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http://gmd.mpimp-golm.mpg.de/

It facilitates the search for and dissemination of mass spectra from biologically active metabolites quantified using Gas chromatography (GC) coupled to mass spectrometry (MS). Use the Search Page to search for a compound of your interest, using the name, mass, formula, InChI etc. as query input. Additionally, a Library Search service enables the search of user submitted mass spectra within the GMD. In parallel to the library search, a prediction of chemical sub-groups is performed. This approach has reached beta level and a publication is currently under review. Using several sub-group specific Decision Trees (DTs), mass spectra are classified with respect to the presence of the chemical moieties within the linked (unknown) compound. Prediction of functional groups (ms analysis) facilitates the search of metabolites within the GMD by means of user submitted GC-MS spectra consisting of retention index (n-alkanes, if vailable) and mass intensities ratios. In addition, a functional group prediction will help to characterize those metabolites without available reference mass spectra included in the GMD so far. Instead, the unknown metabolite is characterized by predicted presence or absence of functional groups. For power users this functionality presented here is exposed as soap based web services. Functional group prediction of compounds by means of GC-EI-MS spectra using Microsoft analysis service decision trees All currently available trained decision trees and sub-structure predictions provided by the GMD interface. Table describes the functional group, optional use of an RI system, record date of the trained decision tree, number of MSTs with proportion of MSTs linked to metabolites with the functional group present for each tree. Average and standard deviation of the 50-fold CV error, namely the ratio false over correctly sorted MSTs in the trained DT, are listed. The GMD website offers a range of mass spectral reference libraries to academic users which can be downloaded free of charge in various electronic formats. The libraries are constituted by base peak normalized consensus spectra of single analytes and contain masses in the range 70 to 600 amu, while the ubiquitous mass fragments typically generated from compounds carrying a trimethylsilyl-moiety, namely the fragments at m/z 73, 74, 75, 147, 148, and 149, were excluded.

Proper citation: GMD (RRID:SCR_006625) Copy   

•   Source: SciCrunch Registry

https://www.fludb.org/brc/home.spg?decorator=influenza

The Influenza Research Database (IRD) serves as a public repository and analysis platform for flu sequence, experiment, surveillance and related data.

Proper citation: Influenza Research Database (IRD) (RRID:SCR_006641) Copy   

•   Source: SciCrunch Registry

http://www.dpvweb.net/

DPVweb provides a central source of information about viruses, viroids and satellites of plants, fungi and protozoa. Comprehensive taxonomic information, including brief descriptions of each family and genus, and classified lists of virus sequences are provided. The database also holds detailed, curated, information for all sequences of viruses, viroids and satellites of plants, fungi and protozoa that are complete or that contain at least one complete gene. For comparative purposes, it also contains a single representative sequence of all other fully sequenced virus species with an RNA or single-stranded DNA genome. The start and end positions of each feature (gene, non-translated region and the like) have been recorded and checked for accuracy. As far as possible, nomenclature for genes and proteins are standardized within genera and families. Sequences of features (either as DNA or amino acid sequences) can be directly downloaded from the website in FASTA format. The sequence information can also be accessed via client software for PC computers (freely downloadable from the website) that enable users to make an easy selection of sequences and features of a chosen virus for further analyses. The public sequence databases contain vast amounts of data on virus genomes but accessing and comparing the data, except for relatively small sets of related viruses can be very time consuming. The procedure is made difficult because some of the sequences on these databases are incorrectly named, poorly annotated or redundant. The NCBI Reference Sequence project (1) provides a comprehensive, integrated, non-redundant set of sequences, including genomic DNA, transcript (RNA) and protein products, for major research organisms. This now includes curated information for a single sequence of each fully sequenced virus species. While this is a welcome development, it can only deal with complete sequences. An important feature of DPV is the opportunity to access genes (and other features) of multiple sequences quickly and accurately. Thus, for example, it is easy to obtain the nucleotide or amino acid sequences of all the available accessions of the coat protein gene of a given virus species or for a group of viruses. To increase its usefulness further, DPVweb also contains a single representative sequence of all other fully sequenced virus species with an RNA or single-stranded DNA (ssDNA) genome. Sponsors: This site is supported by the Association of Applied Biologists and the Zhejiang Academy of Agricultural Sciences, Hangzhou, People''s Republic of China.

Proper citation: Descriptions of Plant Viruses (RRID:SCR_006656) Copy   

•   Source: SciCrunch Registry

http://inparanoid.sbc.su.se/cgi-bin/index.cgi

Collection of pairwise comparisons between 100 whole genomes generated by a fully automatic method for finding orthologs and in-paralogs between TWO species. Ortholog clusters in the InParanoid are seeded with a two-way best pairwise match, after which an algorithm for adding in-paralogs is applied. The method bypasses multiple alignments and phylogenetic trees, which can be slow and error-prone steps in classical ortholog detection. Still, it robustly detects complex orthologous relationships and assigns confidence values for in-paralogs. The original data sets can be downloaded.

Proper citation: InParanoid: Eukaryotic Ortholog Groups (RRID:SCR_006801) Copy   

•   Source: SciCrunch Registry

http://disulfind.dsi.unifi.it/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 28,2023, Software for predicting the disulfide bonding state of cysteines and their disulfide connectivity, starting from a protein sequence alone and may be useful in other genomic annotation tasks.

Proper citation: DISULFIND (RRID:SCR_016072) Copy   

•   Source: SciCrunch Registry

https://github.com/osallou/cassiopee-c

Software to scan an input genomic sequence (dna/rna/protein). It searchs for a subsequence that has an exact match, substitutions (Hamming distance), and/or insertion/deletions with supporting alphabet ambiguity.

Proper citation: Cassiopee (RRID:SCR_016056) Copy   

•   Source: SciCrunch Registry

https://nemoarchive.org/

Data repository specifically focused on storage and dissemination of omic data generated from BRAIN Initiative and related brain research projects. Data repository and archive for BCDC and BICCN project, among others. NeMO data include genomic regions associated with brain abnormalities and disease, transcription factor binding sites and other regulatory elements, transcription activity, levels of cytosine modification, histone modification profiles and chromatin accessibility.

Proper citation: NeMOarchive (RRID:SCR_016152) Copy   

•   Source: SciCrunch Registry

http://genome.imim.es/software/gfftools/GFF2APLOT.html

Software application to visualize the alignment of two genomic sequences together with their annotations. Used to generate print-quality images for comparative genome sequence analysis.

Proper citation: Gff2aplot (RRID:SCR_016128) Copy   

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https://gotrack.msl.ubc.ca/

Open source web-based system and database that provides access to historical records and trends in the Gene Ontology (GO) and GO annotations (GOA). Used for monitoring changes in the Gene Ontology and their impact on genomic data analysis.

Proper citation: GOTrack (RRID:SCR_016399) Copy   

•   Source: SciCrunch Registry

http://ssadedin.github.io/ximmer/

Software to help users of targeted high throughput genomic sequencing data to accurately detect copy number variants (CNVs). Framework for running and evaluating other copy number detection tools.Used for evaluating and improving performance of CNV detection in exome and targeted sequencing data.

Proper citation: Ximmer (RRID:SCR_016427) Copy   

•   Source: SciCrunch Registry

http://cajadb.neuro.ufrn.br

Software application as an integrated web resource of marmoset biological data. Used to find genomic, expression and alternative splicing data to facilitate the study of animal model for neuropsychiatric and social behavior research and to support biological analyses such as functional (ontology) enrichment analysis and protein-protein-network.

Proper citation: CajaDB (RRID:SCR_016506) Copy   

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https://genome.jgi.doe.gov/programs/fungi/1000fungalgenomes.jsf

Web application to provide genomic information for fungi. Includes sequenced fungal genomes, those in progress, and selected nominations. Nomination of new species for genome sequencing in the families or only one reference genome possible after providing DNA/RNA samples for their sequencing. Used to explore the diversity of fungi important for energy and the environment.

Proper citation: 1000 Fungal Genome Project (RRID:SCR_016463) Copy   

•   Source: SciCrunch Registry

http://gcm.wfcc.info/

Database and information retrieval, analysis, and visualization system for microbial resources to help culture collections to manage, disseminate and share the information related to their holdings. Provides an interface for the scientific and industrial communities to access the microbial resource information.

Proper citation: Global Catalogue of Microorganisms (RRID:SCR_016460) Copy   

•   Source: SciCrunch Registry

https://pave.niaid.nih.gov

Collection of curated papillomavirus genomic sequences, accompanied by web-based sequence analysis tools. Database and web applications support the storage, annotation, analysis, and exchange of information.

Proper citation: PaVE (RRID:SCR_016599) Copy   

•   Source: SciCrunch Registry

http://www.dkfz.de/en/epidemiologie-krebserkrankungen/software/software.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 24,2023. Software program that performs estimation of power and sample sizes required to detect genetic and environmental main, as well as gene-environment interaction (GxE) effects in indirect matched case-control studies (1:1 matching). When the hypothesis of GxE is tested, power/sample size will be estimated for the detection of GxE, as well as for the detection of genetic and environmental marginal effects. Furthermore, power estimation is implemented for the joint test of genetic marginal and GxE effects (Kraft P et al., 2007). Power and sample size estimations are based on Gauderman''s (2002) asymptotic approach for power and sample size estimations in direct studies of GxE. Hardy-Weinberg equilibrium and independence of genotypes and environmental exposures in the population are assumed. The estimates are based on genotypic codes (G=1 (G=0) for individuals who carry a (non-) risk genotype), which depend on the mode of inheritance (dominant, recessive, or multiplicative). A conditional logistic regression approach is used, which employs a likelihood-ratio test with respect to a biallelic candidate SNP, a binary environmental factor (E=1 (E=0) in (un)exposed individuals), and the interaction between these components. (entry from Genetic Analysis Software)

Proper citation: PIAGE (RRID:SCR_013124) Copy   

•   Source: SciCrunch Registry

http://bioinformatics.ust.hk/BOOST.html

Software application (entry from Genetic Analysis Software) for a method for detecting gene-gene interactions. It allows examining all pairwise interactions in genome-wide case-control studies.

Proper citation: BOOST (RRID:SCR_013133) Copy   

•   Source: SciCrunch Registry

http://PlasmoDB.org

Functional genomic database for malaria parasites. Database for Plasmodium spp. Provides resource for data analysis and visualization in gene-by-gene or genome-wide scale. PlasmoDB 5.5 contains annotated genomes, evidence of transcription, proteomics evidence, protein function evidence, population biology and evolution data. Data can be queried by selecting from query grid or drop down menus. Results can be combined with each other on query history page. Search results can be downloaded with associated functional data and registered users can store their query history for future retrieval or analysis.Key community database for malaria researchers, intersecting many types of laboratory and computational data, aggregated by gene.

Proper citation: PlasmoDB (RRID:SCR_013331) Copy   

•   Source: SciCrunch Registry

http://folk.uio.no/thoree/FEST/

An R package for simulations and likelihood calculations of pair-wise family relationships using DNA marker data. (entry from Genetic Analysis Software)

Proper citation: R/FEST (RRID:SCR_013347) Copy   

•   Source: SciCrunch Registry

http://www.i2b2.org

i2b2 (Informatics for Integrating Biology and the Bedside) is an NIH-funded National Center for Biomedical Computing based at Partners HealthCare System. The i2b2 Center is developing a scalable informatics framework that will enable clinical researchers to use existing clinical data for discovery research and, when combined with IRB-approved genomic data, facilitate the design of targeted therapies for individual patients with diseases having genetic origin. For some resources (e.g. software) the use of the resource requires accepting a specific (e.g. OpenSource) license.

Proper citation: Informatics for Integrating Biology and the Bedside (RRID:SCR_013629) Copy   

•   Source: SciCrunch Registry

http://www.euratrans.eu/

The European large-scale functional genomics in the rat for translational research (EURATRANS) consortium brings together investigators who will use next-generation sequencing technologies to generate genomic, transcriptomic and epigenomic datasets. The goal is to create quantitative metabonomic and proteomic datasets to give significant depth of coverage, at multiple levels, across pathophysiological phenotypes. The aim is to enable insights into disease mechanisms, through an integrative, cross-disciplinary approach to understanding large-scale functional genomic datasets in rats and humans.

Proper citation: European large-scale functional genomics in the rat for translational research (EURATRANS) (RRID:SCR_013697) Copy   

•   Source: SciCrunch Registry