PubMed Health⌕ Search

PubMed · 12942040

From transcriptomics to bibliomics.

Abstract

BACKGROUND: Current biological investigations tend to operate with genomes, instead of genes as during the last century. It is possible to compare entire genomes, transcriptomes or proteomes, using alphanumeric data corresponding to the differential expression levels of thousands of genes. What remains difficult is to link array results to factual or bibliographical data and retrieve information that is highly structured and - in Shannon's sense - rare. MATERIAL/METHODS: We have developed a tool, Documentation and Information LIBrary (DILIB), that enables us to retrieve, organize and analyze huge amounts of data available on the Internet and related to microarray experiments. DILIB can link hundreds of differentially expressed genes - through their Single Identifier or GenBank accession number - to hundreds of Medline records, and can retrieve, analyze, and compare automatically thousands of non-trivial descriptors related to gene clusters. RESULTS: As exemplified with frequency comparison of MEdical Subject Headings and Registry Number descriptors, we reanalyzed the involvement of 'integrin', 'interleukin' and 'CD Antigens' in mesotheliomas. Thus, DILIB allowed us to: (i). associate literature to expressed genes, (ii). link functional transcriptomes in various experiments, (iii). associate specific descriptors to experiments, (iv). define new research areas, and eventually (v). find new functions for co-expressed genes. CONCLUSIONS: We propose a new concept, 'bibliomics', representing a subset of high quality and rare information, retrieved and organized by systematic literature-searching tools from existing databases, and related to a subset of genes functioning together in '-omic' sciences.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Bertrand H Rihn, Solveig Vidal, Claude Nemurat, Sébastien Vachenc, Steve Mohr, Florian Mazur, Philippe Houdry, Francoise Grandjean, Sophie Visvikis, Jacques Ducloy. 2003. From transcriptomics to bibliomics.. https://pubmed.ncbi.nlm.nih.gov/12942040/

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

CamK-DB: A k-mer MinHash fingerprint database for reference-free genotyping of Camellia accessions.

Tea (Camellia sinensis L.), a major global economic crop in Asia, poses challenges for genetic identification because its highly heterozygous, repetitive genome reduces the efficacy of conventional single-nucleotide polymorphism (SNP) and microsatellite markers, and interspecific hybridization further complicates the situation. To address these issues, CamK-DB was developed as a reference-free Camellia fingerprinting database built on MIKE MinHash sketches. We curated 418 candidate resequencing datasets, and built a database using standardized 5× genome-coverage fingerprints. Each accession is stored as a MIKE. jac fingerprint generated with k = 21 and recommended sketch/pre_cnt = 2000. CamK-DB provides a command-line interface for data management and a custom C++ query engine that computes top-10 matches using Jaccard similarity, complemented by a QT-based graphical interface for interactive analysis. This resource offers a robust and scalable framework for precise and routine germplasm identification, genomic phylogenetic inference, and strategic breeding program design. CamK-DB (database and code) is publicly available at https://github.com/sc-zhang/CamK-DB. CamK-DB binaries are provided for Windows 10/11 and Linux (x86_64, glibc ≥ 2.27).

Databases, Genetic↗

The Saccharomyces Genome Database-a history of ideas and accomplishments, 1994-2026.

The Saccharomyces Genome Database (SGD) is one of the longest-running and most consequential biological databases in the world. Founded in the early 1990s at Stanford University under the visionary leadership of David Botstein and developed under the long-term technical direction of J. Michael Cherry, SGD has served for more than three decades not only as the authoritative knowledge center for the budding yeast Saccharomyces cerevisiae, but also as the source for much of the fundamentals of eukaryotic biology. This history traces the arc of a remarkable intellectual and scientific project: beginning with the challenge of building the very first integrated eukaryotic genome database and evolving across 30 years into a global knowledge hub for genetics, functional genomics, and human disease research. The history is organized chronologically, with each section highlighting the central ideas, technical developments, and concrete accomplishments of that period.

Databases, Genetic↗

Gencube: centralized retrieval and integration of multi-omics resources from leading databases.

MOTIVATION: The volume of multi-omics data for diverse species is growing at an unprecedented rate, with new genome assemblies, related annotations, and high-throughput sequencing resources being submitted daily to various genomic data repositories. In response to this data influx, both existing and new databases are establishing optimized hierarchical structures to manage the vast amount of information. However, the lack of accessible command-line tools, combined with the functional limitations and unintuitive design of existing options, presents significant challenges for researchers. This gap underscores a critical need for a tool that enables streamlined retrieval and integration of omics data across these diverse repositories. RESULTS: We have developed Gencube, a command-line tool that enables centralized retrieval and integration of a comprehensive set of six different data types-genome assemblies, gene sets, annotations, sequences, comparative genomic data, and NGS-based omics resources-from various leading databases. AVAILABILITY AND IMPLEMENTATION: Gencube is a free and open-source tool, with its code available on GitHub: https://github.com/snu-cdrc/gencube and also archived on Zenodo: https://doi.org/10.5281/zenodo.14607649.

Databases, Genetic↗