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Ribosome Profiling

Ribosome Profiling

Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
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Updated: Apr 18, 2026

Purifying the Impure: Sequencing Metagenomes and Metatranscriptomes from Complex Animal-associated Samples

Purifying the Impure: Sequencing Metagenomes and Metatranscriptomes from Complex Animal-associated Samples

Published on: December 22, 2014

Deciphering metatranscriptomic data.

Evguenia Kopylova¹, Laurent Noé, Corinne Da Silva

¹LIFL, UMR CNRS 8022, Lille 1 University, Villeneuve d'Ascq, France, jenya.kopylov@gmail.com.

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

|January 12, 2015

Summary

This summary is machine-generated.

This study presents a computational method to filter ribosomal RNA (rRNA) from total RNA using SortMeRNA. This enables downstream analysis of messenger RNA (mRNA) for microbial community functional and phylogenetic insights.

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Area of Science:

Microbiology
Bioinformatics
Genomics

Background:

Metatranscriptomic data is crucial for understanding microbial community structure and function.
Total RNA comprises various RNA types, including ribosomal RNA (rRNA) and messenger RNA (mRNA), each vital for ecological insights.
Analyzing metatranscriptomic data involves transcript reconstruction and taxonomic/functional classification.

Purpose of the Study:

To demonstrate a computational technique for filtering rRNA from total RNA samples.
To propose a post-processing pipeline for analyzing rRNA-depleted metatranscriptomic data.
To facilitate functional and phylogenetic analyses of microbial communities.

Main Methods:

Utilized the SortMeRNA software for computational filtering of rRNA from total RNA.
Developed a post-processing pipeline employing current software tools for downstream analysis.
Applied methods for mRNA transcript reconstruction and phylogenetic classification using rRNA.

Main Results:

Successfully demonstrated a computational method for efficient rRNA removal from total RNA.
Established a pipeline for subsequent functional and phylogenetic analyses of microbial communities.
Enabled deeper insights into microbial community composition and gene expression.

Conclusions:

Computational rRNA filtering is effective for metatranscriptomic data analysis.
The proposed pipeline enhances the study of microbial community function and phylogeny.
This approach aids in understanding the 'hidden microbial world' through RNA analysis.