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RNA-seq

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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.
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An Oligonucleotide-based Tandem RNA Isolation Procedure to Recover Eukaryotic mRNA-Protein Complexes

An Oligonucleotide-based Tandem RNA Isolation Procedure to Recover Eukaryotic mRNA-Protein Complexes

Published on: August 18, 2018

An introduction to RNA databases.

Marc P Hoeppner¹, Lars E Barquist, Paul P Gardner

¹Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden.

Methods in Molecular Biology (Clifton, N.J.)

|March 19, 2014

Summary

This summary is machine-generated.

This review introduces RNA databases, exploring their history, data collection, and curation methods. It highlights how these factors influence database accuracy and scope, using Noncode, miRBase, Rfam, and SILVA as examples.

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Area of Science:

Bioinformatics
Molecular Biology
Genomics

Background:

RNA databases are crucial resources for understanding RNA biology.
The evolution of RNA databases is driven by advancements in sequencing technologies and bioinformatics.
Effective data curation is essential for the reliability and utility of RNA sequence and structure information.

Purpose of the Study:

To provide a comprehensive overview of RNA databases.
To discuss the historical development and technological underpinnings of RNA databases.
To analyze the impact of data collection and curation strategies on database quality.

Main Methods:

Literature review of RNA database development and methodologies.
Comparative analysis of data collection and curation techniques.
In-depth case studies of four prominent RNA databases: Noncode, miRBase, Rfam, and SILVA.

Main Results:

Different data collection and curation methods significantly affect the scope and accuracy of RNA databases.
Leading RNA databases like Noncode, miRBase, Rfam, and SILVA employ varied approaches to data management.
Understanding these methodologies is key to effectively utilizing RNA database resources.

Conclusions:

The quality and applicability of RNA databases are directly linked to their data acquisition and curation processes.
Researchers must be aware of the specific methodologies behind each database to interpret results accurately.
Continued development in data curation will enhance the value of RNA databases for future research.