RESUMEN
To verify the genome annotation and to create a resource to functionally characterize the proteome, we attempted to Gateway-clone all predicted protein-encoding open reading frames (ORFs), or the 'ORFeome,' of Caenorhabditis elegans. We successfully cloned approximately 12,000 ORFs (ORFeome 1.1), of which roughly 4,000 correspond to genes that are untouched by any cDNA or expressed-sequence tag (EST). More than 50% of predicted genes needed corrections in their intron-exon structures. Notably, approximately 11,000 C. elegans proteins can now be expressed under many conditions and characterized using various high-throughput strategies, including large-scale interactome mapping. We suggest that similar ORFeome projects will be valuable for other organisms, including humans.
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Caenorhabditis elegans/genética , Genoma , Empalme Alternativo , Animales , Clonación Molecular , ADN Complementario/genética , ADN de Helmintos/genética , Bases de Datos Genéticas , Exones , Etiquetas de Secuencia Expresada , Expresión Génica , Genes de Helminto , Genómica , Proteínas del Helminto/genética , Humanos , Intrones , Sistemas de Lectura Abierta , Proteoma , ProteómicaRESUMEN
Biobanking defines all activities linked to bioresource management-whether of human, animal, microbial, or environmental origin-which means that any biobank information management system should take into account the multistep life cycle of the samples: from acquisition, through preparation, storage, to distribution to the end users (medical or research teams). Different types of biobanks can use diverse approaches, making it difficult to find software that can handle all types of scenarios. Modul-Bio has developed MBioLIMS BioBanking®, a software dedicated to biobanking, as a modular solution so that our various clients can access the functionalities and scale in a system to match their needs. These projects range from biobanks setup and managed by academic institutions, hospitals, and private companies to small and large clinical trials across different countries, as well as to whole campus or organization solutions for multiple biorepositories. Each solution differs in size, requirements, and number of users, from small biobanks with a few members of staff accessing the software to large operations with multiple sites that can collect and ship samples to a centralized site. This article explores different projects that use Modul-Bio's software in a myriad of ways to manage the complete life cycle of biospecimens and associated data.
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Bancos de Muestras Biológicas , Investigación Biomédica , Humanos , Programas Informáticos , UniversidadesRESUMEN
BACKGROUND: Transcription regulatory networks are composed of protein-DNA interactions between transcription factors and their target genes. A long-term goal in genome biology is to map protein-DNA interaction networks of all regulatory regions in a genome of interest. Both transcription factor -and gene-centered methods can be used to systematically identify such interactions. We use high-throughput yeast one-hybrid assays as a gene-centered method to identify protein-DNA interactions between regulatory sequences (e.g. gene promoters) and transcription factors in the nematode Caenorhabditis elegans. We have already mapped several hundred protein-DNA interactions and analyzed the transcriptional consequences of some by examining differential gene expression of targets in the presence or absence of an upstream regulator. The rapidly increasing amount of protein-DNA interaction data at a genome scale requires a database that facilitates efficient data storage, retrieval and integration. DESCRIPTION: Here, we report the implementation of a C. elegans differential gene expression database (EDGEdb). This database enables the storage and retrieval of protein-DNA interactions and other data that relate to differential gene expression. Specifically, EDGEdb contains: i) sequence information of regulatory elements, including gene promoters, ii) sequence information of all 934 predicted transcription factors, their DNA binding domains, and, where available, their dimerization partners and consensus DNA binding sites, iii) protein-DNA interactions between regulatory elements and transcription factors, and iv) expression patterns conferred by regulatory elements, and how such patterns are affected by interacting transcription factors. CONCLUSION: EDGEdb provides a protein-DNA -and protein-protein interaction resource for C. elegans transcription factors and a framework for similar databases for other organisms. The database is available at http://edgedb.umassmed.edu.
Asunto(s)
Caenorhabditis elegans/genética , Bases de Datos Genéticas , Análisis de Secuencia por Matrices de Oligonucleótidos/métodos , Secuencias Reguladoras de Ácidos Nucleicos , Factores de Transcripción/genética , Animales , Proteínas de Unión al ADN/genética , Redes Reguladoras de GenesRESUMEN
To initiate studies on how protein-protein interaction (or "interactome") networks relate to multicellular functions, we have mapped a large fraction of the Caenorhabditis elegans interactome network. Starting with a subset of metazoan-specific proteins, more than 4000 interactions were identified from high-throughput, yeast two-hybrid (HT=Y2H) screens. Independent coaffinity purification assays experimentally validated the overall quality of this Y2H data set. Together with already described Y2H interactions and interologs predicted in silico, the current version of the Worm Interactome (WI5) map contains approximately 5500 interactions. Topological and biological features of this interactome network, as well as its integration with phenome and transcriptome data sets, lead to numerous biological hypotheses.