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http://gladyshevlab.org/SelenoproteinPredictionServer/

Web server to predict eukaryotic selenoproteins and SECIS (SElenoCysteine Insertion Sequences) elements along nucleotide sequences. SECISearch3 replaces its predecessor SECISearch as a tool for prediction of eukaryotic SECIS elements. Seblastian is a method for selenoprotein gene detection that uses SECISearch3 and then predicts selenoprotein sequences encoded upstream of SECIS elements. Seblastian is able to both identify known selenoproteins and predict new selenoproteins.

Proper citation: SECISearch3 and Seblastian (RRID:SCR_003186) Copy   

•   Source: SciCrunch Registry

http://identifiers.org/

A system providing resolvable persistent Uniform Resource Identifiers (URIs) used to identify data for the scientific community, with a current focus on the Life Sciences domain. The provision of resolvable identifiers (URLs) fits well with the Semantic Web vision, and the Linked Data initiative. It provides direct access to the identified data using one chosen physical location (or resource). If more than one physical locations providing the data are recorded in the Registry, then you can access them via the top banner or by using a profile.

Proper citation: Identifiers.org (RRID:SCR_003735) Copy   

•   Source: SciCrunch Registry

http://www.homozygositymapper.org/

A web-based approach of homozygosity mapping that can handle tens of thousands markers. User can upload their own SNP genotype files to the database. Intuitive graphic interface is provided to view the homozygous stretches, with the ability of zooming into single chromosomes or user-defined chromosome regions. The underlying genotypes in all samples are displayed. The software is also integrated with our candidate gene search engine, GeneDistiller, so that users can interactively determine the most promising gene. (entry from Genetic Analysis Software)

Proper citation: HOMOZYGOSITYMAPPER (RRID:SCR_001714) Copy   

•   Source: SciCrunch Registry

http://avis.princeton.edu/pixie/index.php

bioPIXIE is a general system for discovery of biological networks through integration of diverse genome-wide functional data. This novel system for biological data integration and visualization, allows you to discover interaction networks and pathways in which your gene(s) (e.g. BNI1, YFL039C) of interest participate. The system is based on a Bayesian algorithm for identification of biological networks based on integrated diverse genomic data. To start using bioPIXIE, enter your genes of interest into the search box. You can use ORF names or aliases. If you enter multiple genes, they can be separated by commas or returns. Press ''submit''. bioPIXIE uses a probabilistic Bayesian algorithm to identify genes that are most likely to be in the same pathway/functional neighborhood as your genes of interest. It then displays biological network for the resulting genes as a graph. The nodes in the graph are genes (clicking on each node will bring up SGD page for that gene) and edges are interactions (clicking on each edge will show evidence used to predict this interaction). Most likely, the first results to load on the results page will be a list of significant Gene Ontology terms. This list is calculated for the genes in the biological network created by the bioPIXIE algorithm. If a gene ontology term appears on this list with a low p-value, it is statistically significantly overrepresented in this biological network. As you move the mouse over genes in the network, interactions involving these genes are highlighted. If you click on any of the highlighted interactions graph, evidence pop-up window will appear. The Evidence pop-up lists all evidence for this interaction, with links to the papers that produced this evidence - clicking these links will bring up the relevant source citation(s) in PubMed. You may need to download the Adobe Scalable Vector Graphic (SVG) plugin to utilize the visualization tool (you will be prompted if you need it).

Proper citation: bioPIXIE (RRID:SCR_004182) Copy   

•   Source: SciCrunch Registry

http://www.ncbi.nlm.nih.gov/tools/primer-blast/

A tool to design target-specific primers for polymerase chain reaction (PCR). It uses Primer3 to design PCR primers and then uses BLAST and global alignment algorithm to screen primers against user-selected database in order to avoid primer pairs (all combinations including forward-reverse primer pair, forward-forward as well as reverse-reverse pairs) that can cause non-specific amplifications.

Proper citation: Primer-BLAST (RRID:SCR_003095) Copy   

•   Source: SciCrunch Registry

https://www.hgmd.cf.ac.uk/ac/introduction.php?lang=english

Curated database of known (published) gene lesions responsible for human inherited disease.

Proper citation: Human Gene Mutation Database (RRID:SCR_001621) Copy   

•   Source: SciCrunch Registry

http://analysis2.bio-x.cn/myAnalysis.php

A powerful web-based platform for analyses of linkage disequilibrium, haplotype construction, and genetic association at polymorphism loci.

Proper citation: SHEsis: Analysis Tools For Random Samples (RRID:SCR_002958) Copy   

•   Source: SciCrunch Registry

http://weatherby.genetics.utah.edu/cgi-bin/Phevor/PhevorWeb.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on October 28,2025. Tool that integrates phenotype, gene function, and disease information with personal genomic data for improved power to identify disease-causing alleles. It works by combining knowledge resident in multiple biomedical ontologies with the outputs of variant prioritization tools. It does so using an algorithm that propagates information across and between ontologies. This process enables Phevor to accurately reprioritize potentially damaging alleles identified by variant prioritization tools in light of gene function, disease, and phenotype knowledge. Phevor is especially useful for single exome and family trio-based diagnostic analyses, the most commonly occurring clinical scenarios, and ones for which existing personal-genomes diagnostic tools are most inaccurate and underpowered. Phevor not only improves diagnostic accuracy for individuals presenting with established disease phenotypes, but also for those with previously undescribed and atypical disease presentations. Importantly, Phevor is not limited to known diseases, or known disease-causing alleles.

Proper citation: Phevor (RRID:SCR_002273) Copy   

•   Source: SciCrunch Registry

http://www.sharmuk.org/

A not for profit organization to accelerate research into aging by sharing resources: providing access to cost and time effective, aged murine tissue through a biorepository and database of live ageing colonies, as well as promoting the networking of researchers and dissemination of knowledge through its online collaborative environment; MiCEPACE. ShARM will provide valuable resources for the scientific community while helping to reduce the number of animals used in vital research into aging. The biobank of tissue and networking facility will enable scientists to access shared research material and data. By making use of collective resources, the number of individual animals required in research experiments can be minimized. The project also has the added value of helping to reduce the costs of research by connecting scientists, pooling resource and combining knowledge. ShARM works in partnership with MRC Harwell and the Centre for Intergrated Research into Musculoskeletal Ageing (CIMA).

Proper citation: ShARM (RRID:SCR_003120) Copy   

•   Source: SciCrunch Registry

http://tiger.dbs.nus.edu.sg/AllerHunter

A cross-reactive allergen prediction program built on a combination of Support Vector Machine (SVM) and pairwise sequence similarity. Cross-reactivity is based on similarity of proteins to allergens. However, not all proteins with similar sequence or structure to known allergens are cross-reactive allergens. AllerHunter aims to predict allergens and non-allergens with high sensitivity and specificity, without compromising efficiency at classification of proteins with similar sequence to known allergens. There are distinct differences between prediction of allergenicity and cross-reactivity of allergens. Allergenicity is the immunogenic potential of an allergen to induce IgE antibody production, whereas cross-reactivity is the potential of a substance to bind to IgE previously induced by a known allergen. It is difficult to predict allergenicity because causes of immunogenicity of allergens are still not completely clear. However it is possible to predict cross-reactivity since it implies similarity in IgE binding sites. Please provide protein sequence in fasta format.

Proper citation: AllerHunter: Cross-reactive Allergen Prediction Home (RRID:SCR_002950) Copy   

•   Source: SciCrunch Registry

http://elgar.ucsd.edu/software/magi/

A web service for fast microRNA-Seq data analysis in a GPU infrastructure.

Proper citation: MAGI (RRID:SCR_003360) Copy   

•   Source: SciCrunch Registry

http://www.inmex.ca./INMEX/

A web-based tool to support meta-analysis of multiple gene-expression data sets, as well as to enable integration of data sets from gene expression and metabolomics experiments. INMEX contains three functional modules. The data preparation module supports flexible data processing, annotation and visualization of individual data sets. The statistical analysis module allows researchers to combine multiple data sets based on P-values, effect sizes, rank orders and other features. The significant genes can be examined in functional analysis module for enriched Gene Ontology terms or Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, or expression profile visualization. INMEX has built-in support for common gene/metabolite identifiers (IDs), as well as 45 popular microarray platforms for human, mouse and rat. Complex operations are performed through a user-friendly web interface in a step-by-step manner.

Proper citation: INMEX (RRID:SCR_004173) Copy   

•   Source: SciCrunch Registry

http://eyegene.ophthy.med.umich.edu/madeline/

Software tool designed for preparing, visualizing, and exploring human pedigree data used in genetic linkage studies. It converts pedigree and marker data into formats required by popular linkage analysis packages, provides powerful ways to query pedigree data sets, and produces Postscript pedigree drawings that are useful for rapid data review.

Proper citation: MADELINE (RRID:SCR_001979) Copy   

•   Source: SciCrunch Registry

http://lcg.rit.albany.edu/dp-bind

This web-server takes a user-supplied sequence of a DNA-binding protein and predicts residue positions involved in interactions with DNA. Prediction can be performed using a profile of evolutionary conservation of the input sequence automatically generated by the web-server or the input sequence alone. Three prediction methods are run for each input sequence and consensus prediction is generated.

Proper citation: DP-Bind: a web server for sequence-based prediction of DNA-binding residues in DNA-binding proteins (RRID:SCR_003039) Copy   

•   Source: SciCrunch Registry

http://www.wormatlas.org/

Anatomical atlas about structural anatomy of Caenorhabditis elegans. Provides simple interface allowing user to easily navigate through every anatomical structure of worm. Contains set of images which can be sorted by different characteristics: sex, genotype, age, body portion or tissue type. Includes links to other major worm websites and databases. Application for viewing and downloading thousands of unpublished electron micrographs and associated data. These images have been generated by several labs in the C. elegans community, including the MRC, the Hall lab (Center for C. elegans Anatomy), and the Culotti and Riddle labs.

Proper citation: WormAtlas (RRID:SCR_002861) Copy   

•   Source: SciCrunch Registry

http://kt.ijs.si/software/SEGS/

A web tool for descriptive analysis of microarray data. The analysis is performed by looking for descriptions of gene sets that are statistically significantly over- or under-expressed between different scenarios within the context of a genome-scale experiments (DNA microarray). Descriptions are defined by using the terms from the Gene Ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and gene-gene interactions found in the ENTREZ database. Gene annotations by GO and KEGG terms can also be found in the ENTREZ database. The tool provides three procedures for testing the enrichment of the gene sets (over- or under-expressed): Fisher's exact test, GSEA and PAGE, and option for combining the results of the tests. Because of the multiple-hypothesis testing nature of the problem, all the p-values are computed using the permutation testing method.

Proper citation: SEGS (RRID:SCR_003554) Copy   

•   Source: SciCrunch Registry

http://viperdb.scripps.edu/

Database for icosahedral virus capsid structures. The emphasis of the resource is on providing data from structural and computational analyses on these systems, as well as high quality renderings for visual exploration. In addition, all virus capsids are placed in a single icosahedral orientation convention, facilitating comparison between different structures. The web site includes powerful search utilities , links to other relevant databases, background information on virus capsid structure, and useful database interface tools. It is an information source for the analysis of high resolution virus structures. VIPERdb is a one-stop site dedicated to helping users around the world examine the many icosahedral virus structures contained within the Protein Data Bank (PDB) by providing them with an easy to use database containing current data and a variety of analytical tools. Sponsors: VIPERdb is funded by the NIH., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: VIPERdb (RRID:SCR_002853) Copy   

•   Source: SciCrunch Registry

http://webdocs.cs.ualberta.ca/~bioinfo/PA/Sub/

Web server specialized to predict the subcellular localization of proteins using established machine learning techniques.

Proper citation: Proteome Analyst Specialized Subcellular Localization Server (RRID:SCR_003143) Copy   

•   Source: SciCrunch Registry

https://www.ctrnet.ca/

The Canadian Tumour Repository Network (CTRNet) is a not-for-profit consortium of leading provincial tumor banks and programs that furthers Canadian health research. CTRNet provides interested researchers with a streamlined process to obtain quality human tissue and human tissue products from member tumor banks. The benefits of working with CTRNet.ca include: * The ability for researchers to search for quality controlled tissue samples from Canada''''s leading tumor banks in one central location and for biobanks to display and make their biospecimens available for research users. * Learning opportunities in tissue handling, research design and relevant technology training and innovations. * Invitation to CTRNet workshops and conferences. * A resource for current news links and discussion forums about cancer research and tumor banking. The vision of CTRNet is to create new opportunities for translational cancer research that will improve cancer outcomes in Canada and beyond. Through the creation and maintenance of an online pan-Canadian network, researchers will gain unprecedented access to a new and comprehensive source of tissue and clinical data. The network is a sustainable resource and operates according to the highest scientific and ethical standards for research to benefit all Canadians. Currently CTRNet has the following member banks: * CBCF Tumor Bank Alberta * Manitoba Tumour Bank * Ontario Tumour Bank * British Columbia Cancer Agency Tumour Tissue Repository (BCCA-TTR) * Le r��seau de recherche en cancer du FRSQ * NCIC Clinical Trials Group

Proper citation: Canadian Tumour Repository Network (RRID:SCR_004192) Copy   

•   Source: SciCrunch Registry

http://nar.oxfordjournals.org/content/34/suppl_2/W635.long

THIS RESOURCE IS NO LONGER IN SERVICE, documented on August 9, 2016. A web server that allows users to efficiently identify and prioritize high-risk SNPs according to their phenotypic risks and putative functional effects. A unique feature is that the functional effect information used for SNP prioritization is always up-to-date, because FASTSNP extracts the information from 11 external web servers at query time using a team of web wrapper agents. Moreover, FASTSNP is extendable by deploying more Web wrapper agents. FASTSNP provides three options for users to submit requests. If users already have some candidate SNPs on a candidate gene, they may use Query by Candidate Gene to select the specific SNPs on the gene to perform prioritization. If users have a specified SNP or a list of SNP rsid's needs to be prioritized, they can use Query by SNP option and upload the SNP list in an Excel-format file. Finally, if users have a novel SNP sequence, FASTSNP provides Novel SNP analysis. FASTSNP will generate a SNP Function Report for each SNP. Users can export SNP data to an excel file for further genotyping processes. Other features of FASTSNP include SNP quality checking and haplotype LD information.

Proper citation: FastSNP (RRID:SCR_003140) Copy   

•   Source: SciCrunch Registry