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FetchM: Streamlining Genome and Metadata Integration for Microbial Comparative Genomics

Anik, T. A.

2025-04-13 bioinformatics
10.1101/2025.04.08.647722 bioRxiv
Show abstract

FetchM is a Python-based tool for fetching, analyzing, and combining bacterial genomic metadata from the NCBI Genome database and associated sample metadata from NCBI BioSample records. When working with bulk-genome analyses, such as comparative genomics or pangenome studies, you often require a unified dataset that captures the full context of a particular bacterial species population. You can obtain genomic metadata by downloading the ncbi_dataset.tsv file from the NCBI Genome database for a specific bacterial species. However, this file lacks key metadata fields such as Collection Date, Host, Geographic Location, and Isolation Source. FetchM fills this gap by automatically retrieving these missing fields, linking genome accessions to their corresponding BioSample records via the NCBI Entrez API. FetchM not only helps you compile a complete, metadata-rich dataset but also provides visualizations and summaries of both genomic and contextual metadata features. You can filter and download sequences based on specific criteria such as year, host, isolation source, country, continent, and subcontinent, making it a flexible and powerful companion for large-scale genomic studies. FetchM is available as an open-source tool at: https://github.com/Tasnimul-Arabi-Anik/FetchM. It can also be downloaded as a PyPI package.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.