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Related Concept Videos

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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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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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Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
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KEMET - A python tool for KEGG Module evaluation and microbial genome annotation expansion.

Matteo Palù1, Arianna Basile1, Guido Zampieri1

  • 1Department of Biology, University of Padova, Via U. Bassi 58/b, 35121 Padova, Italy.

Computational and Structural Biotechnology Journal
|April 15, 2022
PubMed
Summary
This summary is machine-generated.

KEMET, a new Python library, enhances microbial genome annotation by identifying missing functional genes in incomplete sequences. This improves the accuracy of metabolic models derived from metagenomic data.

Keywords:
Gene annotationGenome-scale metabolic modelHidden Markov modelMetabolic pathwayMicrobial genome

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Area of Science:

  • Genomics
  • Metagenomics
  • Bioinformatics

Background:

  • Metagenomic studies generate vast microbial genome data, posing challenges for functional potential mapping.
  • Incomplete metagenome-assembled genomes (MAGs) and partial gene sequences limit traditional annotation tools.
  • Scalable solutions are crucial for evaluating functional potential in microbial genomes.

Purpose of the Study:

  • To address annotation gaps in microbial genomes.
  • To develop a novel tool for analyzing Kyoto Encyclopedia of Genes and Genomes (KEGG) functional units.
  • To improve the functional annotation of metagenome-assembled genomes.

Main Methods:

  • Developed KEMET, an open-source Python library for KEGG functional unit analysis.
  • Focused on metabolic reaction networks to identify missing orthologs using hidden Markov model profiles.
  • Simulated assembly issues on gene sequences to evaluate KEMET's annotation expansion capabilities.

Main Results:

  • KEMET successfully identified missing KEGG orthologs in simulated incomplete genomes.
  • Recovered gene annotations significantly improved draft genome-scale metabolic models from MAGs.
  • Model accuracy from MAGs approached that of models from complete genomes.

Conclusions:

  • KEMET expands genome annotations through targeted orthologous sequence searches.
  • Enables better qualitative and quantitative assessment of metabolic capabilities in novel microbes.
  • Facilitates more accurate functional potential evaluation in metagenomic studies.