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http://cmr.jcvi.org/cgi-bin/CMR/shared/GenomePropertiesHomePage.cgi

The Genome Properties system consists of a suite of Properties which are carefully defined attributes of prokaryotic organisms whose status can be described by numerical values or controlled vocabulary terms for individual completely sequenced genomes. The system has been designed to capture the widest possible range of attributes and currently encompasses taxonomic terms, genometric calculations, metabolic pathways, systems of interacting macromolecular components and quantitative and descriptive experimental observations (phenotypes) from the literature. You may search the Genome Properties Database in 1 of 3 ways: * Search For Predicted Properties in the CMR: The Genome Property Search allows you to search the Genome Property database for state information for selected genomes and properties. * Perform a Keyword Search for a Specific Property: Lists all Genome Properties that match a specific text string. You can choose to search All Fields within a genome property or the Property Name. * Browse Top Level Genome Properties: Click on the properties to see the specific genome property report page. The Genome Properties system presents key aspects of prokaryotic biology using standardized computational methods and controlled vocabularies. Properties reflect gene content, phenotype, phylogeny and computational analyses. The results of searches using hidden Markov models allow many properties to be deduced automatically, especially for families of proteins (equivalogs) conserved in function since their last common ancestor. Additional properties are derived from curation, published reports and other forms of evidence. Genome Properties system was applied to 156 complete prokaryotic genomes, and is easily mined to find differences between species, correlations between metabolic features and families of uncharacterized proteins, or relationships among properties.

Proper citation: JCVI GenProp (RRID:SCR_004592) Copy   

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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   

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http://nbc.ece.drexel.edu/

Webserver for taxonomic classification of metagenomic reads.

Proper citation: NBC (RRID:SCR_004772) Copy   

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http://avia.abcc.ncifcrf.gov/apps/site/index

An interactive web-based tool to explore and interpret large sets of genomic variations (single nucleotide variations and insertion/deletions) to help guide and summarize genomic experiments. The tool is based on coupling a comprehensive annotation pipeline with a flexible visualization method. They leveraged the ANNOVAR (Wang et. al, 2010) framework for assigning functional impact to genomic variations by extending its list of reference annotation databases (RefSeq, UCSC, SIFT, Polyphen etc.) with additional in-house developed sources (Non-B DB, PolyBrowse). Further, because many users also have their own annotation sources, they have added the ability to supply their own files as well. The results can be obtained in tabular format or as tracks in whole genome circular views generated by the Circos application (Krzywinski et. al, 2009). Users can also select different sets of pre-computed tracks, including whole genome distributions of different genomic features (genes, exons, repeats), as well as variations analysis tracks for the 69 CGI public genomes for reference.

Proper citation: AVIA (RRID:SCR_005172) Copy   

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http://www.broadinstitute.org/cancer/cga/oncotator

A tool for annotating human genomic point mutations and indels with data relevant to cancer researchers. Genomic Annotations, Protein Annotations, and Cancer Annotations are aggregated from many resources. A standalone version of Oncotator is being developed.

Proper citation: Oncotator (RRID:SCR_005183) Copy   

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

Database of biological data related to a single initiative, originating from a single organization or from a consortium. A BioProject record provides users a single place to find links to the diverse data types generated for that project. It is a searchable collection of complete and incomplete (in-progress) large-scale sequencing, assembly, annotation, and mapping projects for cellular organisms. Submissions are supported by a web-based Submission Portal. The database facilitates organization and classification of project data submitted to NCBI, EBI and DDBJ databases that captures descriptive information about research projects that result in high volume submissions to archival databases, ties together related data across multiple archives and serves as a central portal by which to inform users of data availability. BioProject records link to corresponding data stored in archival repositories. The BioProject resource is a redesigned, expanded, replacement of the NCBI Genome Project resource. The redesign adds tracking of several data elements including more precise information about a project''''s scope, material, and objectives. Genome Project identifiers are retained in the BioProject as the ID value for a record, and an Accession number has been added. Database content is exchanged with other members of the International Nucleotide Sequence Database Collaboration (INSDC). BioProject is accessible via FTP.

Proper citation: NCBI BioProject (RRID:SCR_004801) Copy   

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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   

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http://genome.jgi.doe.gov/programs/plants/index.jsf

The goal of the DOE JGI Plant Genome Program is to shed light on the fundamental biology of photosynthesis and transduction of solar to chemical energy. Other areas of interest include characterizing: * Ecosystems and the role of terrestrial plants and oceanic phytoplankton-in carbon sequestration. * The role of plants in coping with toxic pollutants in soils by hyper-accumulation and detoxification. * Feedstocks for biofuels, e.g., biodiesel from soybean; cellulosic ethanol from perennial grasses. * The ability to respond to environmental change (e.g., loss of diversity from monoculture produces vulnerabilities; nitrogen fixing nodules in legumes reduce fertilizer need). * The generation of useful secondary metabolites (produced largely for disease resistance)- for positive/negative control in agriculture, with attendant influence on global carbon cycle. The Plant Genome Program accomplishes the above through the following activities: # Sequence. Produce genome sequences of key plant (and algal) species to accelerate biofuel development and understand response to climate change. # Function. Develop datasets (and synthetic biology tools) to elucidate functional elements in plant genomes, with special focus on handful of flagship genomes. # Variation. Characterize natural genomic variation in plants (and their associated microbiomes), and relate to biofuel sustainability and adaptation to climate change. # Integration. Provide a centralized hub for the retrieval and deep integrated analysis of plant genome datasets.

Proper citation: Plant Genome Resource at JGI (RRID:SCR_005315) Copy   

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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   

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http://www.tractor.lncc.br

Database of computationally predicted Transcription Factors and binding sites in gamma-proteobacterial genomes. The user may browse a map containing all known E. coli transcription factors and regulatory interactions that connect them, and retrieve information on the conservation of each regulatory interaction across the 30 organisms included in the database. Downloading the information is straightforward, and navigation tabs added to dynamic pages ease navigation between the five interfaces of the database. The original prediction approach, based on the representation of binding sites through statistical models was complemented by a new approach that uses known E. coli regulatory sites as the basis for a pattern matching search of regulatory sites. The use of both approaches together resulted in a more intensive exploration of the sequence space of each regulator's binding site. These data should aid researchers in the design of microarray experiments and the interpretation of their results. They should also facilitate studies of Comparative Genomics of the regulatory networks of this group of organisms.

Proper citation: Tractor db (RRID:SCR_005610) Copy   

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http://www.youtube.com/ncbinlm

Videos from the National Center for Biotechnology Information including presentations and tutorials about NCBI biomolecular and biomedical literature databases and tools.

Proper citation: NCBI YouTube Channel (RRID:SCR_006084) Copy   

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http://wego.genomics.org.cn/cgi-bin/wego/index.pl

Web Gene Ontology Annotation Plot (WEGO) is a simple but useful tool for plotting Gene Ontology (GO) annotation results. Different from other commercial software for chart creating, WEGO is designed to deal with the directed acyclic graph (DAG) structure of GO to facilitate histogram creation of GO annotation results. WEGO has been widely used in many important biological research projects, such as the rice genome project and the silkworm genome project. It has become one of the useful tools for downstream gene annotation analysis, especially when performing comparative genomics tasks. Platform: Online tool

Proper citation: WEGO - Web Gene Ontology Annotation Plot (RRID:SCR_005827) Copy   

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http://recountdb.cbrc.jp/

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 22, 2016. Database for corrected read counts and genome mapping on NCBI's Short Read Archive. The corrected count was done using RECOUNT and the mapping with LAST. We also provide information of reference genome to which we aligned the short reads. We focus on transcriptomic data, specifically TSS-Seq and RNA-Seq. Because this is the type of data for which sequence count correction is most important. Hence we do not include the genomic reads. The current version contains 2,265 entries from 45 organisms, with read lengths from 17 to 100bp. Via a searchable and browseable interface users can obtain corrected data in formats useful for transcriptomic analysis. We provide the data grouped according to the genome, type of studies and submitter in TAB , PSL and BAM format. They contain the mapping position and annotation of reads observed and corrected counts.

Proper citation: RecountDB (RRID:SCR_006117) Copy   

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http://operons.ibt.unam.mx/OperonPredictor/

The Prokaryotic Operon DataBase (ProOpDB) constitutes one of the most precise and complete repository of operon predictions in our days. Using our novel and highly accurate operon algorithm, we have predicted the operon structures of more than 1,200 prokaryotic genomes. ProOpDB offers diverse alternatives by which a set of operon predictions can be retrieved including: i) organism name, ii) metabolic pathways, as defined by the KEGG database, iii) gene orthology, as defined by the COG database, iv) conserved protein motifs, as defined by the Pfam database, v) reference gene, vi) reference operon, among others. In order to limit the operon output to non-redundant organisms, ProOpDB offers an efficient protocol to select the more representative organisms based on a precompiled phylogenetic distances matrix. In addition, the ProOpDB operon predictions are used directly as the input data of our Gene Context Tool (GeConT) to visualize their genomic context and retrieve the sequence of their corresponding 5�� regulatory regions, as well as the nucleotide or amino acid sequences of their genes. The prediction algorithm The algorithm is a multilayer perceptron neural network (MLP) classifier, that used as input the intergenic distances of contiguous genes and the functional relationship scores of the STRING database between the different groups of orthologous proteins, as defined in the COG database. Nevertheless, the operon prediction of our method is not restricted to only those genes with a COG assignation, since we successfully defined new groups of orthologous genes and obtained, by extrapolation, a set of equivalent STRING-like scores based on conserved gene pairs on different genomes. Since the STRING functional relationships scores are determined in an un-bias manner and efficiently integrates a large amount of information coming from different sources and kind of evidences, the prediction made by our MLP are considerably less influenced by the bias imposed in the training procedure using one specific organism.

Proper citation: ProOpDB (RRID:SCR_006111) Copy   

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http://tbdb.org

Database providing integrated access to genome sequence, expression data and literature curation for Tuberculosis (TB) that houses genome assemblies for numerous strains of Mycobacterium tuberculosis (MTB) as well assemblies for over 20 strains related to MTB and useful for comparative analysis. TBDB stores pre- and post-publication gene-expression data from M. tuberculosis and its close relatives, including over 3000 MTB microarrays, 95 RT-PCR datasets, 2700 microarrays for human and mouse TB related experiments, and 260 arrays for Streptomyces coelicolor. (July 2010) To enable wide use of these data, TBDB provides a suite of tools for searching, browsing, analyzing, and downloading the data.

Proper citation: Tuberculosis Database (RRID:SCR_006619) Copy   

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http://iae.fafu.edu.cn/DBM/

Database storing and integrating genomic data of diamondback moth (DBM), Plutella xylostella (L.). It provides comprehensive search tools and downloadable datasets for scientists to study comparative genomics, biological interpretation and gene annotation of this insect pest. DBM-DB contains assembled transcriptome datasets from multiple DBM strains and developmental stages, and the annotated genome of P. xylostella (version 2). They have also integrated publically available ESTs from NCBI and a putative gene set from a second DBM genome (KONAGbase) to enable users to compare different gene models. DBM-DB was developed with the capacity to incorporate future data resources, and will serve as a long-term and open-access database that can be conveniently used for research on the biology, distribution and evolution of DBM. This resource aims to help reduce the impact DBM has on agriculture using genomic and molecular tools.

Proper citation: DBM-DB (RRID:SCR_006258) Copy   

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http://viralzone.expasy.org/

ViralZone is a SIB Swiss Institute of Bioinformatics web-resource for all viral genus and families, providing general molecular and epidemiological information, along with virion and genome figures. Each virus or family page gives an easy access to UniProtKB/Swiss-Prot viral protein entries. ViralZone project is handled by the virus program of SwissProt group. Proteins popups were developed in collaboration with Prof. Christian von Mering and Andrea Franceschini, Bioinformatics Group , Institute of Molecular Life Sciences, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland, funded in part by the SIB Swiss Institute of bioinformatics. All pictures in ViralZone are copyright of the SIB Swiss Institute of Bioinformatics.

Proper citation: ViralZone (RRID:SCR_006563) Copy   

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http://decipher.sanger.ac.uk/

Interactive database which incorporates a suite of tools designed to aid the interpretation of submicroscopic chromosomal imbalance. Used to enhance clinical diagnosis by retrieving information from bioinformatics resources relevant to the imbalance found in the patient. Contributing to the DECIPHER database is a Consortium, comprising an international community of academic departments of clinical genetics. Each center maintains control of its own patient data (which are password protected within the center''''s own DECIPHER project) until patient consent is given to allow anonymous genomic and phenotypic data to become freely viewable within Ensembl and other genome browsers. Once data are shared, consortium members are able to gain access to the patient report and contact each other to discuss patients of mutual interest, thus facilitating the delineation of new microdeletion and microduplication syndromes.

Proper citation: DECIPHER (RRID:SCR_006552) Copy   

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http://wishart.biology.ualberta.ca/BacMap

An interactive visual database containing hundreds of fully labeled, zoomable, and searchable maps of bacterial genomes. It uses a visualization tool (CGView) to generate high-resolution circular genome maps from sequence feature information. Each map includes an interface that allows the image to be expanded and rotated. In the default view, identified genes are drawn to scale and colored according to coding directions. When a region of interest is expanded, gene labels are displayed. Each label is hyperlinked to a custom ''gene card'' which provides several fields of information concerning the corresponding DNA and protein sequences. Each genome map is searchable via a local BLAST search and a gene name/synonym search. A complete listing of the species and strains in the BacMap database is available on the BacMap homepage. Below each species/strain name is a list of the sequenced chromosomes and plasmids that are available. Some features of BacMap include: * Maps are available for 2023 bacterial chromosomes. * Each map supports zooming and rotation. * Map gene labels are hyperlinked to detailed textual annotations. * Maps can be explored manually, or with the help of BacMap''s built in text search and BLAST search. * A written synopsis of each bacterial species is provided. * Several charts illustrating the proteomic and genomic characteristics of each chromosome are available. * Flat file versions of the BacMap gene annotations, gene sequences and protein sequences can be downloaded. BacMap can be used to: * Obtain basic genome statistics. * Visualize the genomic context of genes. * Search for orthologues and paralogues in a genome of interest. * Search for conserved operon structure. * Look for gene content differences between bacterial species. * Obtain pre-calculated annotations for bacterial genes of interest.

Proper citation: BacMap: Bacterial Genome Atlas (RRID:SCR_006988) Copy   

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http://compbio.soe.ucsc.edu/yeast_introns.html

Database of information about the spliceosomal introns of the yeast Saccharomyces cerevisiae. Listed are known spliceosomal introns in the yeast genome and the splice sites actually used are documented. Through the use of microarrays designed to monitor splicing, they are beginning to identify and analyze splice site context in terms of the nature and activities of the trans-acting factors that mediate splice site recognition. In version 3.0, expression data that relates to the efficiency of splicing relative to other processes in strains of yeast lacking nonessential splicing factors is included. These data are displayed on each intron page for browsing and can be downloaded for other types of analysis.

Proper citation: Yeast Intron Database (RRID:SCR_007144) Copy   

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