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http://tools.dice-database.org/GOnet/)

Web tool for interactive Gene Ontology analysis of any biological data sources resulting in gene or protein lists.

Proper citation: GOnet (RRID:SCR_018977) Copy   

•   Source: SciCrunch Registry

https://github.com/sokrypton/ColabFold

Software application offers accelerated prediction of protein structures and complexes by combining homology search of MMseqs2 with AlphaFold2 or RoseTTAFold. Used for protein folding.

Proper citation: ColabFold (RRID:SCR_025453) Copy   

•   Source: SciCrunch Registry

https://repository.niddk.nih.gov/study/21

Data and biological samples were collected by this consortium organizing international efforts to identify genes that determine an individual risk of type 1 diabetes. It originally focused on recruiting families with at least two siblings (brothers and/or sisters) who have type 1 diabetes (affected sibling pair or ASP families). The T1DGC completed enrollment for these families in August 2009. They completed enrollment of trios (father, mother, and a child with type 1 diabetes), as well as cases (people with type 1 diabetes) and controls (people with no history of type 1 diabetes) from populations with a low prevalence of this disease in January 2010. T1DGC Data and Samples: Phenotypic and genotypic data as well as biological samples (DNA, serum and plasma) for T1DGC participants have been deposited in the NIDDKCentral Repositories for future research.

Proper citation: Type 1 Diabetes Genetics Consortium (RRID:SCR_001557) Copy   

•   Source: SciCrunch Registry

http://www.immunetolerance.org/

International clinical research consortium dedicated to the clinical evaluation of novel tolerogenic approaches for the treatment of autoimmune diseases, asthma and allergic diseases, and the prevention of graft rejection. They aim to advance the clinical application of immune tolerance by performing high quality clinical trials of emerging therapeutics integrated with mechanism-based research. In particular, they aim to: * Establish new tolerance therapeutics * Develop a better understanding of the mechanisms of immune function and disease pathogenesis * Identify new biomarkers of tolerance and disease Their goals are to identify and develop treatment game changers for tolerance modulating therapies for the treatment of immune mediated diseases and disabling conditions, and to conduct high quality, innovative clinical trials and mechanistic studies not likely to be funded by other sources or to be conducted by private industry that advance our understanding of immunological disorders. In the Immune Tolerance Network's (ITN) unique hybrid academic/industry model, the areas of academia, government and industry are integral to planning and conducting clinical studies. They develop and fund clinical trials and mechanistic studies in partnership. Their development model is a unique, interactive process. It capitalizes on their wide-ranging, multidisciplinary expertise provided by an advisory board of highly respected faculty from institutions worldwide. This model gives investigators special insight into developing high quality research studies. The ITN is comprised of leading scientific and medical faculty from more than 50 institutions in nine countries worldwide and employs over 80 full-time staff at the University of California San Francisco (UCSF), Bethesda, Maryland and Benaroya Research Institute in Seattle, Washington.

Proper citation: Immune Tolerance Network (ITN) (RRID:SCR_001535) Copy   

•   Source: SciCrunch Registry

http://cinteny.cchmc.org/

Online database for finding and analyzing syntenic regions across multiple genomes and measuring the extent of genome rearrangement using reversal distance as a measure.

Proper citation: Cinteny (RRID:SCR_002147) Copy   

•   Source: SciCrunch Registry

http://www.aspgd.org/

Database of genetic and molecular biological information about the filamentous fungi of the genus Aspergillus including information about genes and proteins of Aspergillus nidulans and Aspergillus fumigatus; descriptions and classifications of their biological roles, molecular functions, and subcellular localizations; gene, protein, and chromosome sequence information; tools for analysis and comparison of sequences; and links to literature information; as well as a multispecies comparative genomics browser tool (Sybil) for exploration of orthology and synteny across multiple sequenced Sgenus species. Also available are Gene Ontology (GO) and community resources. Based on the Candida Genome Database, the Aspergillus Genome Database is a resource for genomic sequence data and gene and protein information for Aspergilli. Among its many species, the genus contains an excellent model organism (A. nidulans, or its teleomorph Emericella nidulans), an important pathogen of the immunocompromised (A. fumigatus), an agriculturally important toxin producer (A. flavus), and two species used in industrial processes (A. niger and A. oryzae). Search options allow you to: *Search AspGD database using keywords. *Find chromosomal features that match specific properties or annotations. *Find AspGD web pages using keywords located on the page. *Find information on one gene from many databases. *Search for keywords related to a phenotype (e.g., conidiation), an allele (such as veA1), or an experimental condition (e.g., light). Analysis and Tools allow you to: *Find similarities between a sequence of interest and Aspergillus DNA or protein sequences. *Display and analyze an Aspergillus sequence (or other sequence) in many ways. *Navigate the chromosomes set. View nucleotide and protein sequence. *Find short DNA/protein sequence matches in Aspergillus. *Design sequencing and PCR primers for Aspergillus or other input sequences. *Display the restriction map for a Aspergillus or other input sequence. *Find similarities between a sequence of interest and fungal nucleotide or protein sequences. AspGD welcomes data submissions.

Proper citation: ASPGD (RRID:SCR_002047) Copy   

•   Source: SciCrunch Registry

https://med.stanford.edu/sfgf.html

Stanford Genomics formerly Stanford Functional Genomics Facility provides services for high throughput sequencing, single cell assays, gene expression and genotyping studies utilizing microarray and real time PCR, and related services. High throughput sequencing (Illumina HiSeq 4000, NextSeq 500, MiSeq and MiniSeq), microarray gene expression and genotyping services (Affymetrix, Agilent and Illumina). Provides 24/7 access to instruments, equipment and software utilized within genomics field.

Proper citation: Stanford Genomics Service Center Core Facility (RRID:SCR_002050) Copy   

•   Source: SciCrunch Registry

https://github.com/EpistasisLab/ReBATE

Open source software Python package to compare relief based feature selection algorithms used in data mining. Used for feature selection in any bioinformatics problem with potentially predictive features and target outcome variable, to detect feature interactions without examination of all feature combinations, to detect features involved in heterogeneous patterns of association such as genetic heterogeneity .

Proper citation: ReBATE (RRID:SCR_017139) Copy   

•   Source: SciCrunch Registry

https://bioconductor.org/packages/release/bioc/html/Maaslin2.html

SoftwareR package that identifies microbial taxa correlated with factors of interest using generalized linear models and mixed models.Used for efficiently determining multivariable association between clinical metadata and microbial meta'omic features.

Proper citation: MaAsLin2 (RRID:SCR_023241) Copy   

•   Source: SciCrunch Registry

https://github.com/ScilifelabDataCentre/node-pathogens-portal

Software package and code for Pathogen Portal node (i.e. a local Pathogens Portal, such as the Swedish and Dutch Pathogens Portals). Allows users to create their own node quickly and easily.

Proper citation: Pathogens Portal Node Toolbox (RRID:SCR_027086) Copy   

•   Source: SciCrunch Registry

https://pypi.org/project/pmlb/

Python wrapper for Penn Machine Learning Benchmark data repository. Large, curated repository of benchmark datasets for evaluating supervised machine learning algorithms. Part of PyPI https://pypi.org/

Proper citation: Penn machine learning benchmark repository (RRID:SCR_017138) Copy   

•   Source: SciCrunch Registry

https://med.nyu.edu/research/scientific-cores-shared-resources/ion-laboratory

Electrophysiology core facility that is part of Ion Channels and Transporters in Immunity Research Program.Research area includes ion channel and transporter function and ionic signaling in immune cells.Users who are studying other cell types or organ systems are welcome.Provides assistance with experimental design, training, implementation, and data analysis.

Proper citation: New York University School of Medicine IonLab Core Facility (RRID:SCR_021754) Copy   

•   Source: SciCrunch Registry

https://github.com/JLSteenwyk/ClipKIT

Software fast and flexible alignment trimming tool that keeps phylogenetically informative sites and removes others. Multiple sequence alignment-trimming algorithm for accurate phylogenomic inference.

Proper citation: ClipKIT (RRID:SCR_026411) Copy   

•   Source: SciCrunch Registry

http://www.broad.mit.edu/annotation/fungi/fgi/

Produces and analyzes sequence data from fungal organisms that are important to medicine, agriculture and industry. The FGI is a partnership between the Broad Institute and the wider fungal research community, with the selection of target genomes governed by a steering committee of fungal scientists. Organisms are selected for sequencing as part of a cohesive strategy that considers the value of data from each organism, given their role in basic research, health, agriculture and industry, as well as their value in comparative genomics.

Proper citation: Fungal Genome Initiative (RRID:SCR_003169) Copy   

•   Source: SciCrunch Registry

https://curie.utmb.edu/prosurf.html

Web server for predicting interacting sites on protein surfaces. Analyzes solvent-accessible residues likely to participate in PPIs.Predicts interacting amino acid residues in proteins that are most likely to interact with other proteins, given the 3D structures of subunits of protein complex.

Proper citation: InterProSurf (RRID:SCR_027791) Copy   

•   Source: SciCrunch Registry

http://www.genome.ou.edu/cneo.html

Cryptococcus neoformans is an encapsulated yeast that infects the human host via the respiratory tract where it usually causes an inapparent infection. In the susceptible host, it may disseminate, typically producing a chronic and life-threatening meningitis. The Cryptococcus neoformans serotypes A and D are responsible for the overwhelming majority of pulmonary infections in AIDS patients. Cryptococcus neoformans strain H99 Latest Data Release - May 19, 2004 To date, we have isolated ca. 3750 cDNA clones from Cryptococcus neoformans strain H99 in collaboration with Drs. Juneann Murphy and Dave Dyer at the University of Oklahoma Health Sciences Center''s Department of Microbiology and Immunology in Oklahoma City and Kent Buchanan at the Tulane University Medical School, New Orleans, LA. The Cryptococcus neoformans strain H99 EST''s have been generated by Doris Kupfer, Heather Bell, Sunkyoung So, Yuong Tang, and Jennifer Lewis at the University of Oklahoma''s Advanced Center for Genome Technology, in the Department of Chemistry and Biochemistry. We now have end sequenced all available templates (ca. 7500 reactions) from both ends of the directionally cloned inserts after excision into pBlueScript SK-. . All of our data is available from our ftp site, and we now have added the ability to perform blast searches on this data. A keyword search of a blastx search of GenBank with this data also is available but we have not yet linked this to a unigene database as the number of EST''s sequenced doesn''t warrent this yet.

Proper citation: Cryptococcus Neoformans cDNA Sequencing (RRID:SCR_008462) Copy   

•   Source: SciCrunch Registry

https://omics.pnl.gov/software/ms-gf

Software that performs peptide identification by scoring MS/MS spectra against peptides derived from a protein sequence database.

Proper citation: MS-GF+ (RRID:SCR_015646) Copy   

•   Source: SciCrunch Registry

http://www.cpc.unc.edu/projects/addhealth

Longitudinal study of a nationally representative sample of adolescents in grades 7-12 in the United States during the 1994-95 school year. Public data on about 21,000 people first surveyed in 1994 are available on the first phases of the study, as well as study design specifications. It also includes some parent and biomarker data. The Add Health cohort has been followed into young adulthood with four in-home interviews, the most recent in 2008, when the sample was aged 24-32. Add Health combines longitudinal survey data on respondents social, economic, psychological and physical well-being with contextual data on the family, neighborhood, community, school, friendships, peer groups, and romantic relationships, providing unique opportunities to study how social environments and behaviors in adolescence are linked to health and achievement outcomes in young adulthood. The fourth wave of interviews expanded the collection of biological data in Add Health to understand the social, behavioral, and biological linkages in health trajectories as the Add Health cohort ages through adulthood. The restricted-use contract includes four hours of free consultation with appropriate staff; after that, there''s a fee for help. Researchers can also share information through a listserv devoted to the database.

Proper citation: Add Health (National Longitudinal Study of Adolescent Health) (RRID:SCR_007434) Copy   

•   Source: SciCrunch Registry

http://www.jcvi.org/charprotdb/index.cgi/home

The Characterized Protein Database, CharProtDB, is designed and being developed as a resource of expertly curated, experimentally characterized proteins described in published literature. For each protein record in CharProtDB, storage of several data types is supported. It includes functional annotation (several instances of protein names and gene symbols) taxonomic classification, literature links, specific Gene Ontology (GO) terms and GO evidence codes, EC (Enzyme Commisssion) and TC (Transport Classification) numbers and protein sequence. Additionally, each protein record is associated with cross links to all public accessions in major protein databases as ��synonymous accessions��. Each of the above data types can be linked to as many literature references as possible. Every CharProtDB entry requires minimum data types to be furnished. They are protein name, GO terms and supporting reference(s) associated to GO evidence codes. Annotating using the GO system is of importance for several reasons; the GO system captures defined concepts (the GO terms) with unique ids, which can be attached to specific genes and the three controlled vocabularies of the GO allow for the capture of much more annotation information than is traditionally captured in protein common names, including, for example, not just the function of the protein, but its location as well. GO evidence codes implemented in CharProtDB directly correlate with the GO consortium definitions of experimental codes. CharProtDB tools link characterization data from multiple input streams through synonymous accessions or direct sequence identity. CharProtDB can represent multiple characterizations of the same protein, with proper attribution and links to database sources. Users can use a variety of search terms including protein name, gene symbol, EC number, organism name, accessions or any text to search the database. Following the search, a display page lists all the proteins that match the search term. Click on the protein name to view more detailed annotated information for each protein. Additionally, each protein record can be annotated.

Proper citation: CharProtDB: Characterized Protein Database (RRID:SCR_005872) Copy   

•   Source: SciCrunch Registry

http://cryptodb.org/cryptodb/

An integrated genomic and functional genomic database for the parasite Cryptosporidium. CryptoDB integrates whole genome sequence and annotation along with experimental data and environmental isolate sequences provided by community researchers. The database includes supplemental bioinformatics analyses and a web interface for data-mining. Organisms included in CryptoDB are Cryptosporidium parvum, Cryptosporidium hominis, Cryptosporidium muris and environmental isolate sequences from numerous species. CryptoDB is allied with the databases PlasmoDB and ToxoDB via ApiDB, an NIH/NIAID-funded Bioinformatics Resource Center. Tools include: * BLAST: Identify Sequence Similarities * Sequence Retrieval: Retrieve Specific Sequences using IDs and coordinates * PubMed and Entrez: View the Latest Cryptosporidium Pubmed and Entrez Results * Genome Browser: View Sequences and Features in the genome browser * CryptoCyc: Explore Automatically Defined Metabolic Pathways * Searches via Web Services: Web service access to our data

Proper citation: ApiDB CryptoDB (RRID:SCR_013455) Copy   

•   Source: SciCrunch Registry