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https://plantcyc.org/databases/aracyc/15.0

Curated species-specific database present at the Plant Metabolic Network. It has a large number of experimentally supported enzymes and metabolic pathways, but it also houses a substantial number of computationally predicted enzymes and pathways.

Proper citation: AraCyc (RRID:SCR_008109) Copy   

•   Source: SciCrunch Registry

http://psychiatry.ucsd.edu/Neuroembryologylab/index.htm

Dr. Eric Turner''s laboratory studies the mechanisms underlying the development of the nervous system. The vertebrate brain is comprised of a tremendous variety of neurons, each class exhibiting a unique phenotype characterized by the expression of specific neurotransmitter receptors, ion channels, patterns of axonal growth, and synapse formation. The research we conduct focuses on the critical role transcription factors play in the specification of neuronal cell type during development. We are particularly interested in transcription factors of the homeodomain family that bind to DNA and in doing so activate or repress gene expression. One area of study is the role of POU-domain transciption factor Brn3a in axon growth and survival. The primary research areas are: * Neuronal cell fate determination: The expression of regulatory genes is manipulated in living chick embryos using microsurgery and electroporation and the effects on neural marker genes studied. * Molecular mechanisms of gene regulation: Target DNA binding sites of neural transcription factors are biochemically characterized and findings coordinated with sequence data from the mouse and human genomes. * Targeted misexpression of regulatory genes: Transgenic and knockout mouse technology is used to misexpress genes of interest, and the effects on neural marker genes, axonal growth, and cell survival studied. * Global analysis of neural gene expression: Micro-arrays (GeneChips) are employed in conjunction with other areas of study to understand the coordinated regulation of gene expression in the nervous system. Dr. Turner is a member of the University of California, San Diego''s Graduate Program in Neuroscience and Biomedical Sciences Program and accepts students from these two programs. Interesting rotation projects are available using methods ranging from biochemistry and molecular biology to embryology. Additionally, Dr. Turner is also the Director of this NIMH-funded training program for research-oriented psychiatrists, psychologists, and basic neuroscientists working in areas relevant to psychiatry. Typically Fellows spend two years in the program, during which they develop a research project under the close supervision of one of the highly productive members of the UCSD Department of Psychiatry, or another investigator in the La Jolla (UCSD/Salk/Scripps) research community.

Proper citation: Department of Psychiatry, Turner Laboratory (RRID:SCR_008067) Copy   

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http://genewindow.nci.nih.gov/

Software tool for pre- and post-genetic bioinformatics and analytical work, developed and used at the Core Genotyping Facility (CGF) at the National Cancer Institute. While Genewindow is implemented for the human genome and integrated with the CGF laboratory data, it stands as a useful tool to assist investigators in the selection of variants for study in vitro, or in novel genetic association studies. The Genewindow application and source code is publicly available for use in other genomes, and can be integrated with the analysis, storage, and archiving of data generated in any laboratory setting. This can assist laboratories in the choice and tracking of information related to genetic annotations, including variations and genomic positions. Features of GeneWindow include: -Intuitive representation of genomic variation using advanced web-based graphics (SVG) -Search by HUGO gene symbol, dbSNP ID, internal CGF polymorphism ID, or chromosome coordinates -Gene-centric display (only when a gene of interest is in view) oriented 5 to 3 regardless of the reference strand and adjacent genes -Two views, a Locus Overview, which varies in size depending on the gene or genomic region being viewed and, below it, a Sequence View displaying 2000 base pairs within the overview -Navigate the genome by clicking along the gene in the Locus Overview to change the Sequence View, expand or contract the genomic interval, or shift the view in the 5 or 3 direction (relative to the current gene) -Lists of available genomic features -Search for sequence matches in the Locus Overview -Genomic features are represented by shape, color and opacity with contextual information visible when the user moves over or clicks on a feature -Administrators can insert newly-discovered polymorphisms into the Genewindow database by entering annotations directly through the GUI -Integration with a Laboratory Information Management System (LIMS) or other databases is possible

Proper citation: GeneWindow (RRID:SCR_008183) Copy   

•   Source: SciCrunch Registry

http://www.osc.riken.jp/english/

Omics Science Center is aiming to develop a comprehensive system called Life Science Accelerator(LSA) for the advancement of omics research. The LSA is a comprehensive system consists of biological resources, human resources, technologies, know-how, and essential administrative ability. Ultimate goal of LSA is to support and accelerate the advancement in life science research. Omics is the comprehensive study of molecules in living organisms. The complete sequencing of genomes (the complete set of genes in an organism) has enabled rapid developments in the collection and analysis of various types of comprehensive molecular data such as transcriptomes (the complete set of gene expression data) and proteomes (the complete set of intracellular proteins). Fundamental omics research aims to link these omics data to molecular networks and pathways in order to advance the understanding of biological phenomena as systems at the molecular level.

Proper citation: RIKEN Omics Science Center (RRID:SCR_008241) Copy   

•   Source: SciCrunch Registry

http://www.repairgenes.org/index.shtml

The aim of the repairGenes site is to be a source of information about DNA repair genes and a useful resource for research on DNA repair. At the moment, the site contains information about a number of DNA repair genes from a set of selected species. The information is organized by organism and by biological process term as defined by the Gene Ontology (GO) project. The coverage of DNA repair genes is not complete, but hopefully it satisfies to demonstrate the concept and generate ideas for future versions of the system. At present, the raw data about DNA repair genes is extracted from the SWISS-PROT database, and categorized using the GO system. SWISS-PROT entries are being annotated by the Gene Ontology Annotation project at EBI. GOA is an ongoing project which will become more complete with time. As more data is released, this will be fed into repairGenes to keep it up-to-date. In future versions, the user will be able to search freely among organisms and categories of repair genes, enabling easy comparisons between species. For a taste of this, please have a look at the overview of repair genes from five major organisms. The amount of information in the system will be increased and the quality will be improved in the future. So will the features of the system.

Proper citation: repairGenes (RRID:SCR_008240) Copy   

•   Source: SciCrunch Registry

http://abgent.com

Antibody supplier.

Proper citation: Abgent (RRID:SCR_008393) Copy   

•   Source: SciCrunch Registry

http://www.nature.com/nature/supplements/collections/

This website provides summary collections written for a broad audience highlighting some of the significant advances in a particular field. These are not scientific articles although they may reference scientific work. Sponsors: This resource is supported by Nature.com

Proper citation: Nature Supplements: Collections archive (RRID:SCR_008337) Copy   

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http://bioinf.uni-greifswald.de/augustus/

Software for gene prediction in eukaryotic genomic sequences. Serves as a basis for further steps in the analysis of sequenced and assembled eukaryotic genomes.

Proper citation: Augustus (RRID:SCR_008417) Copy   

•   Source: SciCrunch Registry

https://www.stat.auckland.ac.nz/~paul/plaudits/Iobion.htm

GeneTraffic is a web-based microarray data analysis and management software developed by Iobion Informatics that allows users to log onto a server, upload their microarray data and perform analysis and project management remotely. GeneTraffic was made by Iobion Informatics (now under Stratagene) and can be accessed thorough Internet Explorer 6.0 or greater on Windows XP.

Proper citation: GeneTraffic (RRID:SCR_008651) Copy   

•   Source: SciCrunch Registry

http://crezoo.crt-dresden.de/crezoo/

Database of helpful set of CreERT2 driver lines expressing in various regions of the developing and adult zebrafish. The lines have been generated via the insertion of a mCherry-T2A-CreERT2 in a gene trap approach or by using promoter fragments driving CreERT2. You can search the list of all transgenic lines or single entries by insertions (gene) or expression patterns (anatomy/region). In most cases the CreERT2 expression profile using in situ hybridization at 24 hpf and 48 hpf is shown, but also additional information (e.g. mCherry or CreERT2 expression at adult stages, transactivation of a Cre-dependent reporter line) is displayed. Currently, not all insertions have been mapped to a genomic location but the database will be regularly updated adding newly generated insertions and mapping information. Your help in improving and broadening the database by giving your opinion or knowledge of expression patterns is highly appreciated.

Proper citation: CreZoo (RRID:SCR_008919) Copy   

•   Source: SciCrunch Registry

http://go.princeton.edu/cgi-bin/GOTermFinder

The Generic GO Term Finder finds the significant GO terms shared among a list of genes from an organism, displaying the results in a table and as a graph (showing the terms and their ancestry). The user may optionally provide background information or a custom gene association file or filter evidence codes. This tool is capable of batch processing multiple queries at once. GO::TermFinder comprises a set of object-oriented Perl modules GO::TermFinder can be used on any system on which Perl can be run, either as a command line application, in single or batch mode, or as a web-based CGI script. This implementation, developed at the Lewis-Sigler Institute at Princeton, depends on the GO-TermFinder software written by Gavin Sherlock and Shuai Weng at Stanford University and the GO:View module written by Shuai Weng. It is made publicly available through the GMOD project. The full source code and documentation for GO:TermFinder are freely available from http://search.cpan.org/dist/GO-TermFinder/. Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: Generic GO Term Finder (RRID:SCR_008870) Copy   

•   Source: SciCrunch Registry

http://apid.dep.usal.es

APID Interactomes (Agile Protein Interactomes DataServer) provides information on the protein interactomes of numerous organisms, based on the integration of known experimentally validated protein-protein physical interactions (PPIs). The interactome data includes a report on quality levels and coverage over the proteomes for each organism included. APID integrates PPIs from primary databases of molecular interactions (BIND, BioGRID, DIP, HPRD, IntAct, MINT) and also from experimentally resolved 3D structures (PDB) where more than two distinct proteins have been identified. This collection references protein interactors, through a UniProt identifier.

Proper citation: Agile Protein Interactomes DataServer (RRID:SCR_008871) Copy   

•   Source: SciCrunch Registry

http://plantgrn.noble.org/LegumeIP/

LegumeIP is an integrative database and bioinformatics platform for comparative genomics and transcriptomics to facilitate the study of gene function and genome evolution in legumes, and ultimately to generate molecular based breeding tools to improve quality of crop legumes. LegumeIP currently hosts large-scale genomics and transcriptomics data, including: * Genomic sequences of three model legumes, i.e. Medicago truncatula, Glycine max (soybean) and Lotus japonicus, including two reference plant species, Arabidopsis thaliana and Poplar trichocarpa, with the annotation based on UniProt TrEMBL, InterProScan, Gene Ontology and KEGG databases. LegumeIP covers a total 222,217 protein-coding gene sequences. * Large-scale gene expression data compiled from 104 array hybridizations from L. japonicas, 156 array hybridizations from M. truncatula gene atlas database, and 14 RNA-Seq-based gene expression profiles from G. max on different tissues including four common tissues: Nodule, Flower, Root and Leaf. * Systematic synteny analysis among M. truncatula, G. max, L. japonicus and A. thaliana. * Reconstruction of gene family and gene family-wide phylogenetic analysis across the five hosted species. LegumeIP features comprehensive search and visualization tools to enable the flexible query on gene annotation, gene family, synteny, relative abundance of gene expression.

Proper citation: LegumeIP (RRID:SCR_008906) Copy   

•   Source: SciCrunch Registry

http://meme.nbcr.net/meme/cgi-bin/gomo.cgi

Gene Ontology for Motifs (GOMO) is an alignment- and threshold-free comparative genomics approach for assigning functional roles to DNA regulatory motifs from DNA sequence. The algorithm detects associations between a user-specified DNA regulatory motif (expressed as a position weight matrix; PWM) and Gene Ontology terms. The original method for predicting the roles of transcription factors (TFs starts with a PWM motif describing the DNA-binding affinity of the TF. GOMO uses the PWM to score the promoter region of each gene in the genome for its likelihood to be bound by the TF. The resulting ''''affinity'''' scores are then used to test each term in the Gene Ontology for association with high-scoring genes. The algorithm was subsequently extended to leverage conserved signals using multiple, related species in a comparative approach, which greatly improves the resulting annotations. Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: GOMO - Gene Ontology for Motifs (RRID:SCR_008864) Copy   

•   Source: SciCrunch Registry

http://vortex.cs.wayne.edu/projects.htm#OE2GO

Onto-Express is a web-based tool in the Onto-Tools suite that performs automated function profiling for a list of differentially expressed genes. However, Onto-Express does not support functional profiling for the organisms that do not have annotations in public domain, or use of custom (i.e. user-defined) ontologies. This limitation is also true for most of the other existing tools for functional profiling, which means that researchers working with uncommon organisms and/or new annotations or ontologies may be forced to construct such profiles manually. Onto-Express To Go (OE2GO) is a new tool added to the Onto-Tools ensemble to address these issues. OE2GO is built on top of OE to leverage its existing functionality. In OE2GO, the users now have an option to use either the Onto-Tools database as a source of functional annotations or provide their own annotations in a separate file. Currently, OE2GO supports annotation file in the Gene Ontology format. Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: Onto-Express To Go (OE2GO) (RRID:SCR_008854) Copy   

•   Source: SciCrunch Registry

http://promoter.bx.psu.edu/hi-c/

Genome Browser for study of 3D genome organization and gene regulation and data visualization. Used to visualizing chromatin interaction data, browse other omics data such as ChIP-Seq or RNA-Seq for same genomic region, and gain complete view of both regulatory landscape and 3D genome structure for any given gene., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: 3D Genome (RRID:SCR_017525) Copy   

•   Source: SciCrunch Registry

https://github.com/tanghaibao/mcscan

Software package to simultaneously scan multiple genomes to identify homologous chromosomal regions and subsequently align these regions using genes as anchors.Used to identify conserved gene arrays both within same genome and across different genomes. Command line program to wrap dagchainer and combine pairwise results into multi alignments in column format.

Proper citation: MCScan (RRID:SCR_017650) Copy   

•   Source: SciCrunch Registry

https://github.com/gdancik/shinyGEO

Web based tool to download gene expression datasets from GEO in order to perform differential expression and survival analysis for gene of interest. Produces publication ready graphics and generates R code ensuring that all analyses are reproducible. Web based application for analyzing gene expression omnibus datasets.

Proper citation: shinyGEO (RRID:SCR_017605) Copy   

•   Source: SciCrunch Registry

https://scdevdb.deepomics.org/

Database for insights into single cell gene expression profiles during human developmental processes. Interactive database provides DE gene lists in each developmental pathway, t-SNE map, and GO and KEGG enrichment analysis based on these differential genes.

Proper citation: Single Cell Developmental Database (RRID:SCR_017546) Copy   

•   Source: SciCrunch Registry

https://github.com/LaBiOS/Transcriptive

Software tool as bioinformatics analysis pipeline used for RNA sequencing data. Workflow processes raw data from FastQ inputs, aligns reads, generates gene and transcript counts, and performs quality control on results.

Proper citation: Transcriptive (RRID:SCR_017545) Copy   

•   Source: SciCrunch Registry