Special Issue "Metabolomics–Integration of Technology and Bioinformatics"
Deadline for manuscript submissions: 31 October 2020.
Interests: bioinformatics; data processing; ML and NLP; automation
Interests: mass spec imaging; isotopes; drug metabolism
Metabolomics, by its very nature, is a complex multifaceted discipline. Compounding this complexity has been the dramatic rise in the number of researchers and groups approaching metabolomic technologies with their own unique perspectives, as well as the interest to answer their specific questions or explore their special hypotheses. This increase in population and diversity has prompted an increase in the publications of new methods and software tools.
In this Special Issue of Metabolites, we invite authors to demonstrate their tools and explore the integration of other tools with their technology developments. It is well known that some of the best software integrates many aspects of technology into its design and interface. We feel that this is a feature of some of the most interesting areas of development, and invite developers and users to exhibit these multifaceted developments. We hope to explore a range of different metabolomic technologies in LC–MS, alterative ionization sources, MALDI, and SIMS. Tools that cover later aspects of processing, such as machine learning, but include latent effects of these technology within the model or processing are also encouraged.
Dr. H. Paul Benton
Dr. Michael E. Kurczy
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metabolites is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- technology integration
- stable isotopes
- temporal data
- mass spec imaging
- big data
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: MetaboAnalystR 3.0: integrating parameter optimization, batch correction and functional annotation for high-throughput metabolomics
Author: Jianguo (Jeff) Xia
Abstract：Global metabolomics based on high-resolution MS platform is increasingly applied in metabolomics and multi-omics studies. Easy-to-use, comprehensive and high-performance bioinformatics tools are in urgent demand. Here we introduce MetaboAnalystR 3.0, a significantly improved pipeline to address several key computational bottlenecks facing current global metabolomics. Its key features include: 1) an ultra-fast parameter optimization algorithm for peak picking using XCMS. Our benchmark studies show 20~100X faster (compared to IPO) with more biological meaningful patterns (compared to AutoTuner); 2) seamless integration with well-established algorithms (SVA/ComBat and WaveICA) for batch effect corrections; 3) significantly improved functional analysis (pathways and networks) by upgrading mummichog and the associated knowledgebase, as well as better statistical integration approaches. In summary, MetaboloAnalystR 3.0 offers an efficient pipeline for high-throughput metabolomics in an open-source R environment.
Title: Integrating genomics with metabolomics in clinical diagnostics
Author: Judith Jans
Abstract： Whole exome sequencing (WES), the analysis of the coding part of the genome, is of great value in today's diagnostic process. However, disease-gene discovery by WES is complicated as an individual genome is estimated to harbor about 100 genuine loss-of-function variants with approximately 20 genes completely inactivated. Therefore, additional strategies to identify pathogenic mutations are indispensable.
We developed and validated a bioinformatics pipeline integrating clinical metabolomics data and WES data, with the goal of prioritizing the list of genes harboring variants. The prioritization is based on evidence for functional consequences of each genetic variant. Using data obtained through untargeted metabolomics of dried blood spots from patients with known inborn errors of metabolism (IEM) and connecting that data to protein coding genes of the human genome, we tested and optimized various parameters required for optimal performance. We show that, for accurate prediction of disease-causing genes, it is essential to take into account a relatively large network of metabolites, including metabolites multiple steps away from the primary reaction the gene-product performs. We anticipate that the diagnostic process of known and (yet) unknown IEM may profit from combining data obtained with WES with data obtained with untargeted metabolomics.