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http://weizhong-lab.ucsd.edu/metagenomic-analysis/

A customizable web server for fast metagenomic analysis.

Proper citation: WebMGA (RRID:SCR_011951) Copy   

•   Source: SciCrunch Registry

http://www.genoread.org/

A sequence verification pipeline where users can submit trace files to verify if a clone''s physical sequence matches its reference sequence.

Proper citation: GenoREAD (RRID:SCR_012007) Copy   

•   Source: SciCrunch Registry

http://www.unil.ch/comparativegenometrics/

The Comparative Genometrics website displays for sequenced genomes, three different genometric analyses: the DNA walk and the GC and TA skews during the initial phase. Although primarily focused on prokaryotic chromosomes, the CG website posts genometric information on paradigm plasmids, phages, viruses, and organelles. The genometric analyses are available via phylogenetic tree or alphabetical list. It also offers small genome information, for mitochondria, chloroplasts, viruses, bacteriophages, and plasmids.

Proper citation: Comparative Genometrics (RRID:SCR_012920) Copy   

•   Source: SciCrunch Registry

http://www.cancerrxgene.org/

A genomics database project is an academic research program to identify molecular features of cancers that predict response to anti-cancer drugs.

Proper citation: Genomics of Drug Sensitivity in Cancer (RRID:SCR_011956) Copy   

•   Source: SciCrunch Registry

http://www.cybase.org.au/

Cybase is dedicated to the study of a fascinating new class of proteins that possess a cyclic backbone in which the N and C termini have been joined with a conventional amide bond. These recently characterized molecules have now been found in organisms from all kingdoms of life and given the current rate of discovery the number of sequences could soon number in the hundreds. Research in our lab is aimed at further characterizing cyclic proteins and adapting them for commercial and medicinal use. In particular we work on a class of cyclic protein named the cyclotides. These proteins are found in the plants of the Rubiaceae and Violaceae and our specific goals include: determining the role that cyclotides play in plants, discovering the mechanism of action of the wide range of biological activities displayed by the cyclotides (including anti-HIV, anti-bacterial and insecticidal activity), characterising the genetics of the cyclotides and further discovery of novel cyclotides.

Proper citation: Cybase (RRID:SCR_012925) Copy   

•   Source: SciCrunch Registry

http://cdwscience.blogspot.fr/

A blog about genetics, genomics, and medical research.

Proper citation: My Biomedical Informatics Blog (RRID:SCR_012011) Copy   

•   Source: SciCrunch Registry

http://cbbiweb.uthscsa.edu/KMethylomes/

Datbase and web-based system for visualization and analysis of genome-wide methylation data of human cancers.

Proper citation: Cancer Methylome System (RRID:SCR_012013) Copy   

•   Source: SciCrunch Registry

http://54.235.254.95/histonehits/

A database for histone mutations and their phenotypes.

Proper citation: Histone Systematic Mutation Database (RRID:SCR_012015) Copy   

•   Source: SciCrunch Registry

http://dbcat.cgm.ntu.edu.tw/

A database of CpG islands and analytical tools for identifying comprehensive methylation profiles in cancer cells.

Proper citation: DBCAT (RRID:SCR_012014) Copy   

•   Source: SciCrunch Registry

http://www.cravat.us/

A web-based application designed with an easy-to-use interface to facilitate the high-throughput assessment and prioritization of genes and missense alterations important for cancer tumorigenesis.

Proper citation: CRAVAT (RRID:SCR_012776) Copy   

•   Source: SciCrunch Registry

http://appris.bioinfo.cnio.es/

A database that houses annotations of human splice isoforms. It adds reliable protein structural and functional data and information from cross-species conservation. A visual representation of the annotations for each gene allows users to easily identify functional changes brought about by splicing events. In addition to collecting, integrating and analyzing reliable predictions of the effect of splicing events, it also selects a single reference sequence for each gene, termed the principal isoform, based on the annotations of structure, function and conservation for each transcript.

Proper citation: APPRIS (RRID:SCR_012019) Copy   

•   Source: SciCrunch Registry

http://www.sdbonline.org/fly/aimain/1aahome.htm

The InterActive Fly is an online database of Drosophilia development and metazoan evolution. It contains information on biochemical pathways, organs, images, a cis-decoder, and EvoPrinter, a machine that allows users to identify Evolutionarily Resilient DNA Sequences.

Proper citation: Interactive Fly (RRID:SCR_012784) Copy   

•   Source: SciCrunch Registry

https://omictools.com/fusiondb-tool

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. FusionDB is a database of bacterial and archaeal gene fusion events - also known as Rosetta stones. Gene-fusion events are not the only resource to determine functional links between two proteins. Similar phylogenetic profiles and conserved chromosomal co-localization can also be used as an indicator for such interactions.

Proper citation: FusionDB (RRID:SCR_013314) Copy   

•   Source: SciCrunch Registry

http://epi.grants.cancer.gov/CFR/about_colon.html

It is an international research infrastructure for investigators interested in conducting population and clinic-based interdisciplinary studies on the genetic and molecular epidemiology of colon cancer and its behavioral implications. A central goal of the C-CFR is the translation of this research to the clinical and prevention setting for the benefit of Registry participants and the general public. The C-CFR has information and biospecimens contributed by greater than 11,300 families across the spectrum of risk for colon cancers and from population-based or relative controls. Of particular interest are: identification and characterization of cancer susceptibility genes definition of gene-gene and gene-environment interactions in cancer etiology translational, preventive, and behavioral implications of research findings Special features include: population-based and clinic-based ascertainment systematic collection of validated family history epidemiologic risk factor data clinical and follow-up data biospecimens (including tumor blocks and EBV transformed cell lines) ongoing molecular characterization of the participating families Goals: to contribute to the development of public health measures for the general population by increasing knowledge on genetic factors affecting cancer susceptibility and modification by environmental and lifestyle factors to protect those with increased susceptibility from developing cancer to provide life-prolonging treatment to genetically susceptible individuals Objectives: to establish a comprehensive research resource infrastructure to assist with the implementation of collaborative, interdisciplinary research protocols in the genetic epidemiology of cancer to identify, characterize, and follow-up a cohort of individuals and their family members, spanning the spectrum of cancer risk to identify diverse genetically susceptible populations that could benefit from enrollment in preventive and therapeutic interventions to develop an adaptive and evolving informatics model to support ongoing and future research consortia Sponsor. This study was supported by National Cancer Institute Grants R01 CA47147, R01 CA47305, and R01 CA69664.

Proper citation: Colon CFR (RRID:SCR_013162) Copy   

•   Source: SciCrunch Registry

http://www.abrn.net/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 32,2023.

Proper citation: Austrailasian Biospecimen Network (RRID:SCR_013161) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/thornton-srv/databases/MACiE/

MACiE, which stands for Mechanism, Annotation and Classification in Enzymes, is a collaborative project on enzyme reaction mechanisms. MACiE currently contains 223 fully annotated enzyme reaction mechanisms, which comprise 218 EC numbers (161 EC sub-subclasses) and 310 distinct CATH codes. It is a joint effortbetween the Mitchell Group at the Unilever Centre for Molecular Informatics part of the University of Cambridge and the Thornton Group at the European Bioinformatics Institute.

Proper citation: MACiE (RRID:SCR_013296) Copy   

•   Source: SciCrunch Registry

http://www.ncbi.nlm.nih.gov/RefSeq/HIVInteractions/index.html

The Division of Acquired Immunodeficiency Syndrome (DAIDS) of the National Institute of Allergy and Infectious Diseases (NIAID) has initiated a project, in collaboration with Southern Research Institute and the National Center for Biotechnology Information (NCBI), designed to compile a comprehensive database of the described interactions between HIV-1 and cellular proteins. The goal of this project is to provide scientists in the field of HIV/AIDS research a concise, yet detailed, summary of all known interactions of HIV-1 proteins with host cell proteins, other HIV-1 proteins, or proteins from disease organisms associated with HIV/AIDS. This database has been designed to track the following information for each protein-protein interaction identified in the literature: * NCBI Reference Sequence (RefSeq) protein accession numbers. * NCBI Entrez Gene ID numbers. * Amino acids from each protein that are known to be involved in the interaction. * Brief description of the protein-protein interaction. * Keywords to support searching for interactions. * National Library of Medicine (NLM) PubMed identification numbers (PMIDs) for all journal articles describing the interaction.

Proper citation: HIV-1, Human Protein Interaction Database (RRID:SCR_013214) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/ena/search/

A nucleotide sequence similiary search tool which is far faster than BLAST for large datasets, with only a marginal loss in search sensitivity.

Proper citation: ENA Sequence Search (RRID:SCR_013170) Copy   

•   Source: SciCrunch Registry

http://www.phenomicdb.de/

PhenomicDB is a multi-organism phenotype-genotype database including human, mouse, fruit fly, C.elegans, and other model organisms. The inclusion of gene indices (NCBI Gene) and orthologs (same gene in different organisms) from HomoloGene allows to compare phenotypes of a given gene over many organisms simultaneously. PhenomicDB contains data from publicly available primary databases: FlyBase, Flyrnai.org, WormBase, Phenobank, CYGD, MatDB, OMIM, MGI, ZFIN, SGD, DictyBase, NCBI Gene, and HomoloGene. We brought this wealth of data into a single integrated resource by coarse-grained semantic mapping of the phenotypic data fields, by including common gene indexes (NCBI Gene), and by the use of associated orthology relationships (HomoloGene). PhenomicDB is thought as a first step towards comparative phenomics and will improve the understanding of the gene functions by combining the knowledge about phenotypes from several organisms. It is not intended to compete with the much more dedicated primary source databases but tries to compensate its partial loss of depth by linking back to the primary sources. The basic functional concept of PhenomicDB is an integrated meta-search-engine for phenotypes. Users should be aware that comparison of genotypes or even phenotypes between organisms as different as yeast and man can have serious scientific hurdles. Nevertheless finding that the phenotype of a given mouse gene is described as ��similar to psoriasis�� and at the same time that the human ortholog has been described as a gene causing skin defects can lead to novelty and interesting hypotheses. Similarly, a gene involved in cancer in mammalian organisms could show a proliferation phenotype in a lower organism such as yeast and thus, give further insights to a researcher.

Proper citation: PhenomicDB (RRID:SCR_013051) Copy   

•   Source: SciCrunch Registry

http://www.scmbb.ulb.ac.be/Users/benoit/LigASite

A gold-standard dataset of biologically relevant binding sites in protein structures. It consists of proteins with one unbound structure and at least one structure of the protein-ligand complex. Both a redundant and a non-redundant (sequence identity lower than 25) version is available. Quaternary structures proposed by PQS (2) are used for all structures in the dataset. The availability of both unbound and bound structures for each protein guarantees that our dataset can be used to benchmark binding site prediction methods, in conditions that mimic cases where the binding site is truly unknown. In cases where several different bound structures are available for a given protein, all are used to define the binding sites.

Proper citation: LIGand Attachment SITE Database (RRID:SCR_013172) Copy   

•   Source: SciCrunch Registry