:: DESCRIPTION
Nucleosomes Positioning allows you to submit a genomic sequence and to recieve a prediction of the nucleosomes positions on it, based on the nucleosome-DNA interaction model.
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Kaplan et al.,
“The DNA-Encoded Nucleosome Organization of a Eukaryotic Genome“,
Nature 458, 362-366
:: DESCRIPTION
SIDEpro is a novel machine learning approach for the fast and accurate prediction of side-chain conformations.
::DEVELOPER
Institute for Genomics and Bioinformatics
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation:
K. Nagata, A. Randall, & P. Baldi.
SIDEpro: a novel machine learning approach for the fast and accurate prediction of side-chain conformations.
Proteins. 2012 Jan;80(1):142-53. doi: 10.1002/prot.23170. Epub 2011 Nov 9.
:: DESCRIPTION
SOLpro predicts the propensity of a protein to be soluble upon overexpression in E. coli using a two-stage SVM architecture based on multiple representations of the primary sequence. Each classifier of the first layer takes as input a distinct set of features describing the sequence. A final SVM classifier summarizes the resulting predictions and predicts if the protein is soluble or not as well as the corresponding probability.
::DEVELOPER
Institute for Genomics and Bioinformatics
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation:
Magnan CN, Randall A, Baldi P.
SOLpro: accurate sequence-based prediction of protein solubility.
Bioinformatics. 2009 Sep 1;25(17):2200-7. Epub 2009 Jun 23.
:: DESCRIPTION
SELECTpro is a novel structure-based model selection method derived from an energy function comprising physical, statistical, and predicted structural terms. Novel and unique energy terms include predicted secondary structure, predicted solvent accessibility, predicted contact map, beta-strand pairing, and side-chain hydrogen bonding.
::DEVELOPER
Institute for Genomics and Bioinformatics
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation:
A. Randall, P. Baldi.
SELECTpro: effective protein model selection using a structure-based energy function resistant to BLUNDERs.
BMC Structural Biology, 8, 52, 2008
:: DESCRIPTION
DISpro uses a 1D-RNN to predict the probablity that residues are disorder. The probabilities are also thresholded at probablity .5 to make a hard classification. The input to DISpro is the sequence profile, predicted secondary structure, and predicted relative solvent accesiblity.
::DEVELOPER
Institute for Genomics and Bioinformatics
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation:
J. Cheng, M. Sweredoski, & P. Baldi.
Accurate Prediction of Protein Disordered Regions by Mining Protein Structure Data.
Data Mining and Knowledge Discovery, vol. 11, no. 3, pp. 213-222, (2005).
:: DESCRIPTION
DIpro is a cysteine disulfide bond predictor based on 2D recurrent neural network, support vector machine, graph matching and regression algorithms. It can predict if the sequence has disulfide bonds or not, estimate the number of disulfide bonds, and predict the bonding state of each cysteine and the bonded pairs. It yields the best accuracy on the benchmark dataset Sp39. It can handle any number of disulfide bonds where most of methods available so far only can handle less than 6 disulfide bonds.
::DEVELOPER
Institute for Genomics and Bioinformatics
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation:
J. Cheng, H. Saigo, & P. Baldi.
Large-Scale Prediction of Disulphide Bridges Using Kernel Methods, Two-Dimensional Recursive Neural Networks, and Weighted Graph Matching.
Proteins, vol. 62, no. 3, pp. 617-629, (2006)
:: DESCRIPTION
DOMpro predicts domain locations using a 1D-RNN. DOMpro takes an input the sequence profile, predicted secondary structure, and predicted relative solvent accessiblity. The output of the 1D-RNN is a classification for each residue as being in a domain boundary region or not. The domains are then infered from this output.
::DEVELOPER
Institute for Genomics and Bioinformatics
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation:
J. Cheng, M. Sweredoski, & P. Baldi.
DOMpro: Protein Domain Prediction Using Profiles, Secondary Structure, Relative Solvent Accessibility, and Recursive Neural Networks.
Knowledge Discovery and Data Mining, vol. 13, no. 1, pp. 1-20, (2006)
:: DESCRIPTION
MUpro is a set of machine learning programs to predict how single-site amino acid mutation affects protein stability. We developed two machine learning methods: Support Vector Machines and Neural Networks. Both of them were trained on a large mutation dataset and show accuracy above 84% via 20 fold cross validation, which is better than other methods in the literature. One advantage of our methods is that they do not require tertiary structures to predict protein stability changes. Our experimental results show that the prediction accuracy using sequence information alone is comparable to that of using tertiary structures. So even you do not have protein tertiary structures available, you still can use this server to get rather accurate prediction. Of course, if you provide tertiary structures, our methods will take advantage of them and you might get slightly better predictions.
::DEVELOPER
Institute for Genomics and Bioinformatics
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation:
J. Cheng, A. Randall, and P. Baldi.
Prediction of Protein Stability Changes for Single Site Mutations Using Support Vector Machines.
Proteins. 2006 Mar 1;62(4):1125-32.
:: DESCRIPTION
ProteinEvaluator evaluates predictors using SOV, MCC, Q-Score and State Machine
::DEVELOPER
:: SCREENSHOTS
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
:: DESCRIPTION
MAGprediction (Multi-allelic Gene Prediction) is a software which was developed for predicting highly polymorphic gene alleles using unphased SNP data.
::DEVELOPER
Fred Hutchinson Cancer Research Center
:: SCREENSHOTS
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Li SS, Wang H, Smith A, Zhang B, Zhang XC, Schoch G, Geraghty D, Hansen JA, Zhao LP
Predicting Highly Polymorphic Alleles Using Unphased and Flanking Single Nucleotide Polymorphisms.
Genetic Epidemiology 2011,35(2):85-92
