BLANKA: an Algorithm for Blank Subtraction in Mass Spectrometry of Complex Biological Samples

Global Natural Product Social Molecular Networking (GNPS) is an interactive online small molecule-focused tandem mass spectrometry (MS 2 ) data curation and analysis infrastructure. It is intended to provide as much chemical insight as possible into an untargeted MS 2 dataset and to connect this chemical insight to the user's underlying biological questions. This can be performed within one liquid chromatography (LC)-MS 2 experiment or at the repository scale. GNPS-MassIVE is a public data repository for untargeted MS 2 data with sample information (metadata) and annotated MS 2 spectra. These publicly accessible data can be annotated and updated with the GNPS infrastructure keeping a continuous record of all changes. This knowledge is disseminated across all public data; it is a living dataset. Molecular networking-one of the main analysis tools used within the GNPS platform-creates a structured data table that reflects the molecular diversity captured in tandem mass spectrometry experiments by computing the relationships of the MS 2 spectra as spectral similarity. This protocol provides step-by-step instructions for creating reproducible, high-quality molecular networks. For training purposes, the reader is led through a 90-to 120-min procedure that starts by recalling an example public dataset and its sample information and proceeds to creating and interpreting a molecular network. Each data analysis job can be shared or cloned to disseminate the knowledge gained, thus propagating information that can lead to the discovery of molecules, metabolic pathways, and ecosystem/community interactions.

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“…6p). However, it is possible to remove the ions from experimental conditions before generating a molecular network by data processing 119 .…”

Section: Protocol Nature Protocolsmentioning

confidence: 99%

Reproducible molecular networking of untargeted mass spectrometry data using GNPS

et al. 2020

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“…For all extractions, all precursor m/z's that were found in solvent and agar controls (based on both retention time and mass tolerance) were removed prior to input into GNPS using the BLANKA algorithm. 101 102,103 . The molecular networks were visualized using Cytoscape software 104 .…”

Section: Molecular Networkingmentioning

confidence: 99%

Bacterial–fungal interactions revealed by genome-wide analysis of bacterial mutant fitness

et al. 2020

Self Cite

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Microbial interactions are major determinants in shaping microbiome structure and function. Although fungi are found across diverse microbiomes, the mechanisms through which fungi interact with other species remain largely uncharacterized. In this work, we explore the diversity of ways in which fungi can impact bacteria by characterizing interaction mechanisms across 16 different bacterial-fungal pairs, involving 8 different fungi and 2 bacteria (Escherichia coli and Pseudomonas psychrophila). Using random barcode transposon-site sequencing (RB-TnSeq), we identified a large number of bacterial genes and pathways important in fungal interaction contexts. Within each interaction, fungal partners elicit both antagonistic and beneficial effects. Using a panel of phylogenetically diverse fungi allowed us to identify interactions that were conserved across all species. Our data show that all fungi modulate the availability of iron and biotin, suggesting that these may represent conserved bacterial-fungal interactions. Several fungi also appear to produce previously uncharacterized antibiotic compounds. Generating a mutant in a master regulator of fungal secondary metabolite production showed that fungal metabolites are key shapers of bacterial fitness profiles during interactions. This work demonstrates a diversity of mechanisms through which fungi are able to interact with bacterial species. In addition to many species-specific effects, there appear to be conserved interaction mechanisms which may be important across microbiomes. INTRODUCTION .

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“…Separation using peak width is a quick method to classify spectra, but more accurate methods could be applied in an automated pipeline. Multiple algorithms (Cleary et al, 2019; Ho, Kuo, Wang, Chen, & Tseng, 2013; Zhang & Yang, 2008; Zhu et al, 2009) have been developed for the purpose of subtracting the background from LC‐MS data. In addition, a hierarchical cluster analysis technique was developed to identify chemical interferants that are not removable by background subtraction (Caesar, Kvalheim, & Cech, 2018).…”

Section: Resultsmentioning

confidence: 99%

Creation and filtering of a recurrent spectral library of CHO cell metabolites and media components

Telu

Marupaka

Simón‐Manso

et al. 2021

Biotech & Bioengineering

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This paper reports the first implementation of a new type of mass spectral library for the analysis of Chinese hamster ovary (CHO) cell metabolites that allows users to quickly identify most compounds in any complex metabolite sample. We also describe an annotation methodology developed to filter out artifacts and low‐quality spectra from recurrent unidentified spectra of metabolites. CHO cells are commonly used to produce biological therapeutics. Metabolic profiles of CHO cells and media can be used to monitor process variability and look for markers that discriminate between batches of product. We have created a comprehensive library of both identified and unidentified metabolites derived from CHO cells that can be used in conjunction with tandem mass spectrometry to identify metabolites. In addition, we present a workflow that can be used for assigning confidence to a NIST MS/MS Library search match based on prior probability of general utility. The goal of our work is to annotate and identify (when possible), all liquid chromatography‐mass spectrometry generated metabolite ions as well as create automatable library building and identification pipelines for use by others in the field.

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BLANKA: an Algorithm for Blank Subtraction in Mass Spectrometry of Complex Biological Samples

Cited by 14 publications

References 28 publications

Reproducible molecular networking of untargeted mass spectrometry data using GNPS

Reproducible molecular networking of untargeted mass spectrometry data using GNPS

Bacterial–fungal interactions revealed by genome-wide analysis of bacterial mutant fitness

Creation and filtering of a recurrent spectral library of CHO cell metabolites and media components

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