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http://www.imtech.res.in/raghava/bhairpred/

Bhairpred server is based on machine learning technique SVM using single sequence information, evolutionary profile, predicted and observed secondary structure (as obtained using Psipred and DSSP), predicted and observed accessibility values (as obtainned from Netasa and DSSP). The methods were trained and tested on dataset of 2880 proteins and their performance was evaluated on dataset of 534 proteins used by Thornton (PNAS, 2002). Best prediction results were obtained with hybrid approach that combined prediction results from evolutionary profile, predicted secondary structure and accessibility.

Proper citation: SVM based method for predicting beta hairpin structures in proteins (RRID:SCR_008349) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

http://blast.ncbi.nlm.nih.gov/Blast.cgi?PROGRAM=tblastn&PAGE_TYPE=BlastSearch&LINK_LOC=blasthome

Tool to search translated nucleotide databases using a protein query.

Proper citation: TBLASTN (RRID:SCR_011822) Copy   

•   Source: SciCrunch Registry

http://www.imtech.res.in/raghava/hslpred/

A support vector machine (SVM)-based method for the prediction of 4 major subcellular localization (cytoplasm, mitochondrial, nuclear and plasma membrane) of human proteins using various features such as i) amino acid composition, ii) dipeptide composition and iii) evolutionary information of proteins.

Proper citation: HSLPred (RRID:SCR_011972) Copy   

•   Source: SciCrunch Registry

http://jci-bioinfo.cn/iLoc-Plant

Data analysis service for predicting subcellular localization of plant proteins with single and multiple sites.

Proper citation: iLoc-Plant (RRID:SCR_011973) Copy   

•   Source: SciCrunch Registry

http://bio-cluster.iis.sinica.edu.tw/kbloc/index.html

A knowledge-based data analysis service to predict the localization site(s) of both single-localized and multi-localized proteins.

Proper citation: KnowPredsite (RRID:SCR_011974) Copy   

•   Source: SciCrunch Registry

http://bleoberis.bioc.cam.ac.uk/mcsm

Data analysis service to the study of missense mutations which relies on graph-based signatures.

Proper citation: mCSM (RRID:SCR_010776) Copy   

•   Source: SciCrunch Registry

http://gpcr.biocomp.unibo.it/bacello/

A predictor for the subcellular localization of proteins in eukaryotes that is based on a decision tree of several support vector machines (SVMs). It classifies up to four localizations for Fungi and Metazoan proteins and five localizations for Plant ones. BaCelLo's predictions are balanced among different classes and all the localizations are considered as equiprobable.

Proper citation: BaCelLo (RRID:SCR_011965) Copy   

•   Source: SciCrunch Registry

http://www.csbio.sjtu.edu.cn/bioinf/Cell-PLoc/

A package of web-servers for predicting subcellular localization of proteins in different organisms.

Proper citation: Cell-PLoc (RRID:SCR_011966) Copy   

•   Source: SciCrunch Registry

http://cello.life.nctu.edu.tw/

A subCELlular LOcalization predictor based on a multi-class support vector machine (SVM) classification system. CELLO uses 4 types of sequence coding schemes: the amino acid composition, the di-peptide composition, the partitioned amino acid composition and the sequence composition based on the physico-chemical properties of amino acids. They combine votes from these classifiers and use the jury votes to determine the final assignment.

Proper citation: CELLO (RRID:SCR_011968) Copy   

•   Source: SciCrunch Registry

https://www.livercellatlas.org

Portal to search liver single cell RNA-sequencing datasets. Datasets for expression of genes or proteins (when CITE-seq was performed). To search for gene enter the official gene name. To search for protein please click to see specific names to use for different markers included.

Proper citation: Liver cell atlas (RRID:SCR_023627) 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://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

http://compbio.cs.princeton.edu/concavity/

Software for predicting protein ligand binding sites that integrate evolutionary sequence conservation estimates with structure-based methods for identifying protein surface cavities. Used in predicting catalytic sites and drug binding pockets.

Proper citation: Concavity (RRID:SCR_016063) Copy   

•   Source: SciCrunch Registry

http://compbio.cs.princeton.edu/conservation/

Software for scoring protein sequence conservation using the Jensen-Shannon divergence. It can be used to predict catalytic sites and residues near bound ligands.

Proper citation: Conservation (RRID:SCR_016064) Copy   

•   Source: SciCrunch Registry

http://www.glycosciences.de/glycocd/

Manually curated, comprehensive repository of clusters of differentiation (CDs) which are a) defined as distinct oligosaccharide sequences as part of either glycoproteins and/or glycosphingolipids and b) defined as proteins which have carbohydrate recognition sites (CRDs) or as carbohydrate binding lectins. The data base is generated by exhaustive search of literature and other online data banks related to carbohydrates and proteins. This data bank is the beginning of an effort to provide concise, relevant information of carbohydrate-related CDs in a user- friendly manner. For users convenience the data bank under menu browse of GlycoCD is arranged in two section namely carbohydrate recognition CDs (CRD CD) and glycan CD. The carbohydrate recognition CD part is the collection of proteins which recognize glycan structures by means of the CRDs. Glycan CD is the part in which CDs are summarized which characterize specific oligosaccharide structures. The GlycoCD databank has been developed with the aim to assist the immunologist, cell biologist as well as the clinician who wants to keep up with the present knowledge in this field of glycobiology.

Proper citation: Glyco-CD (RRID:SCR_001574) Copy   

•   Source: SciCrunch Registry

http://nmrresource.ucsd.edu/

Biomedical technology research center that develops new technology for NMR spectroscopy and makes it available to the biomedical research community for structure determination of proteins in biological supramolecular assemblies, such as membrane proteins or virus particles. The principal applications are to membrane-associated proteins; however, the approach is generally applicable to polypeptides that cannot be prepared in forms suitable for X-ray crystallography or multidimensional solution NMR spectroscopy. As a result, there are also applications to viruses and other biological systems. The principal instrumentation consists of high-field NMR spectrometers dedicated to high-resolution solid-state NMR spectroscopy. The spectrometers are capable of the full-range of multiple-resonance experiments on stationary and spinning samples; however, the major emphasis is on methods that utilize mechanically or magnetically oriented samples. Development encompasses preparation of samples, including: * Expression and purification of membrane proteins * Design and construction of instrumentation, especially probes * Implementation of new pulse sequences and other experimental protocols for solid-state NMR spectroscopy * Calculations for the processing of experimental data and protein structure determination from the orientational constraints derived from these data

Proper citation: UCSD Center for NMR Spectroscopy and Imaging of Proteins (RRID:SCR_001401) Copy   

•   Source: SciCrunch Registry

https://www.tamuk.edu/agriculture/institutes-and-other-units/nntrc/Products-Services.html

Center to provide global research, training, and resources that will lead to the discovery of medically important toxins found in venoms. The Viper Resource Center (VRC) is located in the Natural Toxins Research Center at Texas A&M University-Kingsville.

Proper citation: National Natural Toxins Research Center (RRID:SCR_002824) Copy   

•   Source: SciCrunch Registry

http://www.uniprot.org/program/Chordata

Data set of manually annotated chordata-specific proteins as well as those that are widely conserved. The program keeps existing human entries up-to-date and broadens the manual annotation to other vertebrate species, especially model organisms, including great apes, cow, mouse, rat, chicken, zebrafish, as well as Xenopus laevis and Xenopus tropicalis. A draft of the complete human proteome is available in UniProtKB/Swiss-Prot and one of the current priorities of the Chordata protein annotation program is to improve the quality of human sequences provided. To this aim, they are updating sequences which show discrepancies with those predicted from the genome sequence. Dubious isoforms, sequences based on experimental artifacts and protein products derived from erroneous gene model predictions are also revisited. This work is in part done in collaboration with the Hinxton Sequence Forum (HSF), which allows active exchange between UniProt, HAVANA, Ensembl and HGNC groups, as well as with RefSeq database. UniProt is a member of the Consensus CDS project and thye are in the process of reviewing their records to support convergence towards a standard set of protein annotation. They also continuously update human entries with functional annotation, including novel structural, post-translational modification, interaction and enzymatic activity data. In order to identify candidates for re-annotation, they use, among others, information extraction tools such as the STRING database. In addition, they regularly add new sequence variants and maintain disease information. Indeed, this annotation program includes the Variation Annotation Program, the goal of which is to annotate all known human genetic diseases and disease-linked protein variants, as well as neutral polymorphisms.

Proper citation: UniProt Chordata protein annotation program (RRID:SCR_007071) Copy   

•   Source: SciCrunch Registry

https://www.moffitt.org/research-science/shared-resources/proteomics-and-metabolomics/

Provides instrumentation for proteomics and metabolomics studies, including protein, peptide and metabolite separations, MS instrumentation for protein, peptide and metabolite analysis, and data systems, software, and bioinformatics tools for data archiving and analysis. Proteomics Core performs routine analytical proteomics services, including target discovery, identification and quantitation, and also provides platforms for functional proteomics using variety of strategies for protein separation, sub-proteome enrichment, post-translational modification analysis, and quantitation.

Proper citation: Moffitt Cancer Center Proteomics and Metabolomics Core Facility (RRID:SCR_012168) Copy   

•   Source: SciCrunch Registry