:: DESCRIPTION
MMBPred is a server of prediction of of MHC class I binders which can bind to wide range of MHC alleles with high affinity. This server has potential to develop sub-unit vaccine for large population
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Bhasin,M. and Raghava, G.P.S. (2003)
Prediction of promiscuous and high-affinity mutated MHC binders.
Hybrid Hybridomics.22(4),229-34.
:: DESCRIPTION
The BcePred server predicts B cell epitope based on physio-chemical properties of amino acids.
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Saha.S and Raghava G.P.S.
BcePred:Prediction of Continuous B-Cell Epitopes in Antigenic Sequences Using Physico-chemical Properties.
In G.Nicosia, V.Cutello, P.J. Bentley and J.Timis (Eds.) ICARIS 2004, LNCS 3239, 197-204, Springer,2004.
:: DESCRIPTION
MHC server was used Matrix Optimization Technique to predict MHC binding core.
::DEVELOPER
G P S Raghava
,
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Singh, H. and Raghava, G. P. S. (2002)
Detection of Orientation of MHC Class II Binding Peptides Using Bioinformatics Tools
Biotech Software and Internet Report, 3:146
:: DESCRIPTION
SplamiR is the first method for the prediction of spliced miRNAs in plants. It takes genomic sequence and the sequence of a potential target mRNA as input. In the first phase of SplamiR, a database of complementary sequence pairs is created for the given genomic sequence. These sequence pairs might encode for RNAs folding into stem-loop structures. In the second phase, this database is searched for sequences with complementarity to the given target mRNA. For the identified sequences, in silico splice variants are generated and these are classified as to whether they might represent pre-miRNAs.
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation:
Bioinformatics. 2011 May 1;27(9):1215-23. doi: 10.1093/bioinformatics/btr132. Epub 2011 Mar 17.
SplamiR–prediction of spliced miRNAs in plants.
Thieme CJ, Gramzow L, Lobbes D, Theissen G.
:: DESCRIPTION
PTMpred is a tool for predicting post-transcriptional modification sites based on protein sequence.
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Prediction of posttranslational modification sites from amino acid sequences with kernel methods.
Xu Y, Wang X, Wang Y, Tian Y, Shao X, Wu LY, Deng N.
J Theor Biol. 2014 Mar 7;344:78-87. doi: 10.1016/j.jtbi.2013.11.012.
:: DESCRIPTION
Prodepth is an online prediction web server for the accurate prediction of residue depth from protein sequences only. It can be used to accurately identify the burial depth status of amino acid residues in a protein.
::DEVELOPER
Akutsu Laboratory , Whisstock Laboratory
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
PLoS One. 2009 Sep 17;4(9):e7072. doi: 10.1371/journal.pone.0007072.
Prodepth: predict residue depth by support vector regression approach from protein sequences only.
Song J, Tan H, Mahmood K, Law RH, Buckle AM, Webb GI, Akutsu T, Whisstock JC.
:: DESCRIPTION
RCDP (Relative Co-evolution of Domain Pairs) tests the generality of the observed behavior, and to predict large-scale domain–domain interactions using the yeast inter-actome.
::DEVELOPER
Teresa Przytycka Research Group
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Raja Jothi et al.
Co-evolutionary analysis of domains in interacting proteins reveals insights into domain-domain interactions mediating protein-protein interactions
Journal of Molecular Biology, 29;362(4):861-75, 2006
:: DESCRIPTION
jPREdictor is a tool for the prediction of cis-regulatory elements, e.g. PRE/TREs in Drosophila melanogaster. Short sequence fragments, namely transcription factor binding sites, are known to be enriched and clustered in such elements. These binding sites are represented as motifs and for the prediction, they must be known. Using these motifs, it is possible to search them on sequences, to weight them by applying a positive or negative training set (model or background, respectively) and to score a sequence.
::DEVELOPER
Thomas Fiedler & Marc Rehmsmeier
:: SCREENSHOTS
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Thomas Fiedler
The jPREdictor and its Application to Motif Discovery, Motif Clustering and the Prediction of Polycomb/Trithorax Response Elements
Dissertation, Univerity of Bielefeld, Apr 2008
Thomas Fiedler and Marc Rehmsmeier
jPREdictor: a versatile tool for the prediction of cis-regulatory elements
Nucleic Acids Research, 34:W546-W550, 2006
:: DESCRIPTION
RNAcontext is a motif-finding algorithm to infer binding preferences of RNA binding proteins (RBP) from experimental affinity data. The input to RNAcontext consists of a set of sequences together with their associated structure annotation profiles (estimated using SFOLD) and RNA-binding affinity estimates for the given RBP. By learning a motif model that predicts the input affinities, RNAcontext infers the sequence and RNA secondary structure preferences of the RBP.
RBPmotif web server implements the RNAcontext algorithm and an additional analysis framework so that binding preferences of an RBP of interest can be identified from experimental data.
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation:
Nucleic Acids Res. 2013 Jul;41(Web Server issue):W180-6. doi: 10.1093/nar/gkt463. Epub 2013 Jun 10.
RBPmotif: a web server for the discovery of sequence and structure preferences of RNA-binding proteins.
Kazan H1, Morris Q.
H Kazan , D Ray, E Chan, TR Hughes, Q Morris (2010)
RNAcontext: a new method for learning the sequence and structure binding preferences.
PLoS Comput Biol 6(7): e1000832. doi:10.1371/journal.pcbi.1000832
:: DESCRIPTION
FrameD is a program that predicts coding regions in prokaryotic and eukaryotic sequences that may contain frameshifts.
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
FrameD: a flexible program for quality check and gene prediction in prokaryotic genomes and noisy matured eukaryotic sequences
Thomas Schiex, Jérôme Gouzy, Annick Moisan, and Yannick de Oliveira
Nucl. Acids. Res. 2003 31: 3738-3741.
