Tools   Select Another Resource Report Type

http://linus.nci.nih.gov./BRB-ArrayTools.html

A software package for the visualization and statistical analysis of DNA microarray gene expression data. The tools have been developed from the R statistical system, in C and fortran programs and Java applications. They are integrated into Excel as an add-in.

Proper citation: Biometric Research Branch: ArrayTools (RRID:SCR_000778) Copy   

•   Source: SciCrunch Registry

http://www.isrec.isb-sib.ch/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. The Computational Cancer Genomics (CCG) group is dedicated to the development of analysis tools and databases relating molecular sequences and biological functions. Sponsors: This group is supported by the Swiss Institute of Bioinformatics (SIB).

Proper citation: Computational Cancer Genomics Group (RRID:SCR_000772) Copy   

•   Source: SciCrunch Registry

http://www.genet.sickkids.on.ca/cftr/

Collection of mutations in CFTR gene for international cystic fibrosis genetics research community. Provides up to date information about individual mutations in CFTR gene. All known CFTR mutations and sequence variants have been converted to standard nomenclature recommended by Human Genome Variation Society. On line process for submission of new mutations has been added.While they continue to ensure quality of data, they urge international community to give them feedback and suggestions. Clinical information in this database relates only to details of discovery of specific mutations. As part of 2010 upgrade, CFTR1 joined new project called CFTR2 - Clinical and Functional TRanslation of CFTR. Links to CFTR2 for many mutations in CFTR1 will provide up-to-date summaries of genotype-phenotype information from patient registries around the world.

Proper citation: Cystic Fibrosis Mutation Database (RRID:SCR_000685) Copy   

•   Source: SciCrunch Registry

http://megasoftware.net/

Software integrated tool for conducting automatic and manual sequence alignment, inferring phylogenetic trees, mining web based databases, estimating rates of molecular evolution, and testing evolutionary hypotheses. Used for comparative analysis of DNA and protein sequences to infer molecular evolutionary patterns of genes, genomes, and species over time. MEGA version 4 expands on existing facilities for editing DNA sequence data from autosequencers, mining Web-databases, performing automatic and manual sequence alignment, analyzing sequence alignments to estimate evolutionary distances, inferring phylogenetic trees, and testing evolutionary hypotheses. MEGA version 6 enables inference of timetrees, as it implements RelTime method for estimating divergence times for all branching points in phylogeny.

Proper citation: MEGA (RRID:SCR_000667) Copy   

•   Source: SciCrunch Registry

http://eumorphia.publicwebserver3.har.mrc.ac.uk/

A portal documenting a project for the development of novel approaches in phenotyping, mutagenesis and informatics to improve the characterization of mouse models for understanding human molecular physiology and pathology. EUMORPHIA has developed a new robust primary screening platform for determining the phenotype of mice: EMPReSS - European Mouse Phenotyping Resource for Standardised Screens. The project is also focused on training new young scientists by funding them to work in a variety of laboratories to gain a broader swathe of techniques. The project has also identified the need for more trained mouse pathologists. To address this, they are setting up training courses in pathology and working at a European level to establish more training.

Proper citation: Understanding Human Disease Through Mouse Genetics (RRID:SCR_000785) Copy   

•   Source: SciCrunch Registry

http://wannovar.usc.edu/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 6,2023. Web interface to the ANNOVAR software, a tool to annotate functional consequences of genetic variation from high-throughput sequencing data, to help biologists without bioinformatics skills to easily submit a list of mutations (even whole-genome variants calls) to the web server, select the desired annotation categories, and receive functional annotation back by emails. Given a list of single nucleotide variants (SNVs) and insertions / deletions in VCF or ANNOVAR input format, wANNOVAR annotates their functional effects on genes (such as amino acid changes for non-synonymous SNPs), calculate their predicted functional importance scores (such as SIFT and PolyPhen scores), retrieve allele frequencies in public databases (such as the 1000 Genomes Project and NHLBI-ESP 6500 exomes), and implement a variants reduction protocol to identify a subset of potentially deleterious variants., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: wANNOVAR (RRID:SCR_000565) Copy   

•   Source: SciCrunch Registry

http://mbl.org

Collection of high resolution images and databases of brains from many genetically characterized strains of mice with aim to systematically map and characterize genes that modulate architecture of mammalian CNS. Includes detailed information on genomes of many strains of mice. Consists of images from approximately 800 brains and numerical data from just over 8000 mice. You can search MBL by strain, age, sex, body or brain weight. Images of slide collection are available at series of resolutions. Apple's QuickTime Plugin is required to view available MBL Movies.

Proper citation: Mouse Brain Library (RRID:SCR_001112) Copy   

•   Source: SciCrunch Registry

http://www.bork.embl.de/j/

The main focus of this Computational Biology group is to predict function and to gain insights into evolution by comparative analysis of complex molecular data. The group currently works on three different scales: * genes and proteins, * protein networks and cellular processes, and * phenotypes and environments. They require both tool development and applications. Some selected projects include comparative gene, genome and metagenome analysis, mapping interactions to proteins and pathways as well as the study of temporal and spatial protein network aspects. All are geared towards the bridging of genotype and phenotype through a better understanding of molecular and cellular processes. The services - resources & tools, developed by Bork Group, are mainly designed and maintained for research & academic purposes. Most of services are published and documented in one or more papers. All our tools can be completely customized and integrated into your existing framework. This service is provided by the company biobyte solutions GmbH. Please visit their tools and services pages for full details and more information. Standard commercial licenses for our tools are also available through biobyte solutions GmbH. The group is partially associated with Max Delbr��ck Center for Molecular Medicine (MDC), Berlin.

Proper citation: EMBL - Bork Group (RRID:SCR_000810) Copy   

•   Source: SciCrunch Registry

http://www.bioconductor.org/packages/release/bioc/html/categoryCompare.html

A software package for meta-analysis of high-throughput experiments using feature annotations. It calculates significant annotations (categories) in each of two (or more) feature (i.e. gene) lists, determines the overlap between the annotations, and returns graphical and tabular data about the significant annotations and which combinations of feature lists the annotations were found to be significant. Interactive exploration is facilitated through the use of RCytoscape (heavily suggested).

Proper citation: categoryCompare (RRID:SCR_001223) Copy   

•   Source: SciCrunch Registry

http://mlemire.freeshell.org/SimM.README

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on April 6th,2023. Gene dropping simulation software. The program is a gzip'ed tar archive and is designed to run under UNIX/Linux operating system.

Proper citation: SIMM (RRID:SCR_000849) Copy   

•   Source: SciCrunch Registry

http://datahub.io/dataset/kupkb

A collection of omics datasets (mRNA, proteins and miRNA) that have been extracted from PubMed and other related renal databases, all related to kidney physiology and pathology giving KUP biologists the means to ask queries across many resources in order to aggregate knowledge that is necessary for answering biological questions. Some microarray raw datasets have also been downloaded from the Gene Expression Omnibus and analyzed by the open-source software GeneArmada. The Semantic Web technologies, together with the background knowledge from the domain's ontologies, allows both rapid conversion and integration of this knowledge base. SPARQL endpoint http://sparql.kupkb.org/sparql The KUPKB Network Explorer will help you visualize the relationships among molecules stored in the KUPKB. A simple spreadsheet template is available for users to submit data to the KUPKB. It aims to capture a minimal amount of information about the experiment and the observations made.

Proper citation: Kidney and Urinary Pathway Knowledge Base (RRID:SCR_001746) Copy   

•   Source: SciCrunch Registry

http://gmod.org/wiki/Main_Page

A collection of open source software tools for creating and managing genome-scale biological databases. GMOD is made up databases, applications, and adaptor software that connects these components together. You can use it to create a small laboratory database of genome annotations, or a large web-accessible community database. At first GMOD just featured model organisms but now any organism with any kind of sequence associated with it is a good candidate as a subject for a GMOD database. There are GMOD databases with just protein sequence in them, with EST sequence only, those that are concerned primarily with gene expression, and even those dedicated to collections of RNA sequence. They have also heard of GMOD databases for oligonucleotides and plasmids.

Proper citation: Generic Model Organism Database Project (RRID:SCR_001731) Copy   

•   Source: SciCrunch Registry

http://www.cbgrits.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. Time-series data sets spanning twelve time-points between E12-P9 for exploring cerebellar development of the mouse in time and space. The database contains a number of mutant / wildtype microarray datasets including two complete wildtype microarray time-series (C57BL/6 and DBA/2J). The dataset also includes in situ hybridization and bioinformatic analyses. Exploration of this dataset will allow the investigator to assess differential gene expression profiles from a developing mutant cerebella, to assess the temporal changes in gene expression in the wildtype, and to verify the cellular expression of these genes in images from our in situ hybridization library. Using the database, the investigator can explore the developmental expression or differential expression patterns of a particular gene, or create lists of similarly expression genes by building simple search algorithms. These lists can then be mined across all the datasets in both space and time. Cb GRiTS's current datasets represent gene expression analyses from multiple cerebellar mutant and wildtype single time-point and developmental series.

Proper citation: Cerebellar Gene Regulation in Time and Space Database (RRID:SCR_001699) Copy   

•   Source: SciCrunch Registry

https://physiomeproject.org/

The Physiome Project is a worldwide public domain effort to provide a computational framework for understanding human and other eukaryotic physiology. It aims to develop integrative models at all levels of biological organization, from genes to the whole organism via gene regulatory networks, protein pathways, integrative cell function, and tissue and whole organ structure/function relations. Additionally, an important goal of the project is to develop applications for teaching physiology. Current projects include the development of: - ontologies to organize biological knowledge and access to databases - markup languages to encode models of biological structure and function in a standard format for sharing between different application programs and for re-use as components of more comprehensive models - databases of structure at the cell, tissue and organ levels - software to render computational models of cell function such as ion channel electrophysiology, cell signaling and metabolic pathways, transport, motility, the cell cycle, etc. in 2 & 3D graphical form - software for displaying and interacting with the organ models which will allow the user to move across all spatial scales Sponsors: This project is supported by the International Union of Physiological Sciences (IUPS), the IEEE Engineering. in Medicine and Biology (EMBS), and the International Federation for Medical and Biological Engineering (IFMBE)

Proper citation: International Union of Physiological Sciences: Physiome Project (RRID:SCR_001760) Copy   

•   Source: SciCrunch Registry

http://csg.sph.umich.edu//abecasis/MACH/index.html

A Markov Chain based software tool for haplotyping, genotype imputation and disease association analysis that can resolve long haplotypes or infer missing genotypes in samples of unrelated individuals.

Proper citation: MACH 1.0 (RRID:SCR_001759) Copy   

•   Source: SciCrunch Registry

http://mousecyc.jax.org/

A manually curated database of both known and predicted metabolic pathways for the laboratory mouse. It has been integrated with genetic and genomic data for the laboratory mouse available from the Mouse Genome Informatics database and with pathway data from other organisms, including human. The database records for 1,060 genes in Mouse Genome Informatics (MGI) are linked directly to 294 pathways with 1,790 compounds and 1,122 enzymatic reactions in MouseCyc. (Aug. 2013) BLAST and other tools are available. The initial focus for the development of MouseCyc is on metabolism and includes such cell level processes as biosynthesis, degradation, energy production, and detoxification. MouseCyc differs from existing pathway databases and software tools because of the extent to which the pathway information in MouseCyc is integrated with the wealth of biological knowledge for the laboratory mouse that is available from the Mouse Genome Informatics (MGI) database.

Proper citation: MouseCyc (RRID:SCR_001791) 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.ncbi.nlm.nih.gov/projects/homology/maps/

This page provides quick access to the Comparative mapping functions available in the Map Viewer. Currently, comparative maps are calculated using HomoloGene orthology predictions. Once the gene pairs have been established, blocks of conserved syteny can be established using the positions of each gene object in their respective builds. Sponsors: This resource is supported by NCBI.

Proper citation: Homology Maps Page (RRID:SCR_001666) Copy   

•   Source: SciCrunch Registry

http://www.chilibot.net/

Data analysis service that searches PubMed literature database (abstracts) about specific relationships between proteins, genes, or keywords using a NLP-based text-mining approach. The results are returned as a graph. The synonym database used in Chilibot is available, without fee, for academic use only. Several different search methods are supported including: * searching for relationship between two genes, proteins or keywords * searching for relationships between many genes, proteins, or keywords * searching for relationships between two lists of genes, proteins, or keywords Advanced options include: * Automated hypothesis generation (graph) * Restricting context using keywords * Providing your own synonyms * Modifying synonyms provided by Chilibot * Color coding nodes with gene expression values * Special search: modulation

Proper citation: Chilibot: Gene and Protein relationships from MEDLINE (RRID:SCR_001705) Copy   

•   Source: SciCrunch Registry

https://rgd.mcw.edu/rgdweb/portal/home.jsp?p=4

An integrated resource for information on genes, QTLs and strains associated with diabetes. The portal provides easy acces to data related to both Type 1 and Type 2 Diabetes and Diabetes-related Obesity and Hypertension, as well as information on Diabetic Complications. View the results for all the included diabetes-related disease states or choose a disease category to get a pull-down list of diseases. A single click on a disease will provide a list of related genes, QTLs, and strains as well as a genome wide view of these via the GViewer tool. A link from GViewer to GBrowse shows the genes and QTLs within their genomic context. Additional pages for Phenotypes, Pathways and Biological Processes provide one-click access to data related to diabetes. Tools, Related Links and Rat Strain Models pages link to additional resources of interest to diabetes researchers.

Proper citation: Diabetes Disease Portal (RRID:SCR_001660) Copy   

•   Source: SciCrunch Registry