Gives access to many free software tools for sequence analysis. EMBOSS aims to serve the molecular biology community. It permits the creation and the release of software in an open source spirit. This tool is useful for sequence analysis into a seamless whole. It is free of charge and is available in open source.
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Provides functional analysis of protein sequences. InterPro is a software which allows to classify sequences into protein families and to predict the presence of important domains and sites. The software combines predictive models, known as signatures, from a range of different protein family databases that have different biological focuses and use different methodological approaches to classify protein families and domains.
A high-throughput tool for more reliable functional annotation. PANNZER predicts Gene Ontology (GO) classes and free text descriptions about protein functionality. It uses weighted k-nearest neighbour methods with statistical testing to maximize the reliability of a functional annotation.
Detects homology. FFAS includes adding optimized structural features (experimental or predicted), ‘symmetrical’ Z-score calculation and re-ranking the templates with a neural network. It has high success rate at the Structural Classification of Proteins (SCOP) family, superfamily and fold levels. The tool was tested on the Lindahl benchmark set for fold recognition and showed superior success rate on the family and superfamily levels.
Provides a solution for protein tagging in mammalian tissue culture cells. Mouse BAC finder is an efficient, generic and scalable approach for bacterial artificial chromosomes (BACs)-based transgenesis in mammalian tissue culture cells, which we term ‘BAC TransgeneOmics’. The use of bacterial artificial chromosomes (BACs) for transgenesis enables the expression of the transgene from its native genomic environment. The method is applicable to very large genes, which are difficult to obtain as cDNAs.
A structure-based method for biological function annotation of protein molecules. To use COFACTOR, user needs to provide a 3D-structural model of the protein of interest. COFACTOR will thread the structure through the BioLiP protein function database by local and global structure matches to identify functional sites and homologies. Functional insights, including ligand-binding site, gene-ontology terms, and enzyme classification, will be derived from the best functional homology template.
Allows users to study the proteomic scale inference of enzyme function. EFICAz can identify functionally discriminating residue (FDR) as residues that discriminate the members of a homo-functional family from a hetero-functional family. It combines the prediction from four independent methods, namely: (1) CHIEFc family-based (FDR) identification, (2) multiple PFAM-based FDR recognition, (3) CHIEFc SIT evaluation and (4) high-specificity multiple PROSITE patterns.
Allows Bayesian phylogenetic inference on protein data sets. GPU MrBayes, a modified version of MrBayes, is a task mapping strategy which makes use of GPU cores and GPU memory and reduces redundant operations. The software uses Kahan summation to improve accuracy, convergence rates, and consequently runtime. It was tested on protein data sets from a range of animals studied in phylogenetics research.
A user-friendly web interface for accurate protein function prediction using the SIFTER algorithm. SIFTER is a state-of-the-art sequence-based gene molecular function prediction algorithm that uses a statistical model of function evolution to incorporate annotations throughout the phylogenetic tree.
Provides a web server that predicts Gene Ontology (GO) terms from a list of query sequences. PFP manages a large prediction coverage by retrieving annotations widely including from weakly similar sequences. The software uses predict function method which allows to consider sequences with a lack of annotated homologs in the database, extract and infers functional information. The tool can be combined with Extended Similarity Group (ESG) in a single interface.
Provides an improving specificity by accumulating contribution of consistently predicted Gene Ontology (GO) terms in an iterative search. ESG predicts a GO term with a high score if it appears many times consistently in the multiple searches including the initial search and the second level searches. The method can be applied in several multiple-domains and aims to improve predictions about functions derived from different domains. The tool can be combined with PFP (Protein Functional Prediction) in the a single interface.
Includes cross-references to other biological resources such as Pfam, SCOP, CATH, GO, InterPro and the NCBI taxonomy database. The Structure Integration with Function, Taxonomy and Sequences resource (SIFTS) is focused on standardization of taxonomy information in the PDB based on the NCBI taxonomy database, and on adding cross-references to UniProtKB for all the protein sequences in the PDB that are present in the UniProt database. It has two main components—the semi-automated process that identifies the correct and up-to-date UniProtKB cross-reference for protein chains in the PDB and the automated pipeline that generates residue-level correspondences between proteins in the PDB and the corresponding UniProtKB sequence.
Allows prediction of protein-to-protein and phenotype-to-protein functional associations based on phylogenetic profiling. ProtPhylo achieves flexibility and state-of-the-art taxonomic and functional coverage by generating phylogenetic profiles. It concerns more than 9 million non-redundant protein sequences across over 2000 organisms and implements four independent orthology detection algorithms. In summary, this tool allows prediction of subcellular localization, protein domains, membrane spanning regions, and complementary evidence of protein-protein interactions (PPIs).
A support vector machine (SVM)-based method has been developed for predicting families and subfamilies of GPCRs from the dipeptide composition of proteins. The method classified GPCRs and non-GPCRs with an accuracy of 99.5% when evaluated using 5-fold cross-validation. The method is further able to predict five major classes or families of GPCRs with an overall Matthew's correlation coefficient (MCC) and accuracy of 0.81 and 97.5% respectively.
Allows users to determine various properties of each protein in an entire proteome. PA permits researchers to perform several tasks: (1) prediction of the GeneQuiz general function and Gene Ontology (GO) molecular function of a protein; (2) prediction of the subcellular localization; or (3) creation of a custom classifier to predict a new property. Moreover, this tool can be used for any user-specified ontology.
Serves for the extraction of sequence-driven features from the primary protein sequence followed by the application of a classification system trained on known animal toxins. ClanTox is a systematic scheme for proteome-wide prediction of toxin-like proteins. ClanTox can be used to rank the statistically significant sequences matching toxin-like criteria. This allows the user to focus on a relatively small fraction of high-confidence candidates.
An automated method for the prediction of protein function. CombFunc incorporates ConFunc, a function prediction method, with other approaches for function prediction that use protein sequence, gene expression and protein-protein interaction data.
A web server developed to predict protein function from a combination of three orthogonal approaches. Sequence similarity and domain architecture searches are combined with protein-protein interaction network data to derive consensus predictions for GO terms using functional enrichment. The INGA server can be queried both programmatically through RESTful services and through a web interface designed for usability. The latter provides output supporting the GO term predictions with the annotating sequences. The method has been evaluated by the CAFA assessors (2014) among the best predictors.
Enables an immediate and intuitive representation of the relationships between sequences in a given cluster.
Enables users to rationally design Temperature sensitive (Ts) mutants for their proteins of interest.
Can use catalytic and binding sites templates to search similar sites in a protein. GASS-WEB is a free and a user-friendly web server created for searching similar active sites based on data from the PDB, CSA and NCBI-VAST. This resource could be an invaluable tool for assisting protein function prediction and active site annotation. GASS-WEB can be used under two different scenarios: (i) given a protein of interest, to match a set of specific active site templates; or (ii) given an active site template, looking for it in a database of protein structures.
Allows for follow-up investigation in silico once correlations have been noted between pairs of protein families. SIMBAL uses clues from genomic context, such as co-occurrence or conserved gene neighborhoods, to build a useful training set from a large number of uncharacterized but mutually homologous proteins. The tool can serve as a fast and powerful in silico alternative to direct experimentation for characterizing proteins and their functional interactions.
A phylogenetic profiling algorithm which uses truncated singular value decomposition to address the problem of uninformative profiles giving rise to false positive predictions. Benchmarking the algorithm against the KEGG pathway database, we found that it has substantially improved performance over existing phylogenetic profiling methods. SVD-Phy executes very fast: its run time is on average about 10–20 minutes per organism on a normal workstation.
A simple way to compare protein families. Rather than directly comparing profiles, SCOOP compares the database search outputs that result from searching a sequence database with the profiles. It attempts to utilize these false-negative matches to find when two sequence families are related to each other.
Identifies and classifies protein kinases in a user-provided fasta file using an HMM derived from serine/threonine protein kinases, a position specific scoring matrix derived from the HMM, and comparison with a local version of the curated kinase database from kinase.com.
A heuristic method which searches for given active site 3D templates in unknown proteins. The method can perform non-exact amino acid matches (conservative mutations). GASS is able to find amino acids in different chains and does not impose any restrictions on the active site size.
Predicts possible enzymatic transformations as well as the resulting output compounds, given a set of input compounds. MaRIboES is a system for metabolite and reaction inference based on enzyme specificities. It was validated using a metabolome-wide leave-one out procedure. It also predicts enzymatic alternatives for reactions thought to be autocatalytic, interesting bypasses within and crosslinks between pathways.
Extracts localized features from sequence sets. CascadeDetect computes expected feature counts, screens artifacts and identifies statistically significant localized features of all orders in sets of short sequences, or features of orders up to a specified maximum in longer sequences. The method detects first-order features that are either overrepresented or underrepresented and higher order features that are overrepresented. The algorithm has been successfully tested on synthetic data, and then used to analyze amino acid sequence sets from problems in two different domains to demonstrate its broad utility: a study of HIV-1 protease (HIV PR) cleavage specificity applies the algorithm to a problem in which features play a transient sensing/recognition role, while an analysis of the Schellman loop motif in proteins evaluates features which are structural and therefore more permanent.
Extends an existing suite of protein function annotation tools to infer functional residues, identify functional sites and predict protein function. ETAscape pairs state-of-the-art network analysis with network visualization, putting the ability to generate novel predictions into the hands of researchers. Perhaps more importantly, it provides insight into the basis for those predictions.
Builds a phylogeny of protein families using the distance matrix of their poisson Hidden Markov Models (pHMMs). Although not designed for subfamily classification, if given a set of protein sequences, pHMM-tree can also call a clustering program to automatically classify proteins into subgroups and then builds a pHMM phylogeny.
A method for the automatic annotation of protein sequences in the UniProt Knowledgebase (UniProtKB) by comparing their domain architectures, classifying proteins based on the similarities and propagating functional annotation. The performance of UniProt-DAAC was measured through a cross-validation analysis using the Gene Ontology annotation of a sub-set of Uni-ProtKB/Swiss-Prot. The results indicate the effectiveness of this approach in detecting functional similarity with an average F-score: 0.85.
Allows easy, universal discovery of new target proteins, as well as understanding the features underlying different high-level protein functions. ProFET extracts hundreds of features covering the elementary biophysical and sequence derived attributes. Most features capture statistically informative patterns. In addition, different representations of sequences and the amino acids (AA) alphabet provide a compact, compressed set of features. The results from ProFET were incorporated in data analysis pipelines, implemented in python, and adapted for multi-genome scale analysis.
A web application for protein function assignment based on identity detection of small significant fragments (protomotifs) that can act as modular pieces in peptide construction. The system is able to assign function by finding correlations between protomotifs and functional annotations contained in SWISS-PROT and Medline databases. In addition, function ontologies are used for hierarchical organization of the predicted functions.
Provides end-to-end feature extraction and classifier training method for enzyme function prediction. DEEPre is based on deep learning and utilizes the sequence information. It forces the model to learn to extract features by itself and adapt the parameters of the classifier simultaneously so that it can improve the performance in a virtuous circle. This tool predicts a score for each candidate value of a certain type II restriction enzyme (EC) digit, and can serve to detect the enzyme promiscuity.
Predicts protein function from protein sequences and known interactions. DeepGO is a method that improves performance of function prediction over a BLAST baseline, and performs particularly well in predicting cellular locations of proteins.
Identifies a functionally informative multiple sequence alignment (MSA) from a large set of alternatives. SAMMI is a sequence-based approach incorporated into a functional residue prediction pipeline. It extensively samples MSAs from a sequence database search and conducts a conservation analysis on the most informative MSA. The software was tested determining the ligand-binding residues of a set of 435 proteins.
Predicts specific protein function via a model that utilizes protein network and considers gene ontology (GO). Effusion exploits a network of partially characterized sequences to provides function predictions. In addition to the GO, it uses sequence similarity networks (SSNs) and applies probabilistic graphical models (PGMs) in a way that is extensible.
Utilizes an adaptive diffusion mechanism to improve the performance of BirgRank. AptRank introduces an adaptive mechanism to the PageRank framework that computes an optimal set of weights for the first several steps of diffusion so as to maximize recovery of a subset of known function annotations. It outperforms the four existing state-of-the-art methods in almost all cases, and in particular, outperforms those methods that do not incorporate information about the functional hierarchy.
Predicts guanylyl cyclases centers (GCCs) from amino acid sequences. GCPred is a webserver that calculates predicted GCCs based on a set of physicochemical properties of amino acids in experimentally validated GCCs. The software provides a way to rank hits based on algorithm that considers the conserved amino acids with assigned catalytic functions and the physicochemical properties of amino acids at the GCCs. It also presents an option for metal ion binding.
New
Predicts sequence-based proteins. Phylo-PFP exploits phylogenetics to establish the evolutionary distance of sequences retrieved from database searches. This software proceeds by mining functional information from a broad range of similar sequences, including ones with low sequence similarity identified by a PSI-BLAST search. It rearranges retrieved sequences according to their phylogenetic distance in order to assess functional similarity between identified sequences.
Generates a broad spectrum of Position-Specific Scoring Matrix (PSSM)-based numerical representation schemes for protein sequences. POSSUM implements a wide range of algorithms available in the literature, provides an easy-to-use interface, and allows users to derive and customize the descriptors. More than 20 types of PSSM profile-based feature descriptors can be generated. The software facilitates feature generation, analysis, training and benchmarking of machine-learning models and predictions.
Extracts features that are highly predictive of protein function. deepNF is a deep learning-based network fusion method for constructing a compact low-dimensional protein feature representation from a multitude of different networks types. It allows users to use out of-the-box machine learning classifiers such as Support Vector Machine (SVMs) to accurately annotate proteins with functional labels.
Provides an automatic function prediction script. SSP-LSDR is an application that uses sequence similarity profiles and label-space dimensionality reduction. It was built with similarity in gene ontology (GO) terms space as well as in protein sequence space. This method also improves critical assessment of functional annotation (CAFA) and cross-validation performances.
Creates a graph representing functional relevance of proteins considering their known functional, proteomics, and transcriptional features. iGFP offers a method to determine the function of the protein groups as well as function of individual proteins by iteratively updating grouping of proteins and functional assignments. It processes by using the single term that considers the coherence of the gene ontology (GO) term’s annotation.
Predicts protein function for a subset of classes from the Gene Ontology (GO) classification scheme. ProtFun is optimised for computational screening of the human genome (and possibly other eukaryotic genomes) for novel drug targets. It can be used for getting an idea of a possible function for proteins known to be involved in a particular disease. The tool can help the user to select appropriate assays.
An automatic algorithm for domain decomposition and clustering of all protein domain families. Alignments derived from an all-on-all sequence comparison are used to define domains within protein sequences based on a global maximum likelihood model.
A pipeline for automatic subfamily identification, followed by subfamily hidden Markov model (HMM) construction. A simple and computationally efficient scoring scheme using family and subfamily HMMs enables classification of novel sequences to protein families and subfamilies. Sequences representing entirely novel subfamilies are differentiated from those that can be classified to subfamilies in the input training set using logistic regression.
A web-server specifically trained to predict the proteins which are destined to localize in mitochondria in yeast and animals particularly. The prediction is made on basis of either occurrence of Pfam domain(s) or homology to an experimentally annotated proteins or ab-initio prediction on the basis of amino acid composition. Domain search is being done my HMMER (hidden Markov Models based search) while homology search by BLAST.
A SVM based tool for the classification of nuclear receptors on the basis of amino acid composition or dipeptide composition. The overall prediction accuracy of amino acid composition and dipeptide composition based methods is 82.6% and 97.2% respectively. The method can classify the nuclear receptors to these four families: Thyroid hormone like, HNF4-like, Estrogen like and Fushi tarazu-F1 like.
Assists users in predicting fertility-related proteins. PrESOgenesis is a two-layer classifier based on the support vector machine (SVM) method. At the first layer, each protein was sorted by SVM classifier to determine whether it is a fertility-related protein or not. If not, the classifier is automatically stopped. If yes, the sequence is considered as a fertility-related protein candidate and is subsequently submitted into the second layer.
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