PutGaps Beta
:: DESCRIPTION
PutGaps is a software to add gaps to a DNA alignment file based on its Amino Acid equivalent.
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
- Windows / Linux / MacOS
- Java
:: DOWNLOAD
:: MORE INFORMATION
Tag: DNA
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
:: SCREENSHOTS
N/A
:: REQUIREMENTS
- Linux/Windows
- Perl
:: DOWNLOAD
:: 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
:: SCREENSHOTS
N/A
:: REQUIREMENTS
- Linux
:: DOWNLOAD
:: 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
:: SCREENSHOTS
N/A
:: REQUIREMENTS
- Linux
:: DOWNLOAD
:: 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
:: SCREENSHOTS
N/A
:: REQUIREMENTS
- Linux
:: DOWNLOAD
:: 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
:: SCREENSHOTS
N/A
:: REQUIREMENTS
- Linux/Windows with Cygwin/MacOsX
:: DOWNLOAD
:: 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
:: SCREENSHOTS
N/A
:: REQUIREMENTS
- Linux
:: DOWNLOAD
:: 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
:: 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