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Fix formatting in the summary section of paper.md #156

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stephwon merged 1 commit into from
Apr 26, 2026
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Fix formatting in the summary section of paper.md #156

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2 changes: 1 addition & 1 deletion joss/paper.md
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Expand Up @@ -58,7 +58,7 @@ bibliography: paper.bib

# Summary

Identifying genomes in metagenomics samples can be complicated by taxonomic profiling tools that lack uncertainty quantification and rely on incomplete reference databases. YACHT (**Y**es/No **A**nswers to **C**ommunity membership via **H**ypothesis **T**esting) introduces a $k$-mer sketching based statistical framework that incorporates average nucleotide identity (ANI) and coverage, the portion of $k$-mers observed for a microbe’s genome detected in a sample, to detect genetic similarity between reference and sample genomes using binomial hypothesis testing on exclusive $k$-mers to confidently determine genome presence/absence [@koslicki2024yacht]. This paper describes the software implementation of this methodology as a command-line tool that detects low-abundant species while controlling the false-negative rate, making it applicable to functional profiling, metatranscriptomics, and clinical microbiome analysis despite incomplete genomes and variable coverage. YACHT is developed with C++ and Python and depends on `sourmash` [@irber2024sourmash] for $k$-mer extraction and management.
Identifying genomes in metagenomic samples can be complicated by taxonomic profiling tools that lack uncertainty quantification and rely on incomplete reference databases. YACHT (**Y**es/No **A**nswers to **C**ommunity membership via **H**ypothesis **T**esting) introduces a $k$-mer sketching based statistical framework that incorporates average nucleotide identity (ANI) and coverage, the portion of $k$-mers observed for a microbe’s genome detected in a sample, to detect genetic similarity between reference and sample genomes using binomial hypothesis testing on exclusive $k$-mers to confidently determine genome presence/absence [@koslicki2024yacht]. This paper describes the software implementation of this methodology as a command-line tool that detects low-abundant species while controlling the false-negative rate, making it applicable to functional profiling, metatranscriptomics, and clinical microbiome analysis despite incomplete genomes and variable coverage. YACHT is developed with C++ and Python and depends on `sourmash` [@irber2024sourmash] for $k$-mer extraction and management.

# Statement of need

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