YACHT: an ANI-based statistical test to detect microbial presence/absence in a metagenomic sample

Area of Science:

• Computational Biology
• Metagenomics
• Bioinformatics

Background:

• Metagenomics involves analyzing microbial communities from environmental DNA.
• A key task is identifying reference genomes within a sample metagenome.
• Current tools lack confidence intervals, complicating interpretation, especially for low-abundance microbes.

Purpose of the Study:

• To introduce YACHT (Yes/No Answers to Community membership via Hypothesis Testing), a novel algorithm for metagenomic analysis.
• To provide a statistical framework for determining genome presence/absence in metagenomic samples.
• To address limitations of existing tools regarding confidence estimates and incomplete reference databases.

Main Methods:

• Developed YACHT, a hypothesis testing framework for metagenomic community membership.
• Incorporated Average Nucleotide Identity (ANI) to account for sequence divergence.
• Modeled incomplete sequencing depth to improve presence/absence calls.

Main Results:

• YACHT provides a statistical framework for genome presence/absence detection in metagenomes.
• The algorithm accounts for sequence divergence and sequencing depth.
• Experiments with simulated and real data demonstrate YACHT's accuracy and scalability.

Conclusions:

• YACHT offers a statistically robust method for metagenomic genome identification.
• The approach improves upon existing tools by providing uncertainty quantification.
• YACHT is available as open-source software for the research community.