MSCAT: A Machine Learning Assisted Catalog of Metabolomics Software Tools

Abstract

The bottleneck for taking full advantage of metabolomics data is often the availability, awareness, and usability of analysis tools. Software tools specifically designed for metabolomics data are being developed at an increasing rate, with hundreds of available tools already in the literature. Many of these tools are open-source and freely available but are very diverse with respect to language, data formats, and stages in the metabolomics pipeline. To help mitigate the challenges of meeting the increasing demand for guidance in choosing analytical tools and coordinating the adoption of best practices for reproducibility, we have designed and built the MSCAT (Metabolomics Software CATalog) database of metabolomics software tools that can be sustainably and continuously updated. This database provides a survey of the landscape of available tools and can assist researchers in their selection of data analysis workflows for metabolomics studies according to their specific needs. We used machine learning (ML) methodology for the purpose of semi-automating the identification of metabolomics software tool names within abstracts. MSCAT searches the literature to find new software tools by implementing a Named Entity Recognition (NER) model based on a neural network model at the sentence level composed of a character-level convolutional neural network (CNN) combined with a bidirectional long-short-term memory (LSTM) layer and a conditional random fields (CRF) layer. The list of potential new tools (and their associated publication) is then forwarded to the database maintainer for the curation of the database entry corresponding to the tool. The end-user interface allows for filtering of tools by multiple characteristics as well as plotting of the aggregate tool data to monitor the metabolomics software landscape.

Mapping Metabolic Signatures with Machine Learning and Deep Learning

“…A recently designed software website: MSCAT (Metabolomics Software CATalog) database of metabolomics software tools, provides an overview of available tools and assists researchers in choosing a data analysis work ow for metabolomics studies according to their speci c needs (Dekermanjian et al, 2021).…”

Opportunities and challenges for sample preparation and enrichment in mass spectrometry for single‐cell metabolomics

“…A wide variety of R packages is openly available for data processing and analysis, providing, in combination with experimental data, a platform for reproducible research [183]. Furthermore, Dekermanjian et al [184] published an ML-generated catalogue of software tools available for the analysis of metabolomics data. A long list of open-source software tools is available for i.a.…”

NMR and Metabolomics—A Roadmap for the Future

“…As discussed below, higher magnetic fields, advances in cryoprobe, microprobe or sub-microprobe technologies, along with novel pulse sequence designs continue to improve the sensitivity of NMR experiments and significantly decrease the lower limits of metabolite detection and quantification [ 30 ]. Additionally, advances in the algorithms and software used for metabolite deconvolution have improved quantification accuracy and have also greatly broadened metabolite coverage within complex biological samples [ 31 , 32 , 33 , 34 , 35 ].…”