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http://dmpi.duke.edu/molecular-genomics-shared-resource

Core offers variety of experimental platforms to facilitate genomics research. Accredited as Duke Shared Resource facility offers experience with genetic, genomic and epigenomic study design and technology, working closely with researchers to customize experiments to meet their needs. Applications include 10x Genomics NGS library generation for both single cell and gDNA experiments, DNA methylation microarrays, SNP genotyping and copy number microarrays, and Taqman targeted SNP genotyping.

Proper citation: Duke University Molecular Genomics Core Facility (RRID:SCR_017860) Copy   

•   Source: SciCrunch Registry

https://escore.ucsf.edu/

Core provides ES cell services with high probability of germline transmission. Offers ES cell targeting, genomic DNA extraction from 96-well plates, expansion of targeted ES cells, chromosome counts, and preparation of ES cells for microinjection.Prior to initiation of project, consultation is available on entire procedures of generating knockout mice. Core works with Gladstone Transgenic Gene Targeting Core for your microinjections to deliver full-range gene targeting service;CRISPR gRNA cloning,Cell-based functional test to identify best-performing TALENs or sgRNAs for your gene-editing experiment via mismatch-based assays such as Surveyor or T7E1;In vitro RNA synthesis - can help to make RNAs for your zygote injection or RNA transfection. We have TALEN and Cas9 plasmids with either T7 or T3 promoter subcloned in for efficient in vitro synthesis.sgRNAs for CRISPR can be synthesized off T7-sgRNA PCR product. Quality of synthesized RNAs will be checked via bioanalyzer;Custom TALEN to make double-strand breaks in genome;ES cell targeting (feeder-independent).Investigators targeting construct will be electroporated by core personnel. We have two feeder-independent ES cell lines, E14 (129-derived) and JM8A3.N1 (C57BL/6-derived) you can choose from. After drug selection for about one week, up to 300 colonies will be picked. When they are about to be confluent, we will split them as duplicate, one master plate to freeze for future expansion of positive clones and one plate for genotyping to identify targeted ES cell clones. Your plates for genotyping will be ready for pick-up 2-3 weeks after electroporation date.Genomic DNA extraction from ES cells on 96-well plate;Expansion of targeted clones from core targeting (up to 5 clones),A maximum of 5 positive clones will be thawed from 96-well plates and expanded to 6-wells. We will freeze 5 vials (each about 1 million)/clone for future use and give you 1 vial-equivalent cells to validate your genotyping before injection. It takes about 10 days to expand and freeze down cells;Expansion of ES cells from outside resources (per clone) Investigators provide one vial of frozen ES cells with information about culture condition from original resource. We will revive, nurture, and refreeze ES cells (5 vials) when they are ready. In addition, we will give you 1~2 million cells for your genotyping verification;Preparation for microinjection;Chromosome counting;Custom services.

Proper citation: University of California at San Francisco Embryonic Stem Cell Targeting Core Facility (RRID:SCR_017902) Copy   

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https://www.bi.vt.edu/services/genomics-sequencing-center

Core for development and application of Next-Generation Sequencing technologies. Provides experimental design consultation, and genomic, transcriptomic, and functional-genomics services. Specializes in development and application of Next-Generation Sequencing technologies and bioinformatics analyses. Instruments include Illumina NovaSeq 6000, Illumina NextSeq 500,Illumina MiSeq,Thermo Ion S5. Services include mRNA-Seq: Stranded and non-stranded, high levels of multiplexing up to 96 or more samples on NovaSeq;Standard amounts, Stranded-Seq: 500 ng total RNA, RIN 8;Low Input amounts, Stranded-Seq: 5 ng to 100 ng total RNA;Ultra Low Input amounts, Non-Stranded-Seq: 1-1000 cells or 10 pg - 10 ng;Total RNA-Seq - Stranded: 5-250 ng;Small RNA-Seq: 1 ug, multiplexing up to 48 samples/NextSeq run;Partially degraded samples - Stranded and Non-Stranded: LCM, FFPE samples, both stranded and non-stranded, 50 -100 ng;Microbial rRNA depletion and RNA-Seq with amounts as low as 1-5 ug of total RNA;Whole Genome Sequencing;Human / Animal / Plant;Microbial;As low as 1 ng De novo Sequencing;Exome/Targeted capture re-sequencing: Enables high sequencing depths;Agilent and Illumina platforms;Human, Mouse, Canine and other species;Targeted re-sequencing: High levels of multiplexing up to 200 samples / MiSeq run;PCR Amplicon sequencing;Illumina and Agilent platforms;ChIP-Seq;Transcription factor analysis;Histone modifications;DNA Methylation;MeDIP- and MBD-Seq;MethylC-Seq;Agilent SureSelect MethylC-Seq;Nucleosome Mapping;FAIRE-Seq and DNAse I-Seq;16S / 18S / ITS amplicon sequencing;Whole Genome Metagenomic sequencing;Metatranscriptomic analysis;DNA/chromatin fragmentation by Covaris DNA / RNA quality analysis: BioAnalyzer / TapeStation assay, Qubit (Picogreen) assays;qPCR services.

Proper citation: Virginia Tech Biocomplexity Institute Genomics Sequencing Center Core Facility (RRID:SCR_017958) Copy   

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http://cqls.oregonstate.edu/

Formerly Center for Genome Research and Biocomputing Core Facility. Functions and facilities include services in genomics, functional genomics, genotyping and imaging.Biocomputing facilities with computing infrastructure, which includes managed cloud and shared resources, data analyses and training are customized to individual needs, including genome assembly and annotation, analysis of RNAseq, GBS, and metagenomics data, and GPU-enabled deep learning analyses.

Proper citation: Oregon State University Center for Quantitative Life Sciences Core Facility (RRID:SCR_018373) Copy   

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http://dunham.gs.washington.edu/protocols.shtml

A portal for Maitreya Dunham's lab, which works on the genomic analysis of experimental evolution in yeast using microarrays and the chemostat. Research interests of the lab include experimental evolution of genetic networks in yeast, aneuploidy and copy number variation, comparative genomics, technology development and human genetics in yeast.

Proper citation: Maitreya Dunham's Lab (RRID:SCR_000784) Copy   

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http://www.fged.org/

Society that develop standards for biological research data quality, annotation and exchange. They facilitate the creation and use of software tools that build on these standards and allow researchers to annotate and share their data easily. They promote scientific discovery that is driven by genome wide and other biological research data integration and meta-analysis. Historically, FGED began with a focus on microarrays and gene expression data. However, the scope of FGED now includes data generated using any technology when applied to genome-scale studies of gene expression, binding, modification and other related applications.

Proper citation: FGED (RRID:SCR_001897) Copy   

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http://discover.nci.nih.gov/gominer/

GoMiner is a tool for biological interpretation of "omic" data including data from gene expression microarrays. Omic experiments often generate lists of dozens or hundreds of genes that differ in expression between samples, raising the question, What does it all mean biologically? To answer this question, GoMiner leverages the Gene Ontology (GO) to identify the biological processes, functions and components represented in these lists. Instead of analyzing microarray results with a gene-by-gene approach, GoMiner classifies the genes into biologically coherent categories and assesses these categories. The insights gained through GoMiner can generate hypotheses to guide additional research. GoMiner displays the genes within the framework of the Gene Ontology hierarchy in two ways: * In the form of a tree, similar to that in AmiGO * In the form of a "Directed Acyclic Graph" (DAG) The program also provides: * Quantitative and statistical analysis * Seamless integration with important public databases GoMiner uses the databases provided by the GO Consortium. These databases combine information from a number of different consortium participants, include information from many different organisms and data sources, and are referenced using a variety of different gene product identification approaches.

Proper citation: GoMiner (RRID:SCR_002360) Copy   

•   Source: SciCrunch Registry

http://galton.uchicago.edu/~junzhang/LAPSTRUCT.html

Software application to describe population structure using biomarker data ( typically SNPs, CNVs etc.) available in a population sample. The main features different from PCA are: (1) geometrically motivated and graphic model based; (2)robustness of outliers. (entry from Genetic Analysis Software)

Proper citation: LAPSTRUCT (RRID:SCR_007550) Copy   

•   Source: SciCrunch Registry

http://www.pedigree-draw.com/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on April 12,2024. Software application for pedigree drawing (entry from Genetic Analysis Software)

Proper citation: Pedigree-Draw (RRID:SCR_008302) Copy   

•   Source: SciCrunch Registry

http://bnmc.caltech.edu/

The Beckman Institute BNMC brings together researchers from many disciplines at Caltech to address problems in the mechanistic modeling of coupled genomic, intercellular and intracellular processes. It represents an attempt to encourage closer interaction and collaboration between groups in Biology, Control and Dynamical Systems, and the Center for Advanced Computing Research. The focus of BNMC is biochemical phenomena occurring within and between cells, in particular the mechanistic modeling of molecular networks of all kinds (e.g., transcriptional, regulatory, metabolic, signal transduction, mechanical, etc.) with and without spatial variation and intercellular communication. BNMC is formed as a coordinated effort aimed at (1) applying existing capabilities to collaboratively solve biological modeling problems that arise in answering scientific questions in Caltech laboratories, (2) exploring a diversity of novel approaches in order to achieve fundamental advances necessary to address the classes of modeling problems biologists want to solve, and (3) organizing projects to better share human experience as well as common infrastructure to avoid duplication and maximize solution interoperability.

Proper citation: Caltech, The Beckman Institute: The Biological Network Modeling Center (RRID:SCR_008060) Copy   

•   Source: SciCrunch Registry

https://code.google.com/p/ontology-for-genetic-interval/

An ontology that formalized the genomic element by defining an upper class genetic interval using BFO as its framework. The definition of genetic interval is the spatial continuous physical entity which contains ordered genomic sets (DNA, RNA, Allele, Marker,etc.) between and including two points (Nucleic_Acid_Base_Residue) on a chromosome or RNA molecule which must have a liner primary sequence structure.

Proper citation: Ontology for Genetic Interval (RRID:SCR_003423) Copy   

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http://smd.stanford.edu/cgi-bin/source/sourceSearch

SOURCE compiles information from several publicly accessible databases, including UniGene, dbEST, UniProt Knowledgebase, GeneMap99, RHdb, GeneCards and LocusLink. GO terms associated with LocusLink entries appear in SOURCE. The mission of SOURCE is to provide a unique scientific resource that pools publicly available data commonly sought after for any clone, GenBank accession number, or gene. SOURCE is specifically designed to facilitate the analysis of large sets of data that biologists can now produce using genome-scale experimental approaches Platform: Online tool

Proper citation: SOURCE (RRID:SCR_005799) Copy   

•   Source: SciCrunch Registry

http://www.daimi.au.dk/%7Emailund/SNPFile/

Software library and API for manipulating large SNP datasets with associated meta-data, such as marker names, marker locations, individuals'' phenotypes, etc. in an I/O efficient binary file format. In its core, SNPFile assumes very little about the metadata associated with markers and individuals, but leaves this up to application program protocols. (entry from Genetic Analysis Software)

Proper citation: SNPFILE (RRID:SCR_009402) Copy   

•   Source: SciCrunch Registry

https://genomecenter.ucdavis.edu/core-facilities/

Genome Center uses technologies to understand how heritable genetic information of diverse organisms functions in health and disease. Provides research facilities, service cores, and staff for genomics research and training. Core facilities for Bioinformatics,DNA Technologies and Expression Analysis, Metabolomics, Proteomics,TILLING Core,Yeast One Hybrid Services Core.

Proper citation: UC Davis Genome Center Labs and Facilities (RRID:SCR_012480) Copy   

•   Source: SciCrunch Registry

https://atgu.mgh.harvard.edu/plinkseq/

An open-source C/C++ library for working with human genetic variation data. The specific focus is to provide a platform for analytic tool development for variation data from large-scale resequencing projects, particularly whole-exome and whole-genome studies. However, the library could in principle be applied to other types of genetic studies, including whole-genome association studies of common SNPs. (entry from Genetic Analysis Software)

Proper citation: PLINK/SEQ (RRID:SCR_013193) Copy   

•   Source: SciCrunch Registry

http://igs-server.cnrs-mrs.fr/mgdb/Rickettsia/

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 18, 2016. Rickettsia are obligate intracellular bacteria living in arthropods. They occasionally cause diseases in humans. To understand their pathogenicity, physiologies and evolutionary mechanisms, RicBase is sequencing different species of Rickettsia. Up to now we have determined the genome sequences of R. conorii, R. felis, R. bellii, R. africae, and R. massiliae. The RicBase aims to organize the genomic data to assist followup studies of Rickettsia. This website contains information on R. conorii and R. prowazekii. A R. conorii and R. prowazekii comparative genome map is also available. Images of genome maps, dendrogram, and sequence alignment allow users to gain a visualization of the diagrams.

Proper citation: Rickettsia Genome Database (RRID:SCR_007102) Copy   

•   Source: SciCrunch Registry

http://bond.unleashedinformatics.com/

THIS RESOURCE IS NO LONGER IN SERVICE, documented May 10, 2017. A pilot effort that has developed a centralized, web-based biospecimen locator that presents biospecimens collected and stored at participating Arizona hospitals and biospecimen banks, which are available for acquisition and use by researchers. Researchers may use this site to browse, search and request biospecimens to use in qualified studies. The development of the ABL was guided by the Arizona Biospecimen Consortium (ABC), a consortium of hospitals and medical centers in the Phoenix area, and is now being piloted by this Consortium under the direction of ABRC. You may browse by type (cells, fluid, molecular, tissue) or disease. Common data elements decided by the ABC Standards Committee, based on data elements on the National Cancer Institute''s (NCI''s) Common Biorepository Model (CBM), are displayed. These describe the minimum set of data elements that the NCI determined were most important for a researcher to see about a biospecimen. The ABL currently does not display information on whether or not clinical data is available to accompany the biospecimens. However, a requester has the ability to solicit clinical data in the request. Once a request is approved, the biospecimen provider will contact the requester to discuss the request (and the requester''s questions) before finalizing the invoice and shipment. The ABL is available to the public to browse. In order to request biospecimens from the ABL, the researcher will be required to submit the requested required information. Upon submission of the information, shipment of the requested biospecimen(s) will be dependent on the scientific and institutional review approval. Account required. Registration is open to everyone.. Documented on August 19,2019.BOND, which requires registration of a free account, is a resource used to perform cross-database searches of available sequence, interaction, complex and pathway information. BOND integrates a range of component databases including GenBank and BIND, the Biomolecular Interaction Network Database. BOND contains 70+ million biological sequences, 33,000 structures, 38,000 GO terms, and over 200,000 human curated interactions contained in BIND, and is open access. BOND serves the interests of the developing global interactome effort encompassing the genomic, proteomic and metabolomic research communities. BOND is the first open access search resource to integrate sequence and interaction information. BOND integrates BLAST functionality, and contains a well-documented API. BOND also stores annotation links for sequences, including links to Genome Ontology descriptions, MedLine abstracts, taxon identifiers, associated structures, redundant sequences, sequence neighbors, conserved domains, data base cross-references, Online Mendalian Inheritance in Man identifiers, LocusLink identifiers and complete genomes. BIND on BOND The Biomolecular Interaction Network Database (BIND), a component database of BOND, is a collection of records documenting molecular interactions. The contents of BIND include high-throughput data submissions and hand-curated information gathered from the scientific literature. BIND is an interaction database with three classifications for molecular associations: molecules that associate with each other to form interactions, molecular complexes that are formed from one or more interaction(s) and pathways that are defined by a specific sequence of two or more interactions.Interactions A BIND record represents an interaction between two or more objects that is believed to occur in a living organism. A biological object can be a protein, DNA, RNA, ligand, molecular complex, gene, photon or an unclassified biological entity. BIND records are created for interactions which have been shown experimentally and published in at least one peer-reviewed journal. A record also references any papers with experimental evidence that support or dispute the associated interaction. Interactions are the basic units of BIND and can be linked together to form molecular complexes or pathways. The BIND interaction viewer is a tool to visualize and analyze molecular interactions, complexes and pathways. The BIND interaction viewer uses Ontoglyphs to display information about a protein via attributes such as molecular function, biological process and sub-cellular localization. Ontoglyphs allow to graphically and interactively explore interaction networks, by visualizing interactions in the context of 34 functional, 25 binding specificity and 24 sub-cellular localization Ontoglyphs categories. We will continue to provide an open access version of BOND, providing its subscribers with free, unlimited access to a core content set. But we are confident you will soon want to upgrade to BONDplus.

Proper citation: Biomolecular Object Network Databank (RRID:SCR_007433) Copy   

•   Source: SciCrunch Registry

http://mips.gsf.de/genre/proj/ustilago/

The MIPS Ustilago maydis Genome Database aims to present information on the molecular structure and functional network of the entirely sequenced, filamentous fungus Ustilago maydis. The underlying sequence is the initial release of the high quality draft sequence of the Broad Institute. The goal of the MIPS database is to provide a comprehensive genome database in the Genome Research Environment in parallel with other fungal genomes to enable in depth fungal comparative analysis. The specific aims are to: 1. Generate and assemble Whole Genome Shotgun sequence reads yielding 10X coverage of the U. maydis genome 2. Integrate the genomic sequence assembly with physical maps generated by Bayer CropScience 3. Perform automated annotation of the sequence assembly 4. Align the strain 521 assembly with the FB1 assembly provided by Exelixis 5. Release the sequence assembly and results of our annotation and analysis to public Ustilago maydis is a basidiomycete fungal pathogen of maize and teosinte. The genome size is approximately 20 Mb. The fungus induces tumors on host plants and forms masses of diploid teliospores. These spores germinate and form haploid meiotic products that can be propagated in culture as yeast-like cells. Haploid strains of opposite mating type fuse and form a filamentous, dikaryotic cell type that invades plant tissue to reinitiate infection. Ustilago maydis is an important model system for studying pathogen-host interactions and has been studied for more than 100 years by plant pathologists. Molecular genetic research with U. maydis focuses on recombination, the role of mating in pathogenesis, and signaling pathways that influence virulence. Recently, the fungus has emerged as an excellent experimental model for the molecular genetic analysis of phytopathogenesis, particularly in the characterization of infection-specific morphogenesis in response to signals from host plants. Ustilago maydis also serves as an important model for other basidiomycete plant pathogens that are more difficult to work with in the laboratory, such as the rust and bunt fungi. Genomic sequence of U. maydis will also be valuable for comparative analysis of other fungal genomes, especially with respect to understanding the host range of fungal phytopathogens. The analysis of U. maydis would provide a framework for studying the hundreds of other Ustilago species that attack important crops, such as barley, wheat, sorghum, and sugarcane. Comparisons would also be possible with other basidiomycete fungi, such as the important human pathogen C. neoformans. Commercially, U. maydis is an excellent model for the discovery of antifungal drugs. In addition, maize tumors caused by U. maydis are prized in Hispanic cuisine and there is interest in improving commercial production. The complete putative gene set of the Broad Institute''s second release is loaded into the database and in addition all deviating putative genes from a putative gene set produced by MIPS with different gene prediction parameters are also loaded. The complete dataset will then be analysed, gene predictions will be manually corrected due to combined information derived from different gene prediction algorithms and, more important, protein and EST comparisons. Gene prediction will be restricted to ORFs larger than 50 codons; smaller ORFs will be included only if similarities to other proteins or EST matches confirm their existence or if a coding region was postulated by all prediction programs used. The resulting proteins will be annotated. They will be classified according to the MIPS classification catalogue receiving appropriate descriptions. All proteins with a known, characterized homolog will be automatically assigned to functional categories using the MIPS functional catalog. All extracted proteins are in addition automatically analysed and annotated by the PEDANT suite.

Proper citation: MIPS Ustilago maydis Database (RRID:SCR_007563) Copy   

•   Source: SciCrunch Registry

http://www.homepages.ed.ac.uk/pmckeigu/pooling/poolscore.htm

Software program for analysis of case-control genetic association studies using allele frequency measurements on DNA pools (entry from Genetic Analysis Software)

Proper citation: POOLSCORE (RRID:SCR_007514) Copy   

•   Source: SciCrunch Registry

http://atlasgeneticsoncology.org/

Online journal and database devoted to genes, cytogenetics, and clinical entities in cancer, and cancer-prone diseases. Its aim is to cover the entire field under study and it presents concise and updated reviews (cards) or longer texts (deep insights) concerning topics in cancer research and genomics.

Proper citation: Atlas of Genetics and Cytogenetics in Oncology and Haematology (RRID:SCR_007199) Copy   

•   Source: SciCrunch Registry