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RNA-seq

RNA-seq

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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Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

Generic and queryable data integration schema for transcriptomics and epigenomics studies.

Yael Tirlet¹, Matéo Boudet^1,2, Emmanuelle Becker¹

¹Univ Rennes, Inria, CNRS, IRISA, 35000, Rennes, France.

Computational and Structural Biotechnology Journal

|December 11, 2024

Summary

This summary is machine-generated.

We developed a flexible RDF-based schema to integrate diverse multi-omics data, enabling efficient querying of complex biological information. This approach enhances data management and analysis across various scientific domains.

Keywords:

Data integration Integration schema Multi-omics analysis Semantic web

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Area of Science:

Bioinformatics
Computational Biology
Data Science

Background:

Multi-omics datasets are rapidly expanding, posing significant challenges for data integration and querying.
Existing methods struggle to efficiently connect diverse omics, epigenomics, and regulatory information.

Purpose of the Study:

To develop a generic, flexible, and extensible RDF-based integration schema for multi-omics data.
To enable complex querying of integrated omics data, including genomic locations.

Main Methods:

Developed a Resource Description Framework (RDF)-based schema.
Integrated differential omics data, epigenomics, and regulatory information.
Employed the FALDO ontology for genomic location-based querying.

Main Results:

The schema successfully integrates various types of multi-omics data.
Validated by reproducing two published studies in biomedicine and environmental science.
Demonstrated efficient data integration and support for complex queries.

Conclusions:

The developed schema provides an effective tool for managing and querying diverse multi-omics datasets.
The generic nature of the schema allows for broad applicability across different scientific fields.
Facilitates advanced data analysis and knowledge discovery from integrated biological data.