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http://pathways.mcdb.ucla.edu/algal/

Tools to search gene lists for functional term enrichment as well as to dynamically visualize proteins onto pathway maps. Additionally, integrated expression data may be used to discover similarly expressed genes based on a starting gene of interest.

Proper citation: Algal Functional Annotation Tool (RRID:SCR_012034) Copy   

•   Source: SciCrunch Registry

https://scicrunch.org/resolver/SCR_002250

THIS RESOURCE IS NO LONGER IN SERVICE. Documented Jul 19, 2024. Metadatabase manually curated that provides web accessible tools related to genomics, transcriptomics, proteomics and metabolomics. Used as informative directory for multi-omic data analysis.

Proper citation: OMICtools (RRID:SCR_002250) Copy   

•   Source: SciCrunch Registry

http://monarchinitiative.org/

Repository of information about model organisms, in vitro models, genes, pathways, gene expression, protein and genetic interactions, orthology, disease, phenotypes, publications, and authors, and the the ability to navigate multi-scale spatial and temporal phenotypes across in vivo and in vitro model systems in the context of genetic and genomic data, using semantics and statistics. The discovery system provides basic and clinical science researchers, informaticists, and medical professionals with an integrated interface and set of discovery tools to reveal the genetic basis of disease, facilitate hypothesis generation, and identify novel candidate drug targets. The aim for this system is to promote true translational research, in that clinicians can connect with model systems researchers with expertise in related phenotypes, assays, or models. In addition to providing easy-to-use tools to navigate the model-disease data landscape, it also provides services for other resources, and educational outreach regarding the production of structured data for biomedical discovery. Model systems are the cornerstone of biomedical research to investigate biological processes, test gene-based disease hypotheses, and develop and test disease treatments. The vast knowledge about model systems can be better utilized if semantically aggregated and made queryable based on any number of facets, such as phenotypic similarity, network analysis, gene expression and function, and genomics.

Proper citation: Monarch Initiative (RRID:SCR_001373) Copy   

•   Source: SciCrunch Registry

http://www.mgc.ac.cn/TrED/

TrED is a database of Trichophyton rubrum, a fungus. The database contains strains, cDNA libraries, pathways, and microarray data as well as a directed set of literature. Trichophyton rubrum is the most common dermatophyte species and the most frequent cause of fungal skin infections in humans worldwide. It''''s a major concern because feet and nail infections caused by this organism is extremely difficult to cure. A large set of expression data including expressed sequence tags (ESTs) and transcriptional profiles of this important fungal pathogen are now available. Careful analysis of these data can give valuable information about potential virulence factors, antigens and novel metabolic pathways. We intend to create an integrated database TrED to facilitate the study of dermatophytes, and enhance the development of effective diagnostic and treatment strategies. All publicly available ESTs and expression profiles of T. rubrum during conidial germination in time-course experiments and challenged with antifungal agents are deposited in the database. In addition, comparative genomics hybridization results of 22 dermatophytic fungi strains from three genera, Trichophyton, Microsporum and Epidermophyton, are also included. ESTs are clustered and assembled to elongate the sequence length and abate redundancy. TrED provides functional analysis based on GenBank, Pfam, and KOG databases, along with KEGG pathway and GO vocabulary. It is integrated with a suite of custom web-based tools that facilitate querying and retrieving various EST properties, visualization and comparison of transcriptional profiles, and sequence-similarity searching by BLAST. TrED is built upon a relational database, with a web interface offering analytic functions, to provide integrated access to various expression data of T. rubrum and comparative results of dermatophytes. It is devoted to be a comprehensive resource and platform to assist functional genomic studies in dermatophytes.

Proper citation: TrED (RRID:SCR_005869) Copy   

•   Source: SciCrunch Registry

http://operons.ibt.unam.mx/OperonPredictor/

The Prokaryotic Operon DataBase (ProOpDB) constitutes one of the most precise and complete repository of operon predictions in our days. Using our novel and highly accurate operon algorithm, we have predicted the operon structures of more than 1,200 prokaryotic genomes. ProOpDB offers diverse alternatives by which a set of operon predictions can be retrieved including: i) organism name, ii) metabolic pathways, as defined by the KEGG database, iii) gene orthology, as defined by the COG database, iv) conserved protein motifs, as defined by the Pfam database, v) reference gene, vi) reference operon, among others. In order to limit the operon output to non-redundant organisms, ProOpDB offers an efficient protocol to select the more representative organisms based on a precompiled phylogenetic distances matrix. In addition, the ProOpDB operon predictions are used directly as the input data of our Gene Context Tool (GeConT) to visualize their genomic context and retrieve the sequence of their corresponding 5�� regulatory regions, as well as the nucleotide or amino acid sequences of their genes. The prediction algorithm The algorithm is a multilayer perceptron neural network (MLP) classifier, that used as input the intergenic distances of contiguous genes and the functional relationship scores of the STRING database between the different groups of orthologous proteins, as defined in the COG database. Nevertheless, the operon prediction of our method is not restricted to only those genes with a COG assignation, since we successfully defined new groups of orthologous genes and obtained, by extrapolation, a set of equivalent STRING-like scores based on conserved gene pairs on different genomes. Since the STRING functional relationships scores are determined in an un-bias manner and efficiently integrates a large amount of information coming from different sources and kind of evidences, the prediction made by our MLP are considerably less influenced by the bias imposed in the training procedure using one specific organism.

Proper citation: ProOpDB (RRID:SCR_006111) Copy   

•   Source: SciCrunch Registry