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Genome Annotation and Assembly03:36

Genome Annotation and Assembly

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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
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What is Gene Expression?01:36

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A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is comprised  of nucleotides and proteins are comprised of amino acids, a mediator is required to convert the information encoded in DNA into proteins. This mediator is the messenger RNA (mRNA). mRNA copies the blueprint from DNA by a process called transcription. In eukaryotes, transcription occurs in the nucleus by complementary base-pairing with the DNA template. The mRNA is then...
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What is Gene Expression?01:42

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Overview
Gene expression is the process in which DNA directs the synthesis of functional products, that is, proteins. Cells can regulate gene expression at various stages. It allows organisms to generate different cell types and enables cells to adapt to internal and external factors.
Genetic Information Flows from DNA to RNA to Protein
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RNA-seq03:21

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Related Experiment Video

Updated: May 4, 2026

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal
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Semantic integration of gene expression analysis tools and data sources using software connectors.

Flávia A Miyazaki, Gabriela D A Guardia, Ricardo Z N Vêncio

    BMC Genomics
    |December 18, 2013
    PubMed
    Summary

    This study introduces an ontology-based methodology for semantically integrating gene expression analysis tools and data sources. This approach simplifies data exchange and interpretation for functional genomics research.

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    Area of Science:

    • Functional genomics and bioinformatics.
    • Computational biology and data integration.

    Background:

    • Gene expression analysis is crucial for functional genomics, but integrating diverse tools and data sources is challenging.
    • Semantic integration ensures consistent data meaning across applications, facilitating knowledge extraction.
    • Current methods for integrating gene expression analysis tools are often complex and error-prone.

    Purpose of the Study:

    • To develop an ontology-based methodology for the semantic integration of gene expression analysis tools and data sources.
    • To address the challenges of creating integrated analysis environments in functional genomics.
    • To enable semantically consistent and meaningful data exchange among heterogeneous systems.

    Main Methods:

    • Investigated challenges in computer system integration and the role of software connectors.
    • Studied gene expression technologies, analysis tools, and ontologies to define integration scenarios and a reference ontology.
    • Defined development activities and guidelines for creating software connectors, including data transformation rules.

    Main Results:

    • Developed and applied an ontology-based methodology for semantic integration.
    • Successfully constructed three integration scenarios using diverse tools and gene expression data types.
    • Demonstrated the effectiveness of software connectors in accessing heterogeneous data sources and managing data transformations.

    Conclusions:

    • The proposed methodology facilitates the development of connectors for semantically integrating gene expression analysis tools and data.
    • The methodology supports both simple and complex processing requirements, ensuring accurate data exchange and interpretation.
    • Enables more reliable and meaningful analysis of gene expression data through improved integration.