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Related Concept Videos

Ribosome Profiling02:24

Ribosome Profiling

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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.
The technique...
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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. 
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Related Experiment Video

Updated: Jan 5, 2026

Purifying the Impure: Sequencing Metagenomes and Metatranscriptomes from Complex Animal-associated Samples
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Advances and Challenges in Metatranscriptomic Analysis.

Migun Shakya1, Chien-Chi Lo1, Patrick S G Chain1

  • 1Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, United States.

Frontiers in Genetics
|October 15, 2019
PubMed
Summary
This summary is machine-generated.

Computational metatranscriptomics analyzes active microbial communities by sequencing RNA. This review covers tools and methods for studying microbial gene expression and community dynamics.

Keywords:
RNASeqgene expressionmicrobiomeomicsworkflows

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Genomics

Background:

  • Traditional microbiome studies (16S rRNA, shotgun metagenomics) identify organisms and genes but not their activity.
  • These methods provide community membership and functional potential but lack insight into active biological processes.
  • Understanding microbial community responses requires analyzing active gene expression.

Purpose of the Study:

  • To provide a comprehensive review of computational metatranscriptomics approaches for studying microbial community transcriptomes.
  • To compare metatranscriptomics with other microbiome analysis methods, detailing strengths and weaknesses.
  • To outline available tools, workflows, use cases, and limitations in the field.

Main Methods:

  • Review of existing literature and computational tools for metatranscriptomics data analysis.
  • Comparative analysis of amplicon sequencing, shotgun metagenomics, and metatranscriptomics.
  • Identification and categorization of computational workflows and software for transcriptome analysis.

Main Results:

  • Metatranscriptomics offers deeper insights into active microbial communities by analyzing gene expression.
  • The field is rapidly advancing, with increasing data complexity and project scope (e.g., longitudinal studies).
  • A range of computational tools and workflows are available, with ongoing needs for development.

Conclusions:

  • Computational metatranscriptomics is crucial for understanding dynamic microbial community responses to environmental changes.
  • The review provides a valuable resource for researchers navigating the computational landscape of metatranscriptomics.
  • Future development is needed to fully harness the potential of large-scale metatranscriptomic data analysis.