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Meta2Data: Automated data processing for large scale microbiome data re-analysis

Li, J.; Duplouy, A.; Sun, L.

2026-10-06 bioinformatics
10.64898/2026.10.01.732036 bioRxiv
Show abstract

Meta2Data is a modular, largely automated pipeline implemented in Python and Bash for the retrieval, standardization, and processing of public microbiome sequencing data. It retrieves metadata from major public repositories, including INSDC and CNCB, using user-provided keywords or BioProject/BioSample identifiers. Retrieved metadata are standardized using a synonym dictionary and then subjected to manual curation. Based on the curated metadata, Meta2Data downloads the corresponding raw sequencing data and processes them using standardized workflows based on DADA2 or VSEARCH. The pipeline supports both single- and multiple-target-region datasets and produces integrated feature tables, taxonomic assignments, and phylogenetic trees for downstream microbial community analyses. Availability and ImplementationMeta2Data is freely available at https://github.com/LinyangSun/Meta2Data and archived on Zenodo at https://doi.org/10.5281/zenodo.20670433

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The top 6 journals account for 50% of the predicted probability mass.

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"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.