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https://github.com/hakyimlab/PrediXcan

Software tool to detect known and novel genes associated with disease traits and provide insights into the mechanism of these associations. Used to test the molecular mechanisms through which genetic variation affects phenotype.

Proper citation: PrediXcan (RRID:SCR_016739) Copy   

•   Source: SciCrunch Registry

http://metascape.org/gp/index.html#/main/step1

Web service to analyze gene or protein lists. Provides automated meta analysis tools to understand pathways within a group of orthogonal target-discovery studies.

Proper citation: Metascape (RRID:SCR_016620) Copy   

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

Software package for low-level analyses of single-cell RNA-seq data. Used for quality control, data exploration and normalization, cell cycle phase assignment, identification of highly variable and correlated genes, clustering into subpopulations and marker gene detection.

Proper citation: scran (RRID:SCR_016944) Copy   

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http://www.ncbi.nlm.nih.gov/genbank/tpa/

Database designed to capture experimental or inferential results that support submitter-provided annotation for sequence data that the submitter did not directly determine but derived from GenBank primary data. Records are divided into two categories: * TPA:experimental: Annotation of sequence data is supported by peer-reviewed wet-lab experimental evidence. * TPA:inferential: Annotation of sequence data by inference (where the source molecule or its product(s) have not been the subject of direct experimentation) TPA records are retrieved through the Nucleotide Database and feature information on the sequence, how it was cataloged, and proper way to cite the sequence information.

Proper citation: TPA (RRID:SCR_003593) Copy   

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https://www.hsph.harvard.edu/alkes-price/software/

Software application that uses genotyping data from SNP arrays for accurately inferring chromosomal segments of distinct continental ancestry in admixed populations, using dense genetic data. (entry from Genetic Analysis Software)

Proper citation: Hapmix (RRID:SCR_004203) Copy   

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http://yasuimac.bio.brandeis.edu/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 6, 2023. Database and atlas of transgenic mouse lines that are generated by random insertion of enhancer trap probes. The trapped lines have highly restricted expression of tet transcription activator (some lines also have Cre DNA recombinase), which enables genetic manipulations in specific cell types.

Proper citation: Enhancer Trap Line Browser (RRID:SCR_003592) Copy   

•   Source: SciCrunch Registry

http://www.ncbi.nlm.nih.gov/structure

Database of three-dimensional structures of macromolecules that allows the user to retrieve structures for specific molecule types as well as structures for genes and proteins of interest. Three main databases comprise Structure-The Molecular Modeling Database; Conserved Domains and Protein Classification; and the BioSystems Database. Structure also links to the PubChem databases to connect biological activity data to the macromolecular structures. Users can locate structural templates for proteins and interactively view structures and sequence data to closely examine sequence-structure relationships. * Macromolecular structures: The three-dimensional structures of biomolecules provide a wealth of information on their biological function and evolutionary relationships. The Molecular Modeling Database (MMDB), as part of the Entrez system, facilitates access to structure data by connecting them with associated literature, protein and nucleic acid sequences, chemicals, biomolecular interactions, and more. It is possible, for example, to find 3D structures for homologs of a protein of interest by following the Related Structure link in an Entrez Protein sequence record. * Conserved domains and protein classification: Conserved domains are functional units within a protein that act as building blocks in molecular evolution and recombine in various arrangements to make proteins with different functions. The Conserved Domain Database (CDD) brings together several collections of multiple sequence alignments representing conserved domains, in addition to NCBI-curated domains that use 3D-structure information explicitly to define domain boundaries and provide insights into sequence/structure/function relationships. * Small molecules and their biological activity: The PubChem project provides information on the biological activities of small molecules and is a component of NIH''''s Molecular Libraries Roadmap Initiative. PubChem includes three databases: PCSubstance, PCBioAssay, and PCCompound. The PubChem data are linked to other data types (illustrated example) in the Entrez system, making it possible, for example, to retrieve information about a compound and then Link to its biological activity data, retrieve 3D protein structures bound to the compound and interactively view their active sites, and find biosystems that include the compound as a component. * Biological Systems: A biosystem, or biological system, is a group of molecules that interact directly or indirectly, where the grouping is relevant to the characterization of living matter. The NCBI BioSystems Database provides centralized access to biological pathways from several source databases and connects the biosystem records with associated literature, molecular, and chemical data throughout the Entrez system. BioSystem records list and categorize components (illustrated example), such as the genes, proteins, and small molecules involved in a biological system. The companion FLink icon FLink tool, in turn, allows you to input a list of proteins, genes, or small molecules and retrieve a ranked list of biosystems.

Proper citation: NCBI Structure (RRID:SCR_004218) Copy   

•   Source: SciCrunch Registry

http://www.mknt.hu/sites/default/files/NEPSYBANK_0.doc

The Hungarian Society of Clinical Neurgenetics established a nationwide collaboration for prospective collection of human biological materials and databases from patient with neurological and psychiatric diseases. The basic triangle of the NEPSYBANK is the sample, the information and the study management. The present participants of the NEPSYBANK are the Department of Neurology and Psychiatry of the four Medical Universities (in Budapest, Debrecen, Pecs, Szeged) and the National Institute of Psychiatry and Neurology in Budapest. The NEPSYBANK is a disease based biobank collecting both phenotypical and environmental data and biological materials such as DNA/RNA, whole blood, plasma, cerebral spinal fluid, muscle / nerve / skin biopsy, brain, and fibroblast. The target of the diseases is presently (Phase I): stroke syndromes, dementias, movement disorders, motoneuron diseases, epilepsy, multiple sclerosis, schizophrenia, alcohol addiction. In the near future (Phase II.) it is planned to enlarge the scale with headaches, disorders of the peripheral nerves, disorders of neuromuscular transmission, disorders of skeletal muscle, depression, anxiety. DNA/RNA is usually extracted from whole blood, but occasionally different tissues such as muscle, brain etc. can be used as well. The extracting procedures differ among the institutes, but in all cases the concentration and the quality of the DNA/RNA must be registered in the database. Participating institutional biobanks have committed themselves to follow common quality standards, which provide access to samples after prioritization on scientific grounds only. In every case the following data are registered. 1. General data: main bank categories, age, sex, ethnicity, body height, body weight, economic stats, education, type of place of living, marital status, birth complications, alcohol, drugs, smoking. 2. Sample properties (sample ID, type of sample, date of extraction, concentration, and level of purity). General patient data as blood pressure, heart rate, internal medical status, ECG, additional diseases. Disease specific question e.g. in schizophrenia the diagnosis after DSMIV and ICD 10, detailed diagnostic questions after both classification, detailed psychiatric and neurological status, laboratory findings, rating scales, data of neuroimaging, genetic tests, applied medication (with generic name, dose, duration), adverse drug effects and other treatments. The Biobank Information Management System (BIMS) is responsible for linkage of databases containing information on the individual sample donors. If you want to have samples from the NEPSYBANK an application must be submitted containing the following information: short research plan including aims and study design, ethic application with a positive decision, specific demands regarding the right of disposition, agreements with grant organizations which regulate immaterial property, information about financing (academic grants, support from industry). All participants have the right to withdraw their samples through a simple order.

Proper citation: Hungarian Neurological-Psychiatric Biobank (RRID:SCR_003715) Copy   

•   Source: SciCrunch Registry

http://sevens.cbrc.jp/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 11, 2023. SEVENS summarizes GPCR (G-protein coupled receptor) genes that are identified with high accuracy from 43 eukaryote genomes, by a pipeline integrating such software as a gene finder, a sequence alignment tool, a motif and domain assignment tool, and a transmembrane helix predictor. This treats a larger data space (than that in currently available other databases), which should include not only the expressed sequences but also the newly identified sequences that cannot be detected by in vivo experiments, although they definitely exist on the genome sequence and are just waiting for the opportunity to express their functions. SEVENS provides the infrastructure of general information of GPCR universe for comparative genomics. We developed an automatic system for identifying GPCR (G-protein coupled receptor) genes from various kinds of genomes, by integrating such software as a gene finder, a sequence alignment tool, a motif and domain assignment tool, and a transmembrane helix predictor. SEVENS enables us to perform a genome-scale overview of the GPCR universe using sequences that are identified with high accuracy (99.4% sensitivity and 96.6% specificity). Using this system, we surveyed the complete genomes of 7 eukaryotes and 224 prokaryotes, and found that there are 4 to 1016 GPCR genes in the 7 eukaryotes, and only a total of 16 GPCR genes in all the prokaryotes. Our preliminary results indicate that 11 subfamilies of the Class A family, the Class 2(B) family, the Class 3(C) family and the fz/smo family are commonly found among human, fly, and nematode genomes. We also analyzed the chromosomal locations of the GPCR genes with the Kolmogorov-Smirnov test, and found that species-specific families, such as olfactory, taste, and chemokine receptors in human and nematode chemoreceptor in worm, tend to form clusters extensively, whereas no significant clusters were detected in fly and plant genomes. How we found GPCR sequences: Candidate GPCR genes were collected from 32 eukaryote genomes by using the GPCR gene discovery pipeline, composed of two stages: (1) the gene finding stage, and (2) the GPCR gene screening stage. 1)Gene finding stage (i.e., translation of genomic sequences into amino acid sequences). 2)GPCR gene screening stage of GPCR candidates by assessing genes with sequence search, motif- and domain assignment, and transmembrane helix (TMH) prediction. Details available at the website. Acknowledgment: We are pleased to acknowledge the use of the BLAST package from NCBI, the SOSUI from Dr. T. Hirokawa, the ALN from Dr. O. Gotoh, the HMMER from Dr. A. Bateman. This work was supported by KAKENHI (208059) (Grant-in-Aid for Publication of Scientific Research Results) of Japan Society for the Promotion of Science (JSPS).

Proper citation: SEVENS (RRID:SCR_004688) Copy   

•   Source: SciCrunch Registry

http://tools.niehs.nih.gov/polg/

Database that lists all known mutations in the coding region of the POLG gene and describes the associated disease. Human DNA polymerase is composed of two subunits, a 140 kDa catalytic subunit encoded by the POLG on chromosome 15q25, and a 55kDa accessory subunit encoded by the POLG2 gene on chromosome 17q23-24. A number of mutations have been mapped to the gene for the catalytic subunit of DNA polymerase, POLG, and found to be associated with mitochondrial diseases. The nucleotide changes are numbered from the initiation Methionine codon and are based on the cDNA (accession U60325.1) and gene sequence (accession AF497906.1).

Proper citation: Human DNA Polymerase Gamma Mutation Database (RRID:SCR_004722) Copy   

•   Source: SciCrunch Registry

http://www.jbldesign.com/jmogil/enter.html

Database of genes regulated by pain derived from published manuscripts describing results of pain-relevant knockout studies. The database has two levels of exploration: across-gene and within-gene. The across-gene level, the PainGenesdbSelector, is encountered first. All genes in the database can be accessed and sorted by their gene name, protein name, common names and acronyms, or genomic position (by navigating a graphic representation of the mouse genome). The gene and protein names can be selected from an alphabetical list, or by typing a text string into a search box.

Proper citation: Pain Genes database (RRID:SCR_004771) Copy   

•   Source: SciCrunch Registry

http://www.ncbi.nlm.nih.gov/unigene

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 11, 2023. Web tool for an organized view of the transcriptome. Collection of the computationally identified transcripts from the same locus. Information on protein similarities, gene expression, cDNA clones, and genomic location. System for automatically partitioning GenBank sequences into a non redundant set of gene oriented clusters.

Proper citation: UniGene (RRID:SCR_004405) Copy   

•   Source: SciCrunch Registry

http://www.ihop-net.org/UniPub/iHOP/

Information system that provides a network of concurring genes and proteins extends through the scientific literature touching on phenotypes, pathologies and gene function. It provides this network as a natural way of accessing millions of PubMed abstracts. By using genes and proteins as hyperlinks between sentences and abstracts, the information in PubMed can be converted into one navigable resource, bringing all advantages of the internet to scientific literature research. Moreover, this literature network can be superimposed on experimental interaction data (e.g., yeast-two hybrid data from Drosophila melanogaster and Caenorhabditis elegans) to make possible a simultaneous analysis of new and existing knowledge. The network contains half a million sentences and 30,000 different genes from humans, mice, D. melanogaster, C. elegans, zebrafish, Arabidopsis thaliana, yeast and Escherichia coli.

Proper citation: Information Hyperlinked Over Proteins (RRID:SCR_004829) Copy   

•   Source: SciCrunch Registry

http://mousemap.princeton.edu/

In collaboration with several research groups at Jackson Laboratory, we mapped the tissue-specific functional relationship networks in the laboratory mouse by simulating the natural tissue specificity realized by differential protein expression between tissues. MouseMAP is an interface that allows browsing networks of different tissues and the comparison between them. The current version of MouseMAP includes one global network as an extension of mouseNET (http://mousenet.princeton.edu) and 107 tissue-specific networks organized into 15 different major body systems. The interface has the following features (their usage are detailed in Help and FAQ page): 1. Single graph query with one or multiple genes of interests. 2. Expansion/shrinking of query results through edge or node cutoffs. 3. Comparison of local networks between different tissues. 4. Dynamic generation of figure legend to facilitate manuscript writing.

Proper citation: mouseMAP (RRID:SCR_004784) Copy   

•   Source: SciCrunch Registry

http://www.snp-nexus.org/

A web server for functional annotation of novel and publicly known genetic variants that was developed to assess the potential significance of known and novel SNPs on the major transcriptome, proteome, regulatory and structural variation models in order to identify the phenotypically important variants. A broader range of variations have been incorporated such as insertions / deletions, block substitutions, IUPAC codes submission and region-based analysis, expanding the query size limit, and most importantly including additional categories for the assessment of functional impact. SNPnexus provides a comprehensive set of annotations for genomic variation data by characterizing related functional consequences at the transcriptome/proteome levels of seven major annotation systems with in-depth analysis of potential deleterious effects, inferring physical and cytogenetic mapping, reporting information on HapMap genotype/allele data, finding overlaps with potential regulatory elements, structural variations and conserved elements, and retrieving links with previously reported genetic disease studies.

Proper citation: SNPnexus (RRID:SCR_005192) Copy   

•   Source: SciCrunch Registry

http://spot.cgsmd.isi.edu

A web-based tool for using biological databases to prioritize single nucleotide polymorphisms (SNPs) after a genome-wide association study (GWAS). The site allows users to upload a list of SNPs and GWAS P-values and returns a prioritized list of SNPs using the GIN method. Users can specify candidate genes or genomic regions with custom levels of prioritization. The results can be downloaded or viewed in the browser where users can interactively explore the details of each SNP, including graphical representations of the genomic information network (GIN) method. For investigators interested in incorporating biological databases into a post-GWAS SNP selection strategy, the SPOT web tool is an easily implemented and flexible solution.

Proper citation: SPOT - Biological prioritization after a SNP association study (RRID:SCR_005193) Copy   

•   Source: SciCrunch Registry

http://www.scandb.org/newinterface/about.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on March 17, 2022. A large-scale database of genetics and genomics data associated to a web-interface and a set of methods and algorithms that can be used for mining the data in it. The database contains two categories of single nucleotide polymorphism (SNP) annotations: # Physical-based annotation where SNPs are categorized according to their position relative to genes (intronic, inter-genic, etc.) and according to linkage disequilibrium (LD) patterns (an inter-genic SNP can be annotated to a gene if it is in LD with variation in the gene). # Functional annotation where SNPs are classified according to their effects on expression levels, i.e. whether they are expression quantitative trait loci (eQTLs) for that gene. SCAN can be utilized in several ways including: (i) queries of the SNP and gene databases; (ii) analysis using the attached tools and algorithms; (iii) downloading files with SNP annotation for various GWA platforms. . eQTL files and reported GWAS from NHGRI may be downloaded., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: SCAN (RRID:SCR_005185) Copy   

•   Source: SciCrunch Registry

http://www.gomapman.org/

An open web-accessible resource for gene functional annotations in the plant sciences to facilitate improvement, consolidation and visualization of gene annotations across several plant species. It is based on the MapMan ontology, organized in the form of a hierarchical tree of biological concepts, which describe gene functions. Currently, genes of the model species Arabidopsis, potato, tomato, rice, and tobacco are included. The main features are (i) dynamic and interactive gene product annotation through various curation options; (ii) consolidation of gene annotations for different plant species through the integration of orthologue group information; (iii) traceability of gene ontology changes and annotations; (iv) integration of external knowledge about genes from different public resources; and (v) providing gathered information to high-throughput analysis tools via dynamically generated export files. All of the GoMapMan functionalities are openly available, with the restriction on the curation functions, which require prior registration to ensure traceability of the implemented changes.

Proper citation: GoMapMan (RRID:SCR_005060) Copy   

•   Source: SciCrunch Registry

http://deepbase.sysu.edu.cn/chipbase/

A database for decoding transcription factor binding maps, expression profiles and transcriptional regulation of long non-coding RNAs (lncRNAs, lincRNAs), microRNAs, other ncRNAs (snoRNAs, tRNAs, snRNAs, etc.) and protein-coding genes from ChIP-Seq data. ChIPBase currently includes millions of transcription factor binding sites (TFBSs) among 6 species. ChIPBase provides several web-based tools and browsers to explore TF-lncRNA, TF-miRNA, TF-mRNA, TF-ncRNA and TF-miRNA-mRNA regulatory networks., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: ChIPBase (RRID:SCR_005404) Copy   

•   Source: SciCrunch Registry

http://rulai.cshl.edu/tred

Collects mammalian cis- and trans-regulatory elements together with experimental evidence. Regulatory elements were mapped on to assembled genomes. Resource for gene regulation and function studies. Users can retrieve primers, search TF target genes, retrieve TF motifs, search Gene Regulatory Networks and orthologs, and make use of sequence analysis tools. Uses databases such as Genbank, EPD and DBTSS, and employ promoter finding program FirstEF combined with mRNA/EST information and cross-species comparisons. Manually curated.

Proper citation: Transcriptional Regulatory Element Database (RRID:SCR_005661) Copy   

•   Source: SciCrunch Registry