MS and NMR Analysis of Isotopically Labeled Chloramination Disinfection Byproducts: Hyperlinks and Chemical Reactions.

FT-ICR MS and NMR analysis of an isotopically labeled complex mixture of water disinfection byproducts formed by chloramine disinfection of model phenolic acids is described. A new molecular formula assignment procedure using the CoreMS Python library able to assign isotopically enriched formulas is proposed. Statistical analysis of the assigned formulas showed that the number of compounds, the diversity of the mixture, and the chlorine count increase during the chloramination reaction. The complex reaction mixture was investigated as a network of reactions using PageRank and Reverse PageRank algorithms. Independent of the MS signal intensities, the PageRank algorithm calculates the formulas with the highest probability at convergence of the reaction; these were chlorinated and nitrated derivatives of the starting materials. The Reverse PageRank revealed that the most probable chemical transformations in the complex mixture were chlorination and decarboxylation. These agree with the data obtained from INADEQUATE NMR spectra and literature data, indicating that this approach could be applied to gain insight into reactions pathways taking place in complex mixtures without any prior knowledge.

PyKrev: A Python Library for the Analysis of Complex Mixture FT-MS Data.

In this paper, we present PyKrev, a Python library for the analysis of complex mixture Fourier transform mass spectrometry (FT-MS) data. PyKrev is a comprehensive suite of tools for analysis and visualization of FT-MS data after formula assignment has been performed. These comprise formula manipulation and calculation of chemical properties, intersection analysis between multiple lists of formulas, calculation of chemical diversity, assignment of compound classes to formulas, multivariate analysis, and a variety of visualization tools producing van Krevelen diagrams, class histograms, PCA score, and loading plots, biplots, scree plots, and UpSet plots. The library is showcased through analysis of hot water green tea extracts and Scotch whisky FT-ion cyclotron resonance-MS data sets. PyKrev addresses the lack of a single, cohesive toolset for researchers to perform FT-MS analysis in the Python programming environment encompassing the most recent data analysis techniques used in the field.

The Response of Microbial Communities to Peatland Drainage and Rewetting. A Review.

Peatlands are significant global carbon stores and play an important role in mediating the flux of greenhouse gasses into the atmosphere. During the 20th century substantial areas of northern peatlands were drained to repurpose the land for industrial or agricultural use. Drained peatlands have dysfunctional microbial communities, which can lead to net carbon emissions. Rewetting of drained peatlands is therefore an environmental priority, yet our understanding of the effects of peatland drainage and rewetting on microbial communities is still incomplete. Here we summarize the last decade of research into the response of the wider microbial community, methane-cycling microorganisms, and micro-fauna to drainage and rewetting in fens and bogs in Europe and North America. Emphasis is placed on current research methodologies and their limitations. We propose targets for future work including: accounting for timescale of drainage and rewetting events; better vertical and lateral coverage of samples across a peatland; the integration of proteomic and metabolomic datasets into functional community analysis; the use of RNA sequencing to differentiate the active community from legacy DNA; and further study into the response of the viral and micro-faunal communities to peatland drainage and rewetting. This review should benefit researchers embarking on studies in wetland microbiology and non-microbiologists working on peatland drainage and rewetting in general.

VHost-Classifier: virus-host classification using natural language processing.

MOTIVATION: When analyzing viral metagenomic sequences, it is often desired to filter the results of a BLAST analysis by the host species of the virus. VHost-Classifier automates this procedure using a natural language processing algorithm written in Python 3, which takes a list of taxonomic identifiers (taxids) returned from a BLAST query using viral sequences as input. The taxid output is binned by the evolutionary lineage of their host, based on string matching the words in their English names. If VHost-Classifier cannot identify a host, it attempts to bin the sequences by the environment from which the sample originated. VHost-Classifier predicts the evolutionary lineage of the host from the virus name and does not rely on referencing taxids against a database; therefore, it is not constrained by the size of a database and can host classify newly characterized viruses. RESULTS: Benchmarked on a test dataset of 1000 randomly selected viral taxids on the NCBI taxonomy database, VHost-Classifier assigned, with 100% accuracy, a host to the rank of Class for >93% of viruses, and to the rank of Family for >37% of viruses. AVAILABILITY AND IMPLEMENTATION: For more information about VHost-Classifier as well as implementation instructions, visit https://github.com/Kzra/VHost-Classifier. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.