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Genome Annotation and Assembly03:36

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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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Updated: Aug 8, 2025

Analyzing Gene Expression from Marine Microbial Communities using Environmental Transcriptomics
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Analyzing Gene Expression from Marine Microbial Communities using Environmental Transcriptomics

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Reverse engineering environmental metatranscriptomes clarifies best practices for eukaryotic assembly.

Arianna I Krinos1,2,3, Natalie R Cohen4, Michael J Follows5

  • 1MIT-WHOI Joint Program in Oceanography and Applied Ocean Science and Engineering, Cambridge and Woods Hole, MA, USA. akrinos@mit.edu.

BMC Bioinformatics
|March 3, 2023
PubMed
Summary
This summary is machine-generated.

A new workflow enhances eukaryotic metatranscriptome assembly, improving the accuracy of microbial community analysis. This method aids in understanding ocean microbial eukaryotes and their essential ecosystem services.

Keywords:
EcologyMarine microbiologyMetatranscriptomicsOceanPipelineProtist

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Area of Science:

  • Marine biology
  • Microbial ecology
  • Genomics

Background:

  • Microbial eukaryotes in the ocean are vital for ecosystem services like primary production and carbon cycling.
  • Omics tools, particularly metatranscriptomics, are revolutionizing the study of these diverse communities by revealing near real-time gene expression and metabolic activity.

Purpose of the Study:

  • To present and validate a workflow for eukaryotic metatranscriptome assembly.
  • To provide an open-source tool for simulating environmental metatranscriptomes for testing and validation.
  • To reanalyze existing metatranscriptomic datasets using the developed approach.

Main Methods:

  • Development of a novel workflow for eukaryotic metatranscriptome assembly.
  • Validation using real and simulated (in-silico mock community) eukaryotic community-level expression data.
  • Application of the analysis approach to previously published metatranscriptomic datasets.

Main Results:

  • The workflow successfully recapitulates real and manufactured eukaryotic community-level expression data.
  • A multi-assembler strategy was identified as superior for eukaryotic metatranscriptome assembly.
  • The approach demonstrated improved taxonomic and functional annotations from an in-silico mock community.

Conclusions:

  • A multi-assembler approach enhances the accuracy of eukaryotic metatranscriptome assembly.
  • Systematic validation of assembly and annotation methods is crucial for reliable measurements of microbial eukaryotic community composition and function.