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Logan: Planetary-Scale Genome Assembly Surveys Life's Diversity
Rayan Chikhi1, Téo Lemane2, Raphaël Loll-Krippleber3,4
1Institut Pasteur, Université Paris Cité, CNRS UMR3525, Paris, France.
Biorxiv : the Preprint Server for Biology
|September 15, 2025
Summary
Logan is a new massive sequence assembly that transforms public sequencing data into searchable contigs. This enables rapid discovery of novel enzymes, expands known genetic diversity, and facilitates global health and ecological research.
Area of Science:
- Genomics
- Bioinformatics
- Molecular Biology
Background:
- Public nucleic acid sequencing data, particularly from the Sequence Read Archive (SRA), offers insights into genetic diversity but faces analysis challenges due to rapid data growth.
- The SRA contains 27.3 million datasets (5 × 10^16 basepairs), representing a vast but underutilized resource for biological discovery.
Purpose of the Study:
- To develop a scalable solution for analyzing the massive Sequence Read Archive (SRA) data.
- To enable efficient, planetary-scale sequence searching and discovery of novel biological entities and functions.
Main Methods:
- Construction of Logan, a massive sequence assembly transforming short reads into long contigs and achieving over 100-fold data compression.
- Development of Logan-Search, a k-mer index of Logan for rapid, petabase-scale sequence similarity searches.
Main Results:
- Identification of over 200 million plastic-degrading enzyme homologs, including novel enzymes with superior catalytic activity.
- Significant expansion of known diversity for proteins (30-fold), plasmids (22-fold), P4 satellites (4.5-fold), and Obelisk RNA elements (3.7-fold).
- Enabled global tracking of antimicrobial resistance genes and characterization of viral reactivation across millions of human BioSamples.
Conclusions:
- Logan transforms the SRA, democratizing access to public genetic data for large-scale analysis.
- The Logan framework opens new frontiers in biotechnology, molecular ecology, and global health research by enabling efficient data mining.
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