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Pse-in-One
Generates nearly all the possible feature vectors for DNA, RNA and protein sequences. Pse-in-One contains three sub web servers: (1) PseDAC-General, (2) PseRAC-General and (3) PseAAC-General. Each of them contains three categories. The first one is to generate the pseudo components for the short-range or local sequence order information by counting the occurrence frequencies of the k nearest residues along the sequence S. The second and third categories are to generate the pseudo components for the long-range or global sequence order information by counting, respectively, the auto and special correlations of residues along the sequence.
PartsList
Provides answers to simple questions such as these for the domain of protein folds. PartsList is a web application where properties for a protein fold include: (i) amino acid composition, (ii) alignment information, (iii) fold occurrences in various genomes, (iv) statistics related to motions, (v) absolute expression levels of yeast in different experiments, (vi) relative expression ratios for yeast, worm and Escherichia coli in various conditions, (vii) information on protein–protein interactions (based on whole-genome yeast interaction data and databank surveys) and (Viii) sensitivity of the genes associated with the fold to inserted transposons.
PseAAC-Builder / PSEudo-Amino Acid Composition-Builder
A program to generate various different modes of Chou's general PseAAC, such as the gene ontology mode, the functional domain mode, and the sequential evolution mode. PseAAC-General allows the users to define their own desired modes. In every mode, 544 physicochemical properties of the amino acids are available for choosing. The computing efficiency is at least 100 times that of existing programs, which makes it able to facilitate the extensive studies on proteins and peptides.
propy / protein in python
A freely available, open source python package for calculating the widely used structural and physicochemical features of proteins and peptides from amino acid sequence. propy computes five feature groups composed of 13 features, including amino acid composition, dipeptide composition, tripeptide composition, normalized Moreau-Broto autocorrelation, Moran autocorrelation, Geary autocorrelation, sequence-order-coupling number, quasi-sequence-order descriptors, composition, transition and distribution of various structural and physicochemical properties and two types of pseudo amino acid composition (PseAAC) descriptors. These features could be generally regarded as different Chou's PseAAC modes. In addition, it can also easily compute the previous descriptors based on user-defined properties, which are automatically available from the AAindex database.
PseKRAAC / The pseudo K-tuple Reduced Amino Acids Composition
A web server for generating pseudo K-tuple reduced amino acids composition. By implementing reduced amino acid alphabets, the protein complexity can be significantly simplified, which leads to decrease chance of overfitting, lower computational handicap, and reduce information redundancy. PseKRAAC delivers more capability for protein research by incorporating three crucial parameters that describes protein composition. Users can easily generate many different modes of PseKRAAC tailored to their needs by selecting various reduced amino acids alphabets and other characteristic parameters. PseKRAAC is a very useful tool in computational proteomics and protein sequence analysis.
SG-MPt_Pred
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Provides a stacked generalization classifier to predict membrane protein types based on pseudo-amino acid composition. SG-MPt_Pred uses the stacking approach which can combine several types of classifiers through a meta-classifier to maximize the generalization accuracy. The results thus obtained were very encouraging. It is anticipated that the stacking approach may also hold a high potential to improve the identification quality for, among many other protein attributes, subcellular location, enzyme family class, protease type, and protein–protein interaction (PPI) type.
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