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http://www.dnaftb.org/dnaftb/

An animated primer on the basics of DNA, genes, and heredity organized around three key concepts: Classical Genetics, Molecules of Genetics, and Genetic Organization and Control. The science behind each concept is explained by: animation, image gallery, video interviews, problem, biographies, and links.

Proper citation: DNA From The Beginning: AN Animated Primer on the Basics of DNA, Genes, and Heredity (RRID:SCR_008028) Copy   

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http://locus.jouy.inra.fr/cgi-bin/bovmap/intro.pl

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 22, 2016. Database containing information on the cattle genome comprising loci list, phenes list, homology query, cattle maps, gene list, and chromosome homology. The objective of BovMap is to develop a set of anchored loci for the cattle genome map. In total, 58 clones were hybridized with chromosomes and identified loci on 22 of the 31 different bovine chromosomes. Three clones contained satellite DNA. Two or more markers were placed on 12 chromosomes. Sequencing of the microsatellites and flanking regions was performed directly from 43 cosmids, as previously reported. Primers were developed for 39 markers and used to describe the polymorphism associated with the corresponding loci. Users are also allowed to summit their own data for Bovmap. An integrated cytogenetic and meiotic map of the bovine genome has also been developed around the Bovmap database. One objective that Bovmap uses as the mapping strategy for the bovine genome uses large insert clones as a tool for physical mapping and as a source of highly polymorphic microsatellites for genetic typing.

Proper citation: BovMap Database (RRID:SCR_008145) Copy   

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http://www.genome.wisc.edu/

The E. coli Genome Project has the goal of completely sequencing the E. coli and human genomes. They began isolation of an overlapping lambda clonebank of E. coli K-12 strain MG1655. Those clones served as the starting material in our initial efforts to sequence the whole genome. Improvements in sequencing technology have since reached the point where whole-genome sequencing of microbial genomes is routine, and the human genome has in fact been completed. They initiated additional sequencing efforts, concentrating on pathogenic members of the family Enterobacteriaceae -- to which E. coli belongs. They also began a systematic functional characterization of E. coli K-12 genes and their regulation, using the whole genome sequence to address how the over 4000 genes of this organism act together to enable its survival in a wide range of environments.

Proper citation: E. coli Genome project (RRID:SCR_008139) Copy   

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http://www.mouseatlas.org/

A portal to the Mouse Atlas of Gene Expression Project and Dissecting Gene Expression Networks in Mammalian Organogenesis Project. This Atlas will define the normal state for many tissues by determining, in a comprehensive and quantitative fashion, the number and identity of genes expressed throughout development. The resource will be comprehensive, quantitative, and publicly accessible, containing data on essentially all genes expressed throughout select stages of mouse development. Serial Analysis of Gene Expression (SAGE) is the gene expression methodology of choice for this work. Unlike expressed sequence tags (ESTs) and gene chip data, SAGE data are independent of prior gene discovery and are quantitative. Furthermore, SAGE data are digital, easily exchanged between laboratories for comparison and can be added to by scientists for years to come. Thus, this Atlas will include a data structure and data curation strategy that will facilitate the ongoing collection of gene expression data, even after the completion of this project. The Mouse Atlas project compromises 202 SAGE Libraries from 198 tissues. The list of libraries is available in a number of different groupings, including groups of libraries taken from specific tissue locations and libraries taken from specific developmental stages. Furthermore, this atlas will assemble gene expression profiles for a few focused experiments that will test hypotheses related to the techniques employed, tumor models and models of abnormal development. This will test the resource and provide quality control, validation and demonstrate applicability. Additionally, The Mammalian Organogenesis - Regulation by Gene Expression Networks (MORGEN) project will provide a complete, permanent, and accurate picture of mouse gene expression in the heart (atrioventricular canal and outflow tract), pancreas, and liver; new techniques to understand the interplay of proteins governing the expression of genes key to the development of these organ systems; and the identification of the master regulatory switches that control development of the tissues.

Proper citation: Mouse Gene Expression at the BC Cancer Agency (RRID:SCR_008091) Copy   

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http://www.koki.hu/main.php?folderID=903

The aim of this laboratory is to understand how information is encoded in specific spatiotemporal activity patterns and structural configurations at the circuit, cellular, and molecular levels in the hippocampus, thereby enabling the process of memory. A major task is to find the neuronal codes of internal representations of memory items and the mapping rules between the levels of gene expression/proteins synthesis and the level of cognitive processing. Novel combinations of approaches, including multiple single-cell recording technology, patch-clamp electrophysiology, neuroanatomy/neurochemistry at the cellular and subcellular levels, and computational models are employed to test specific hypotheses about that mapping process (such as local circuit anatomy and activity-dependent short-term and long-term synaptic plasticity). Collaborations within the Institute allows the group to also incorporate gene targeting methods and behavioral learning/memory tests in their methodological repertoire. The laboratory has been focusing on the normal and pathological (epileptic, ischemic) activity of cortical networks, with particular attention to the generation of behaviour-dependent population discharge patterns (theta and gamma oscillations, hippocampal sharp waves). Anatomical, in vitro and in vivo electrophysiological, pharmacological and molecular techniques and modeling are combined to elucidate the functional roles of inhibitory cell types in the control of population synchrony and synaptic plasticity in the hippocampus, their local and subcortical modulation via selective afferent pathways (GABAergic and cholinergic septal, as well as serotonergic raphe input) and pre- or postsynaptic receptors. An expanding new direction of research is related to the role of endocannabinoid signaling in the activity-dependent modulation of GABAergic and glutamatergic transmission, and its involvement in anxiety-like behavior.

Proper citation: Institute of Experimental Medicine of the Hungarian Academy of Sciences: Laboratory of Cerebral Cortext Reserach (RRID:SCR_008041) Copy   

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http://compgen.bio.unipd.it/bioinfo/

Welcome to the computational genomics laboratory''s home page. Main research interests: computational biology, bioinformatics and systems biology applied to the study of the mechanisms underlying gene expression regulation. They also develop bioinformatic software aiming at helping the research community solve some problems.

Proper citation: Computational Genomics Laboratory (RRID:SCR_007901) Copy   

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http://www.ebi.ac.uk/gxa/

Gene Expression Atlas is a semantically enriched database of meta-analysis based summary statistics over a curated subset of ArrayExpress Archive, servicing queries for condition-specific gene expression patterns as well as broader exploratory searches for biologically interesting genes/samples. The EBI Gene Expression Atlas Blog discusses ideas, features and problems of creating a large scale meta-analytical atlas of gene expression from publicly available microarray data. Atlas REST API provides all the results available in the main web application in a pragmatic, easy to use form - simple HTTP GET queries as input and either JSON or XML formats as output. Gene Expression Atlas goals: 1. Provision of a statistically robust framework for integration of gene expression experiment results across different platforms at a meta-analytical level 2. A simple interface for identifying strong differential expression candidate genes in conditions of interest 3. Integration of ontologies for high quality annotation of gene and sample attributes 4. Construction of new gene expression summarized views, with a view to analysis of putative signaling pathway targets, discovery of correlated gene expression patterns and the identification of condition/tissue-specific patterns of gene expression.

Proper citation: Gene Expression Atlas (RRID:SCR_007989) Copy   

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http://www.mf.uni-lj.si/bitola/

An interactive literature-based biomedical discovery support system. The goal of this system is to discover new, potentially meaningful relations between a given starting concept of interest and other concepts, by mining the bibliographic database MEDLINE. To make the system more suitable for disease candidate-gene discovery and to decrease the number of candidate relations, we integrated background knowledge about the chromosomal location of the starting disease as well as the chromosomal location of the candidate genes from resources such as Entrez Gene, HUGO and OMIM. The BITOLA system can also be used as an alternative way of searching the Medline database. The system is available in two versions: closed discovery and open discovery. Closed discovery allows the input of two concepts (Example 1: a disorder and a gene. Example 2: a drug and a side effect) and generates potential explanations of the relationship between two entities. It does this by searching published literature to finds intermediate links. Open discovery allows the input of a single concept, then categories for first-order relatives of that concept, then categories for relatives of those first order concepts. Thus it can link from a disease to related drugs, then to genes related to those drugs and then test if those genes have been mentioned/tested in association with the disease. If the answer is no, then the gene is potentially related yet untested in the literature. Thus the open discovery tool is a nominator of new genes, drugs or neuroscience correlates to be investigated with diseases, disorders, physiological responses or any other phenotype.

Proper citation: BITOLA: Biomedical Discovery Support System (RRID:SCR_008175) Copy   

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http://mousemutant.jax.org/index.html

Producer and supplier of a collection of mice bearing spontaneous mutations. These mice contribute to a better understanding of the genetic bases of neurological, neuromuscular, sensory, metabolic, skeletal/craniofacial and developmental disorders and conditions such as diabetes, obesity and heart disease.

Proper citation: Mouse Mutant Resource (RRID:SCR_008367) Copy   

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http://ophid.utoronto.ca/navigator/

A software package for visualizing and analyzing protein-protein interaction networks. NAViGaTOR can query OPHID / I2D - online databases of interaction data - and display networks in 2D or 3D. To improve scalability and performance, NAViGaTOR combines Java with OpenGL to provide a 2D/3D visualization system on multiple hardware platforms. NAViGaTOR also provides analytical capabilities and supports standard import and export formats such as GO and the Proteomics Standards Initiative (PSI). NAViGaTOR can be installed and run on Microsoft Windows, Linux / UNIX, and Mac OS systems. NAViGaTOR is written in Java and uses JOGL (Java bindings for OpenGL) to support scalability, highlighting or suppressing of information, and other advanced graphic approaches.

Proper citation: Network Analysis, Visualization and Graphing TORonto (RRID:SCR_008373) Copy   

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http://bioinformatics.ekmd.huji.ac.il/milano/

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. MILANO provides a useful tool for the automatic custom annotation of microarray results which is based on all the available literature. It has two major advances over similar tools: the ability to expand gene names to include all their informative synonyms while removing synonyms that are not informative and access to the GeneRIF database which provides short summaries of curated articles relevant to known genes. MILANO allows annotation of lists of genes derived from microarray results by user defined terms. The annotation strategy is based on counting the number of literature co-occurrences of each gene on the list with a user defined term. This strategy allows the customization of the annotation procedure and thus overcomes one of the major limitations of the functional annotations usually provided with microarray results. MILANO expands the gene names to include all their informative synonyms while filtering out gene symbols that are likely to be less informative as literature searching terms. It supports searching two literature databases: GeneRIF and Medline (through PubMed), allowing retrieval of both quick and comprehensive results. MILANO''s ability can improve microarray analysis by analyzing a list of 150 genes that were affected by p53 overproduction. This analysis reveals that it enables immediate identification of known p53 target genes on this list and assists in sorting the list into genes known to be involved in p53 related pathways, apoptosis and cell cycle arrest. This program performs automatic searches in PubMed or the GeneRIF collection for articles containing co-occurrences of search terms with a list of genes (e.g. from a microarray experiment). The program is used by pasting the list of Gene ID''s or symbols in the Primary Search Term field, and the list of cross-reference search terms in the Secondary Search Term field. The output is a table containing the number of hits for each pair of search terms. Sample input is provided in the fields, so you can just click submit and see what happens. If you want to run data of your own, delete the sample input first.

Proper citation: Microarray Literature-based Annotation (RRID:SCR_008206) Copy   

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http://mitores.ba.itb.cnr.it

MitoRes, is a comprehensive and reliable resource for massive extraction of sequences and sub-sequences of nuclear genes and encoded products targeting mitochondria in metazoa. It has been developed for supporting high-throughput in-silico analyses aimed to studies of functional genomics related to mitochondrial biogenesis, metabolism and to their pathological dysfunctions. It integrates information from the most accredited world-wide databases to bring together gene, transcript and encoded protein sequences associated to annotations on species name and taxonomic classification, gene name, functional product, organelle localization, protein tissue specificity, Enzyme Classification (EC), Gene Ontology (GO) classification and links to other related public databases. The section Cluster, has been dedicated to the collection of data on protein clustering of the entire catalogue of MitoRes protein sequences based on all versus all global pair-wise alignments for assessing putative intra- and inter-species functional relationships. The current version of MitoRes is based on the UniProt release 4 and contains 64 different metazoan species. The incredible explosion of knowledge production in Biology in the past two decades has created a critical need for bioinformatic instruments able to manage data and facilitate their retrieval and analysis. Hundreds of biological databases have been produced and the integration of biological data from these different resources is very important when we want to focus our efforts towards the study of a particular layer of biological knowledge. MitoRes is a completely rebuilt edition of MitoNuc database, which has been extensively modified to deal successfully with the challenges of the post genomic era. Its goal is to represent a comprehensive and reliable resource supporting high-quality in-silico analyses aimed to the functional characterization of gene, transcript and amino acid sequences, encoded by the nuclear genome and involved in mitochondrial biogenesis, metabolism and pathological dysfunctions in metazoa. The central features of MitoRes are: # an integrated catalogue of protein, transcript and gene sequences and sub-sequences # a Web-based application composed of a wide spectrum of search/retrieval facilities # a sequence export manager allowing massive extraction of bio-sequences (genes, introns, exons, gene flanking regions, transcripts, UTRs, CDS, proteins and signal peptides) in FASTA, EMBL and GenBank formats. It is an interconnected knowledge management system based on a MySQL relational database, which ensures data consistency and integrity, and on a Web Graphical User Interface (GUI), built in Seagull PHP Framework, offering a wide range of search and sequence extraction facilities. The database is compiled extracting and integrating information from public resources and data generated by the MitoRes team. The MitoRes database consists of comprehensive sequence entries whose core data are protein, transcript and gene sequences and taxonomic information describing the biological source of the protein. Additional information include: bio-sequences structure and location, biological function of protein product and dynamic links to both, external public databases used as data resources and public databases reporting complementary information. The core entity of the MitoRes database is represented by the protein so that each MitoRes entry is generated for each protein reported in the UniProt database as a nuclear encoded protein involved in mitochondrial biogenesis and function. Sponsors: MitoRes has been supported by Ministero Universit e Ricerca Scientifica, Italy (PRIN, Programma Biotecnologie legge 95/95-MURST 5, Proiect MURST Cluster C03/2000, CEGBA). Currently it is supported by operating grants from the Ministero dellIstruzione, dellUniversit e della Ricerca (MIUR), Italy (PNR 2001-2003 (FIRB art.8) D.M. 199, Strategic Program: Post-genome, grant 31-063933 and Project n.2, Cluster C03 L. 488/929).

Proper citation: MitoRes (RRID:SCR_008208) Copy   

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http://www.broad.mit.edu/cancer/software/genecluster2/gc2.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 17, 2013. A software package for analyzing gene expression and other bioarray data, giving users a variety of methods to build and evaluate class predictors, visualize marker lists, cluster data and validate results. GeneCluster 2.0 greatly expands the data analysis capabilities of GeneCluster 1.0 by adding supervised classification, gene selection, class discovery and permutation test methods. It includes algorithms for building and testing supervised models using weighted voting (WV) and k-nearest neighbor (KNN) algorithms, a module for systematically finding and evaluating clustering via self-organizing maps, and modules for marker gene selection and heat map visualization that allow users to view and sort samples and genes by many criteria. It enhances the clustering capabilities of GeneCluster 1.0 by adding a module for batch SOM clustering, and also includes a marker gene finder based on a KNN analysis and a visualization module. GeneCluster 2.0 is a stand-alone Java application and runs on any platform that supports the Java Runtime Environment version 1.3.1 or greater.

Proper citation: GeneCluster 2: An Advanced Toolset for Bioarray Analysis (RRID:SCR_008446) Copy   

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http://degradome.uniovi.es/diseases.html

This resource has cataloged a total of 80 human hereditary diseases caused by mutations in protease-coding genes, which implies that more than 10% of the human protease genes are involved in human pathologies. They are classified in three groups: loss of function, gain of function, and an heterogeneous group including non-protease homologs (np), putative proteases, and hedgehog proteins with only autoprocessing activity. Type of inheritance is indicated by R (recessive) or D (dominant).

Proper citation: Human Hereditary Diseases of Proteolysis (RRID:SCR_008344) Copy   

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http://www.broad.mit.edu/mammals/dog

The genome of the domesticated dog, a close evolutionary relation to human, is a powerful new tool for understanding the human genome. Comparison of the dog with human and other mammals reveals key information about the structure and evolution of genes and genomes. The unique breeding history of dogs, with their extraordinary behavioral and physical diversity, offers the opportunity to find important genes underlying diseases shared between dogs and humans, such as cancer, diabetes, and epilepsy. The Canine Genome Sequencing Project produced a high-quality draft sequence of a female boxer named Tasha. By comparing Tasha with many other breeds, the project also compiled a comprehensive set of SNPs (single nucleotide polymorphisms) useful in all dog breeds. These closely spaced genomic landmarks are critical for disease mapping. By comparing the dog, rodent, and human lineages, researchers at the Broad Institute uncovered exciting new information about human genes, their evolution, and the regulatory mechanisms governing their expression. Using SNPs, researchers describe the strikingly different haplotype structure in dog breeds compared with the entire dog population. In addition, they show that by understanding the patterns of variation in dog breeds, scientists can design powerful gene mapping experiments for complex diseases that are difficult to map in human populations. Contribute Although the astounding generosity of Eli and Edythe L. Broad and several other venture philanthropists empowers our scientists to tackle many of the most important problems at the cutting edge of genomic medicine, there are many other critical challenges that they cannot yet pursue because of limited resources. We need additional visionary partners to join the Broads and the Broad Institute in transforming medicine with the power of genomics.

Proper citation: Dog Genome Project (RRID:SCR_008486) Copy   

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http://bdtnp.lbl.gov/Fly-Net/index.jsp?w=home

The goal of this project is to decipher the transcriptional information contained in the extensive cis-acting DNA sequences that direct the patterns of gene expression that underlie animal development. Using the early embryo of the fruitfly Drosophila melanogaster as a model, these researchers are developing experimental and computational methods to systematically characterize and dissect the complex expression patterns and regulatory interactions already present prior to gastrulation. They have identified 37 principal regulatory factors within this network for initial analysis together with their target genes. Sponsors: This project is chiefly funded by a grant from NIGMS and NHGRI, R01 GM070444. Additional funding comes from grants to Michael Eisen, Sue Celniker, and Bernd Hamann.

Proper citation: Berkeley Drosophila Transcription Network Project (RRID:SCR_008640) Copy   

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http://comgen.pl/mirex/

mirEX is a comprehensive platform for comparative analysis of primary microRNA expression data. quantitative real-time PCR-based gene expression profiles are stored in a universal and expandable database scheme and wrapped by an intuitive user-friendly interface. A new way of accessing gene expression data in mirEX includes a simple mouse operated querying system and dynamic graphs for data mining analyses. In contrast to other publicly available databases, the mirEX interface allows a simultaneous comparison of expression levels between various microRNA genes in diverse organs and developmental stages. Currently, mirEX integrates information about the expression profile of 190 Arabidopsis thaliana pri-miRNAs in seven different developmental stages: seeds, seedlings and various organs of mature plants. Additionally, by providing RNA structural models, publicly available deep sequencing results, experimental procedure details and careful selection of auxiliary data in the form of web links, mirEX can function as a one-stop solution for Arabidopsis microRNA information. This database aims to be useful to anyone investigating the role of microRNAs in shaping plant development, organ formation and response to different biotic and abiotic stresses. To start exploring the database just press the "Browse Atlas" button or search for a particular microRNA record by typing at least two numbers from its ID in the window.

Proper citation: mirEX (RRID:SCR_006060) Copy   

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http://ucsd.researchaccelerator.org/

Software platform that allows researchers to easily collaborate on research and share reagents, antibodies, cell lines and more. It is designed to increase scientific collaboration across disciplines and geographical boundaries. Among the institutions now using the platform include Yale University, U of Pennsylvania, U of Chicago, Washington U, Cambridge University, University College London. The platform is licensed to select institutions. ResearchAccelerator.org allows researchers to form targeted, data driven collaborations. Researchers can search for data based on gene, disease and pathway, and they can post data which would otherwise be orphaned. The resulting collaborations, which are likely to be transdisciplinary, can greatly amplify impact and research productivity.

Proper citation: Research Accelerator (RRID:SCR_006051) Copy   

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http://pubsearch.stanford.edu/

THIS RESOURCE IS NO LONGER IN SERVCE, documented September 2, 2016. PubSearch is a web-based literature curation tool, allowing curators to search and annotate genes to keywords from articles. It has a simple mySQL database backend and uses a set of Java Servlets and JSPs for querying, modifying, and adding gene, gene-annotation, and literature information. PubSearch can be downloaded from GMOD. Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: PubSearch (RRID:SCR_005830) Copy   

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http://db-mml.sjtu.edu.cn/ICEberg/

ICEberg is an integrated database that provides comprehensive information about integrative and conjugative elements (ICEs) found in bacteria. ICEs are conjugative self-transmissible elements that can integrate into and excise from a host chromosome. An ICE contains three typical modules, integration and excision, conjugation, and regulation modules, that collectively promote vertical inheritance and periodic lateral gene flow. Many ICEs carry likely virulence determinants, antibiotic-resistant factors and/or genes coding for other beneficial traits. ICEberg offers a unique, highly organized, readily explorable archive of both predicted and experimentally supported ICE-relevant data. It currently contains details of 428 ICEs found in representatives of 124 bacterial species, and a collection of >400 directly related references. A broad range of similarity search, sequence alignment, genome context browser, phylogenetic and other functional analysis tools are readily accessible via ICEberg. ICEberg will facilitate efficient, multidisciplinary and innovative exploration of bacterial ICEs and be of particular interest to researchers in the broad fields of prokaryotic evolution, pathogenesis, biotechnology and metabolism. The ICEberg database will be maintained, updated and improved regularly to ensure its ongoing maximum utility to the research community.

Proper citation: ICEberg (RRID:SCR_006026) Copy   

•   Source: SciCrunch Registry