From 072c3243870f161ba9f0d8c7c8e9e334f64b05e2 Mon Sep 17 00:00:00 2001
From: Stephanie Won <64761042+stephwon@users.noreply.github.com>
Date: Sun, 26 Apr 2026 14:27:17 -0400
Subject: [PATCH] Fix formatting in the summary section of paper.md

metagenomics samples should be "metagenomic samples"
---
 joss/paper.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/joss/paper.md b/joss/paper.md
index a0be6c55..dea95785 100644
--- a/joss/paper.md
+++ b/joss/paper.md
@@ -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