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http://bioinformatics.biol.uoa.gr/ConBBPRED/

A web tool for the Consensus Prediction of TransMembrane Beta-Barrel Proteins. Prediction of the transmembrane strands and topology of beta-barrel outer membrane proteins is of interest in current bioinformatics research. Several methods have been applied so far for this task, utilizing different algorithmic techniques and a number of freely available predictors exist. The methods can be grossly divided to those based on Hidden Markov Models (HMMs), on Neural Networks (NNs) and on Support Vector Machines (SVMs). In this work, we compare the different available methods for topology prediction of beta-barrel outer membrane proteins. We evaluate their performance on a non-redundant dataset of 20 beta-barrel outer membrane proteins of gram-negative bacteria, with structures known at atomic resolution. Also, we describe, for the first time, an effective way to combine the individual predictors, at will, to a single consensus prediction method. We assess the statistical significance of the performance of each prediction scheme and conclude that Hidden Markov Model based methods, HMM-B2TMR, ProfTMB and PRED-TMBB, are currently the best predictors, according to either the per-residue accuracy, the segments overlap measure (SOV) or the total number of proteins with correctly predicted topologies in the test set. Furthermore, we show that the available predictors perform better when only transmembrane beta-barrel domains are used for prediction, rather than the precursor full-length sequences, even though the HMM-based predictors are not influenced significantly. The consensus prediction method performs significantly better than each individual available predictor, since it increases the accuracy up to 4% regarding SOV and up to 15% in correctly predicted topologies.

Proper citation: ConBBPRED (RRID:SCR_006194) Copy   

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http://web.cbio.uct.ac.za/ITGOM/

The Integrated Tool for IC-based GO Semantic Similarity Measures (IT-GOM) integrates the currently known GO semantic similarity measures into a single tool. It provides the information content (IC) of GO terms, semantic similarity between GO terms and GO-based protein functional similarity scores. The specificity of GO terms and the similarity of biological content between GO terms or proteins are transformed into numeric values for protein analyses at the functional level. The integration of the different measures enables users to choose the measure best suited to their application and to compare results between different semantic similarity measures. Platform: Online tool

Proper citation: IT-GOM: Integrated Tool for IC-based GO Semantic Similarity Measures (RRID:SCR_005815) Copy   

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http://probeexplorer.cicancer.org/principal.php

Probe Explorer is an open access web-based bioinformatics application designed to show the association between microarray oligonucleotide probes and transcripts in the genomic context, but flexible enough to serve as a simplified genome and transcriptome browser. Coordinates and sequences of the genomic entities (loci, exons, transcripts), including vector graphics outputs, are provided for fifteen metazoa organisms and two yeasts. Alignment tools are used to built the associations between Affymetrix microarrays probe sequences and the transcriptomes (for human, mouse, rat and yeasts). Search by keywords is available and user searches and alignments on the genomes can also be done using any DNA or protein sequence query. Platform: Online tool

Proper citation: ProbeExplorer (RRID:SCR_007116) Copy   

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http://funspec.med.utoronto.ca/

FunSpec is a web-based tool for statistical evaluation of groups of genes and proteins (e.g. co-regulated genes, protein complexes, genetic interactors) with respect to existing annotations, including GO terms. FunSpec (an acronym for Functional Specification) inputs a list of yeast gene names, and outputs a summary of functional classes, cellular localizations, protein complexes, etc. that are enriched in the list. The classes and categories evaluated were downloaded from the MIPS Database and the GO Database . In addition, many published datasets have been compiled to evaluate enrichment against. Hypertext links to the publications are given. The p-values, calculated using the hypergeometric distribution, represent the probability that the intersection of given list with any given functional category occurs by chance. The Bonferroni-correction divides the p-value threshold, that would be deemed significant for an individual test, by the number of tests conducted and thus accounts for spurious significance due to multiple testing over the categories of a database. After the Bonferroni correction, only those categories are displayed for which the chance probability of enrichment is lower than: p-value/#CD where #CD is the number of categories in the selected database. Without the Bonferroni Correction, all categories are displayed for which the same probability of enrichment is lower than: p-value threshold in an individual test Note that many genes are contained in many categories, especially in the MIPS database (which are hierarchical) and that this can create biases for which FunSpec currently makes no compensation. Also the databases are treated as independent from one another, which is really not the case, and each is searched seperately, which may not be optimal for statistical calculations. Nonetheless, we find it useful for sifting through the results of clustering analysis, TAP pulldowns, etc. Platform: Online tool

Proper citation: FunSpec (RRID:SCR_006952) Copy   

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http://www.cs.ualberta.ca/~bioinfo/PA/GOSUB/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on June 30, 2015. Refer to Proteome Analyst 3.0. Subcellular Localization and GO General Molecular Function predictions for many model organism proteomes using Protein Analyst, with a very high coverage rate. When users blast their proteins against the database of results, they will not only be shown blast homologs from the model organisms, but also the Subcellular Localization and GO General Molecular Function predictions as well.

Proper citation: Proteome Analyst PA-GOSUB (RRID:SCR_008234) Copy   

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http://www.ebi.ac.uk/Tools/blast2/index.html

It is used to compare a novel sequence with those contained in nucleotide and protein databases by aligning the novel sequence with previously characterized genes.

Proper citation: Washington University Basic Local Alignment Search Tool (RRID:SCR_008285) Copy   

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http://www.uwstructuralgenomics.org/

It is a specialized research center supported by the Protein Structure Initiative (PSI) of the National Institute of General Medical Sciences (NIGMS), one of the National Institutes of Health (NIH). PSI is a federal, university, and industry effort aimed at dramatically reducing the costs and lessening the time it takes to determine a three-dimensional protein structure. The long-range goal of PSI is to solve 10,000 protein structures in 10 years and to make the three-dimensional atomic-level structures of most proteins easily obtainable from knowledge of their corresponding DNA sequences. CESG is located within the Department of Biochemistry at the University of Wisconsin-Madison (Madison, WI) and the Department of Biochemistry at the Medical College of Wisconsin (Milwaukee, WI). CESG develops new methods and technologies to address unique eukaryotic bottlenecks and disseminates its methodologies and experimental results to the scientific community worldwide through: :- Cell-Free Protein Production Workshops :- Plasmids at PSI Materials Repository :- Posters Presented at Scientific Meetings :- Publications in PubMed / PubMed Central :- Sesame (LIMS) Available for Researchers :- Solved Structures in the Protein Data Bank :- Technology Dissemination Reports They have welcomed requests by researchers to solve eukaryotic protein structures, particularly medically relevant proteins, through our Online Structure Request System for Researchers. They have solved many community-nominated targets and deposited information about these targets in public databases and published on our investigations and findings. Sponsors: CESG is supported by NIH / NIGMS Protein Structure Initiative grant numbers U54 GM074901 and P50 GM064598.

Proper citation: CESG (RRID:SCR_008451) Copy   

•   Source: SciCrunch Registry

https://cryosparc.com/

Software integrated platform used for obtaining 3D structural information from single particle cryo-EM data. Enables automated, high quality and high-throughput structure discovery of proteins, viruses and molecular complexes for research and drug discovery.

Proper citation: cryoSPARC (RRID:SCR_016501) Copy   

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http://saxs.ifsc.usp.br/

Software tool as online calculator of molecular weight of proteins in dilute solution from experimental SAXS data measured on relative scale. Software package for easy processing of small angle X ray scattering data from mono disperse systems in diluted solution.

Proper citation: SAXS Molecular Weight (RRID:SCR_018137) Copy   

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https://github.com/lufuhao/AutoEVM

Software tool as Autorun Evidence Modeler. Requires EVidenceModeler (aka EVM) software which combines ab into gene predictions and protein and transcript alignments into weighted consensus gene structures.

Proper citation: AutoEVM (RRID:SCR_017556) Copy   

•   Source: SciCrunch Registry

https://www.thegpm.org/crap/

List of proteins commonly found in proteomics experiments that are present either by accident or through unavoidable contamination of protein samples. List is based on analysis of current version of GPMDB, as well as suggestions by users. Current version of cRAP in FASTA format can be obtained from the GPM FTP site.

Proper citation: cRAP protein sequences (RRID:SCR_018187) Copy   

•   Source: SciCrunch Registry

http://www.cbs.dtu.dk/services/NetPhos/

Web tool as artificial neural network method that predicts phosphorylation sites in independent sequences. Web application based on determination of activity of protein kinases using in vitro assays with either naturally occurring peptides or synthetic peptides. NetPhos 3.1 server predicts serine, threonine or tyrosine phosphorylation sites in eukaryotic proteins using ensembles of neural networks. Both generic and kinase specific predictions are performed. Generic predictions are identical to predictions performed by NetPhos 2.0. Kinase specific predictions are identical to predictions by NetPhosK 1.0. NetPhos 3.1 is available as stand-alone software package.

Proper citation: NetPhos (RRID:SCR_017975) Copy   

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http://himc.stanford.edu

Core designed for immune monitoring services for clinical and translational studies. Goals include providing standardized, state-of-the art immune monitoring assays at RNA, protein, and cellular level, testing and developing new technologies for immune monitoring, archive, report, and mine data from immune monitoring studies. HIMC uses online database for integration of data from standard HIMC assays, along with de-identified clinical and demographic data.

Proper citation: Stanford University Human Immune Monitoring Center Core Facility (RRID:SCR_018266) Copy   

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http://www.cbs.dtu.dk/services/ProP/

Web application which predicts arginine and lysine propeptide cleavage sites in eukaryotic protein sequences using an ensemble of neural networks. Furin-specific prediction is the default. It is also possible to perform a general proprotein convertase prediction.

Proper citation: ProP Server (RRID:SCR_014936) Copy   

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http://hollow.sourceforge.net/

HOLLOW facilitates the production of surface images of proteins. HOLLOW is a portable command-line utility written in Python 2.4-2.7; it does not have any other dependencies (although running under the PyPy JIT interpreter, it runs much faster). The input is a PDB file. The output is a PDB file of dummy water atoms that forms a cast of the voids and channels of a protein. HOLLOW generates a surface from a cast of the protein surface. HOLLOW fills the interior spaces of a protein structure with dummy atoms defined on an overlapping grid. The surface generated by these dummy atoms can be shown to reproduce the surface of the protein at the ideal limit. The use of the surface of the dummy atoms allows us to focus on a specific piece of the interior surface. Simply by deleting dummy atoms, the interior surface can be trimmed to produce a custom portion of the interior space. For advanced coloring of the surface, the B-factor of the dummy atoms can be calculated as the average of the B-factor of the protein atoms surrounding the dummy atoms. This allows various colorings of the surface to be conveyed through the B-factor field of the PDB files. The volume filling representation facilitated by HOLLOW is meant to complement other programs that identify voids, pockets and channels, such as SPHGEN and CASTp, which identify binding sites but cannot produce output that can be rendered in standard molecular graphics software. HOLLOW can be used to help render these binding pockets.

Proper citation: HOLLOW (RRID:SCR_005729) Copy   

•   Source: SciCrunch Registry

http://www.pondr.com/

Web tool to predict order and disorder from amino acid sequence. Used to predict of natural disordered regions in proteins.

Proper citation: PONDR (RRID:SCR_023691) Copy   

•   Source: SciCrunch Registry

https://fuzdrop.bio.unipd.it/predictor

Web tool to predict probability of proteins to undergo liquid-liquid phase separation.Used to perform sequence based identification of both droplet promoting regions and of aggregation promoting regions within droplets. Used to predict droplet promoting regions and proteins, which can spontaneously phase separate.

Proper citation: FuzDrop (RRID:SCR_023675) Copy   

•   Source: SciCrunch Registry

https://github.com/eduardporta/e-Driver

Software tool to identify cancer driver genes based on linear annotations of biological regions such as protein domains.Uses information on three-dimensional structures of mutated proteins to identify specific structural features. Then algorithm analyzes whether these features are enriched in cancer somatic mutations and are candidate driver genes.

Proper citation: e-Driver (RRID:SCR_002674) Copy   

•   Source: SciCrunch Registry

http://mapman.gabipd.org/web/guest/mapman

Software tool that displays large genomics datasets (e.g. gene expression data from Arabidopsis Affymetrix arrays) onto diagrams of metabolic pathways or other biological processes.

Proper citation: MapMan (RRID:SCR_003543) Copy   

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http://www.sanger.ac.uk/resources/software/vagrent/

Software tool set for calculating the biological consequences of genomic variations. The suite of perl modules compares genomic variations with reference genome annotations and generates the possible effects each variant may have on the transcripts it overlaps. It evaluates each variation/transcript combination and describes the effects in the mRNA, CDS and protein sequence contexts. It provides details of the sequence and position of the change within the transcript / protein as well as Sequence Ontology terms to classify its consequences.

Proper citation: VAGrENT (RRID:SCR_005180) Copy   

•   Source: SciCrunch Registry