simple and Complex Motifs

MUSA 0.5.6

:: DESCRIPTION

MUSA (Motif finding using an UnSupervised Approach) is a new algorithm that can be used either to autonomously find over-represented complex motifs or to estimate search parameters for modern motif finders.

::DEVELOPER

Nuno D. Mendes

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 MUSA

:: MORE INFORMATION

Citation

Mendes ND, Casimiro AC, Santos PM, Sá-Correia I, Oliveira AL, Freitas AT.
MUSA: a parameter free algorithm for the identification of biologically significant motifs.
Bioinformatics. 2006 Dec 15; 22(24): 2996-3002

sefOri 1.0 – Selecting the best engineered sequence features to predict DNA Replication Origins

sefOri 1.0

:: DESCRIPTION

The software sefOri selects the subset of sequence features with the best prediction accuracies of the DNA replication origins for the four yeast genomes.

::DEVELOPER

Health Informatics Lab (HILab)

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows
  • Python
  • Visual Studio

:: DOWNLOAD

sefOri 

:: MORE INFORMATION

Citation

Bioinformatics. 2019 Jun 20. pii: btz506. doi: 10.1093/bioinformatics/btz506.
sefOri: selecting the best-engineered squence features to predict DNA replication origins.
Lou C, Zhao J, Shi R, Wang Q, Zhou W, Wang Y, Wang G, Huang L, Feng X, Zhou F

AnABlast – Detect potential Coding Regions in DNA by Sequence Comparison

AnABlast

:: DESCRIPTION

AnABlast is an algorithm to discover signals of protein-coding sequences within genomic regions. You can analyze a short nucleotide sequence (up to 25Kb in length or up to 1Mb if you upload the Blast report). It highlights genomic regions with stacked non-significant alignments (protomotifs) which would represent present or ancient protein-coding sequences. It allows to discover new genes in bacteria or exons in eukaryotic organisms.

::DEVELOPER

Computational Biology and Data Mining (CBDM) Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Methods Mol Biol. 2019;1962:207-214. doi: 10.1007/978-1-4939-9173-0_12.
AnABlast: Re-searching for Protein-Coding Sequences in Genomic Regions.
Rubio A, Casimiro-Soriguer CS, Mier P, Andrade-Navarro MA, Garzón A, Jimenez J, Pérez-Pulido AJ