We are happy to report that we have released our new software **NeatMS**
Available automated methods for peak detection in untargeted metabolomics suffer from poor precision. We present NeatMS which uses machine learning to replace peak curation by human experts. We show how to integrate our open source module into different LC-MS analysis workflows and quantify its performance. NeatMS is designed to be suitable for large scale studies and improves the robustness of the final peak list.
NeatMS is open-source and is freely available at https://github.com/bihealth/NeatMS under permissive MIT license. A pypi package is available at https://pypi.org/project/NeatMS/, a Bioconda package is available at https://anaconda.org/bioconda/neatms. The user documentation can be found at https://neatms.readthedocs.io/en/latest/.
Further details can be found in the preprint
Last modified: Mar 21, 2024