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Related Experiment Video

Updated: May 19, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

FetchM: streamlining genome and metadata integration for bacterial comparative genomics.

Tasnimul Arabi Anik^1,2, Peter Kjær Mackie Jensen³, Anowara Begum¹

¹Environmental Microbiology Laboratory, Department of Microbiology, University of Dhaka, Dhaka 1000, Bangladesh.

Bioinformatics Advances

|May 18, 2026

Summary

This summary is machine-generated.

FetchM automates bacterial genome metadata retrieval from NCBI, integrating assembly and BioSample data. This tool enhances large-scale comparative genomics by providing ready-to-analyze datasets.

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Large-scale bacterial comparative genomics demands comprehensive metadata for genome assemblies and their biological context.
Current methods require manual merging of data from NCBI Genome and BioSample, hindering large-scale studies.

Purpose of the Study:

To develop FetchM, a Python-based tool for automated retrieval, integration, analysis, and visualization of bacterial genome metadata.
To streamline the process of linking genome assemblies with contextual information from NCBI databases.

Main Methods:

FetchM utilizes the NCBI Entrez API to link genome assemblies with BioSample records.
The tool appends missing contextual fields, supports filtering by user-defined criteria (year, host, country, continent), and generates summary tables and plots.
Automated retrieval and integration of metadata from NCBI Genome and BioSample.

Main Results:

FetchM successfully integrated metadata for 14,382 *Vibrio cholerae* genome assemblies.
Metadata availability varied, with temporal (89.07%), geographic (86.37%), and host (40.92%) data highlighting gaps in public repositories.
Validation confirmed FetchM's accuracy in retrieving and integrating genomic and contextual metadata.

Conclusions:

FetchM enables reliable and scalable integration of bacterial genomic and contextual metadata.
The tool facilitates downstream analysis by producing ready-to-analyze datasets.
FetchM also identifies limitations in metadata completeness within public repositories, guiding future data curation efforts.