From f4412447bb1228474077c4c0096adbe9c6ce2e11 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?S=C3=B6ren=20Wacker?=
 <3391614+sorenwacker@users.noreply.github.com>
Date: Sat, 7 Dec 2024 13:21:53 +0100
Subject: [PATCH] Update README.md

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 # ms-mint: The Python library for data scientists doing large-scale metabolomics
 The `ms-mint` library is a tool designed to assist with targeted metabolomics studies, which involves the systematic analysis of small chemical compounds called metabolites that are present in biological samples. These metabolites can provide valuable information about the state of an organism, including indicators of disease or other physiological changes. In order to perform a targeted metabolomics study, researchers typically use liquid chromatography-mass spectrometry (LCMS) to identify and quantify specific metabolites of interest.
 
-The `ms-mint` library includes a range of functions for processing LCMS data from targeted metabolomics experiments, and it is particularly well-suited for handling large amounts of data (10,000+ files). To use `ms-mint`, you provide it with a target list of the specific metabolites you want to analyze, as well as the names of the mass spectrometry files containing the data. ms-mint then extracts peak intensities and other relevant information from the data, allowing you to gain insights into the concentrations and profiles of the metabolites in your samples. This information can be used to identify biomarkers, which are indicators of disease or other physiological changes that can be used in the development of diagnostic tests or other medical applications.
+The `ms-mint` library includes a range of functions for processing LCMS data from targeted metabolomics experiments, and it is particularly well-suited for handling **large amounts of data (10,000+ files)**. To use `ms-mint`, you provide it with a target list of the specific metabolites you want to analyze, as well as the names of the mass spectrometry files containing the data. ms-mint then extracts peak intensities and other relevant information from the data, allowing you to gain insights into the concentrations and profiles of the metabolites in your samples. This information can be used to identify biomarkers, which are indicators of disease or other physiological changes that can be used in the development of diagnostic tests or other medical applications.
 
 ## News
 Starting with version 1.0.0, we have updated the installation setup to use pyproject.toml. Additionally, each release of the repository will now be assigned a DOI to facilitate citation of the software.