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http://hapmap.ncbi.nlm.nih.gov/

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 22, 2016. A multi-country collaboration among scientists and funding agencies to develop a public resource where genetic similarities and differences in human beings are identified and catalogued. Using this information, researchers will be able to find genes that affect health, disease, and individual responses to medications and environmental factors. All of the information generated by the Project will be released into the public domain. Their goal is to compare the genetic sequences of different individuals to identify chromosomal regions where genetic variants are shared. Public and private organizations in six countries are participating in the International HapMap Project. Data generated by the Project can be downloaded with minimal constraints. HapMap project related data, software, and documentation include: bulk data on genotypes, frequencies, LD data, phasing data, allocated SNPs, recombination rates and hotspots, SNP assays, Perlegen amplicons, raw data, inferred genotypes, and mitochondrial and chrY haplogroups; Generic Genome Browser software; protocols and information on assay design, genotyping and other protocols used in the project; and documentation of samples/individuals and the XML format used in the project.

Proper citation: International HapMap Project (RRID:SCR_002846) Copy   

•   Source: SciCrunch Registry

http://fcon_1000.projects.nitrc.org/indi/CoRR/html/

Consortium that has aggregated resting state fMRI (R-fMRI) and diffusion imaging data from laboratories around the world, creating an open science resource for the imaging community, that facilitates the assessment of test-retest reliability and reproducibility for functional and structural connectomics. Given that this was a retrospective data collection, they have focused on basic phenotypic measures that are relatively standard in the neuroimaging field, as well as fundamental for analyses and sample characterization. Their phenotypic key is organized to reflect three classifications of variables: 1) core (i.e., minimal variables required to characterize any dataset), 2) preferred (i.e., variables that were strongly suggested for inclusion due to their relative import and/or likelihood of being collected by most sites), and 3) optional (variables that are data-set specific or only shared by a few sites). CoRR includes 33 datasets consisting of: * 1629 Subjects * 3357 Anatomical Scans * 5093 Resting Functional Scans * 1302 Diffusion Scans * 300 CBF and ASL Scans

Proper citation: Consortium for Reliability and Reproducibility (RRID:SCR_003774) Copy   

•   Source: SciCrunch Registry

http://www.immunoinformatics.net/HLAsupE/

Database of HLA supertype-specific epitopes. It describes major histocompatibility complex (MHC) molecules that bind short peptides derived from endogenous or exogenous antigens and present them onto the surface of antigen-presenting cells (APCs) for T-cell receptor (TCR) recognition.

Proper citation: HLAsupE (RRID:SCR_016277) Copy   

•   Source: SciCrunch Registry

http://bio-bigdata.hrbmu.edu.cn/lnc2cancer/

Manually curated database of experimentally supported lncRNAs associated with various human cancers. Cancer long non coding RNA database. Lnc2Cancer 3.0 is updated resource for experimentally supported lncRNA/circRNA cancer associations and web tools based on RNA-seq and scRNA-seq data.

Proper citation: lnc2cancer (RRID:SCR_023781) Copy   

•   Source: SciCrunch Registry

http://www.picb.ac.cn/hanlab/iNPS.html

Software for nucleosome detection that builds on the NPS software. Its application to T-cell activation data demonstrates a greater ability to facilitate detection of nucleosome repositioning, uncovering additional biological features underlying the activation process., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: iNPS (RRID:SCR_015750) Copy   

•   Source: SciCrunch Registry

http://datf.cbi.pku.edu.cn/

Database that collects all arabidopsis transcription factors (totally 1922 Loci; 2290 Gene Models) and classifies them into 64 families. It uses not only locus (gene), but also gene model (transcript, protein) and the detail information is for each gene model not for locus. It adds multiple alignment of the DNA-binding domain of each family, Neighbor-Joining phylogenetic tree of each family, the GO annotation, homolog with the Database of Rice Transcription Factors (DRTF). It also keeps old information items such as the unique cloned and sequenced information of about 1200 transcription factors, protein domains, 3D structure information with BLAST hits against PDB, predicted Nuclear Location Signals, UniGene information, as well as links to literature reference.

Proper citation: Database of Arabidopsis Transcription Factors (RRID:SCR_007101) Copy   

•   Source: SciCrunch Registry

http://fcon_1000.projects.nitrc.org/indi/retro/BeijingEOEC.html

Data set of 48 healthy controls from a community (student) sample from Beijing Normal University in China with 3 resting state fMRI scans each. During the first scan participants were instructed to rest with their eyes closed. The second and third resting state scan were randomized between resting with eyes open versus eyes closed. In addition this dataset contains a 64-direction DTI scan for every participant. The following data are released for every participant: * 6-minute resting state fMRI scan (R-fMRI) * MPRAGE anatomical scan, defaced to protect patient confidentiality * 64-direction diffusion tensor imaging scan (2mm isotropic) * Demographic information and information on the counterbalancing of eyes open versus eyes closed.

Proper citation: Beijing: Eyes Open Eyes Closed Study (RRID:SCR_001507) Copy   

•   Source: SciCrunch Registry

http://ahd.cbi.pku.edu.cn

Database providing a systematic and comprehensive view of morphological phenotypes regulated by plant hormones, as well as regulatory genes participating in numerous plant hormone responses. By integrating the data from mutant studies, transgenic analysis and gene ontology annotation, genes related to the stimulus of eight plant hormones were identified, including abscisic acid, auxin, brassinosteroid, cytokinin, ethylene, gibberellin, jasmonic acid and salicylic acid. Another pronounced characteristics of this database is that a phenotype ontology was developed to precisely describe all kinds of morphological processes regulated by plant hormones with standardized vocabularies. To increase the coverage of phytohormone related genes, the database has been updated from AHD to AHD2.0 adding and integrating several pronounced features: (1) added 291 newly published Arabidopsis hormone related genes as well as corrected information (e.g. the arguable ABA receptors) based on the recent 2-year literature; (2) integrated orthologues of sequenced plants in OrthoMCLDB into each gene in the database; (3) integrated predicted miRNA splicing site in each gene in the database; (4) provided genetic relationship of these phytohormone related genes mining from literature, which represents the first effort to construct a relatively comprehensive and complex network of hormone related genes as shown in the home page of our database; (5) In convenience to in-time bioinformatics analysis, they also provided links to a powerful online analysis platform Weblab that they have recently developed, which will allow users to readily perform various sequence analysis with these phytohormone related genes retrieved from AHD2.0; (6) provided links to other protein databases as well as more expression profiling information that would facilitate users for a more systematic analysis related to phytohormone research. Please help to improve the database with your contributions.

Proper citation: Arabidopsis Hormone Database (RRID:SCR_001792) Copy   

•   Source: SciCrunch Registry

http://www.megabionet.org/atpid/webfile/

Centralized platform to depict and integrate the information pertaining to protein-protein interaction networks, domain architecture, ortholog information and GO annotation in the Arabidopsis thaliana proteome. The Protein-protein interaction pairs are predicted by integrating several methods with the Naive Baysian Classifier. All other related information curated is manually extracted from published literature and other resources from some expert biologists. You are welcomed to upload your PPI or subcellular localization information or report data errors. Arabidopsis proteins is annotated with information (e.g. functional annotation, subcellular localization, tissue-specific expression, phosphorylation information, SNP phenotype and mutant phenotype, etc.) and interaction qualifications (e.g. transcriptional regulation, complex assembly, functional collaboration, etc.) via further literature text mining and integration of other resources. Meanwhile, the related information is vividly displayed to users through a comprehensive and newly developed display and analytical tools. The system allows the construction of tissue-specific interaction networks with display of canonical pathways.

Proper citation: Arabidopsis thaliana Protein Interactome Database (RRID:SCR_001896) Copy   

•   Source: SciCrunch Registry

http://spatialomics.org/SpatialDB/

Database for spatially resolved transcriptomes. Provides curated spatially resolved transcriptomic data from published papers, aiming to provide comprehensive and accurate resource of spatial gene expression profiles in tissues. Allows users to browse spatial gene expression profile and compare spatial gene expression profile of any two datasets generated by same or different techniques side by side.

Proper citation: Spatial DB (RRID:SCR_026135) Copy   

•   Source: SciCrunch Registry

http://herb.ac.cn/

High-throughput experiment- and reference-guided database of traditional Chinese medicine.

Proper citation: HERB (RRID:SCR_026468) Copy   

•   Source: SciCrunch Registry

http://www.zhounan.org/ferrdb/current/

Manually curated database of ferroptosis regulators and ferroptosis-disease associations. There are two secondary categories of ferroptosis regulators: (1) genes and (2) substances. Gene regulators include driver, suppressor, marker, and unclassified regulator. Substances cover range of chemical entities, including pure substances (e.g., iron, erastin) and mixtures (e.g., herbal extracts). Substance regulators include inducers and inhibitors. FerrDb V2 is updated database.

Proper citation: FerrDb (RRID:SCR_026852) Copy   

•   Source: SciCrunch Registry

http://bio-comp.org.cn/llpsdb/home.html

Database of proteins undergoing liquid–liquid phase separation in vitro. Contains LLPS related proteins together with the corresponding phase separation conditions validated by experiments.

Proper citation: LLPSDB (RRID:SCR_024966) Copy   

•   Source: SciCrunch Registry

http://www.rnaphasep.cn/#/Home

Database that collects phase separation related RNAs manually curated from publication and public databases.

Proper citation: RNAPhaSep (RRID:SCR_024958) Copy   

•   Source: SciCrunch Registry

http://ctrdb.ncpsb.org.cn/

Data resource for clinical transcriptomes with cancer treatment response, and meanwhile supports various data analysis functions, providing insights into the molecular determinants of drug resistance. CTR-DB 2.0 is updated cancer clinical transcriptome resource, expanding primary drug resistance and newly adding acquired resistance datasets and enhancing the discovery and validation of predictive biomarkers.

Proper citation: Cancer Treatment Response gene signature DataBase (RRID:SCR_027950) Copy   

•   Source: SciCrunch Registry

https://github.com/BGI-shenzhen/PopLDdecay

Software tool for linkage disequilibrium decay analysis based on variant call format files.

Proper citation: PopLDdecay (RRID:SCR_022509) Copy   

•   Source: SciCrunch Registry

http://lilab-ecust.cn/pharmmapper/index.html

Web server for potential drug target identification using pharmacophore mapping approach.Designed to identify potential target candidates for given probe small molecules including drugs, natural products, or other newly discovered compounds with binding targets unidentified using pharmacophore mapping approach. Used for potential drug target identification with comprehensive target pharmacophore database.

Proper citation: PharmMapper (RRID:SCR_022604) Copy   

•   Source: SciCrunch Registry

http://www.lirmed.com/tam2/

Web server for miRNA set enrichment analysis. TAM 2.0 is updated version of this web server. Allows to test functional and disease annotations of miRNAs by overrepresentation analysis and to compare input de-regulated miRNAs with those de-regulated in other disease conditions via correlation analysis.

Proper citation: TAM (RRID:SCR_023800) Copy   

•   Source: SciCrunch Registry

https://bioconductor.org/packages/miRBaseConverter/

Software R package for converting and retrieving information of miRNAs in different miRBase versions. Used for converting and retrieving miRNA Name, Accession, Sequence, Version, History and Family information in different miRBase versions. Can process huge number of miRNAs in short time without other depends.

Proper citation: miRBaseConverter (RRID:SCR_023873) Copy   

•   Source: SciCrunch Registry

https://github.com/basehc/IPEV

Software tool to identify of Prokaryotic and Eukaryotic virus derived sequences in virome using deep learning. Used to calculate set of scores that reflect probability that input sequence fragments are prokaryotic and eukaryotic viral sequences.

Proper citation: IPEV (RRID:SCR_023702) Copy   

•   Source: SciCrunch Registry