Magnetstein: An Open-Source Tool for Quantitative NMR Mixture Analysis Robust to Low Resolution, Distorted Lineshapes, and Peak Shifts.

1H NMR spectroscopy is a powerful tool for analyzing mixtures including determining the concentrations of individual components. When signals from multiple compounds overlap, this task requires computational solutions. They are typically based on peak-picking and the comparison of obtained peak lists with libraries of individual components. This can fail if peaks are not sufficiently resolved or when peak positions differ between the library and the mixture. In this paper, we present Magnetstein, a quantification algorithm rooted in the optimal transport theory that makes it robust to unexpected frequency shifts and overlapping signals. Thanks to this, Magnetstein can quantitatively analyze difficult spectra with the estimation trueness an order of magnitude higher than that of commercial tools. Furthermore, the method is easier to use than other approaches, having only two parameters with default values applicable to a broad range of experiments and requiring little to no preprocessing of the spectra.

Trapalyzer: a computer program for quantitative analyses in fluorescent live-imaging studies of neutrophil extracellular trap formation.

Neutrophil extracellular traps (NETs), pathogen-ensnaring structures formed by neutrophils by expelling their DNA into the environment, are believed to play an important role in immunity and autoimmune diseases. In recent years, a growing attention has been put into developing software tools to quantify NETs in fluorescent microscopy images. However, current solutions require large, manually-prepared training data sets, are difficult to use for users without background in computer science, or have limited capabilities. To overcome these problems, we developed Trapalyzer, a computer program for automatic quantification of NETs. Trapalyzer analyzes fluorescent microscopy images of samples double-stained with a cell-permeable and a cell-impermeable dye, such as the popular combination of Hoechst 33342 and SYTOX™ Green. The program is designed with emphasis on software ergonomy and accompanied with step-by-step tutorials to make its use easy and intuitive. The installation and configuration of the software takes less than half an hour for an untrained user. In addition to NETs, Trapalyzer detects, classifies and counts neutrophils at different stages of NET formation, allowing for gaining a greater insight into this process. It is the first tool that makes this possible without large training data sets. At the same time, it attains a precision of classification on par with state-of-the-art machine learning algorithms. As an example application, we show how to use Trapalyzer to study NET release in a neutrophil-bacteria co-culture. Here, after configuration, Trapalyzer processed 121 images and detected and classified 16 000 ROIs in approximately three minutes on a personal computer. The software and usage tutorials are available at https://github.com/Czaki/Trapalyzer.

Harvest time affects antioxidant capacity, total polyphenol and flavonoid content of Polish St John's wort's (Hypericum perforatum L.) flowers.

The polyphenol content and antioxidant capacity of hyperforin and hypericin-standardized H. perforatum L. extracts may vary due to the harvest time. In this work, ethanol and ethanol-water extracts of air-dried and lyophilized flowers of H. perforatum L., collected throughout a vegetation season in central Poland, were studied. Air-dried flowers extracts had higher polyphenol (371 mg GAE/g) and flavonoid (160 mg CAE/g) content, DPPH radical scavenging (1672 mg DPPH/g), ORAC (5214 µmol TE/g) and FRAP (2.54 mmol Fe2+/g) than lyophilized flowers extracts (238 mg GAE/g, 107 mg CAE/g, 1287 mg DPPH/g, 3313 µmol TE/g and 0.31 mmol Fe2+/g, respectively). Principal component analysis showed that the collection date influenced the flavonoid and polyphenol contents and FRAP of ethanol extracts, and DPPH and ORAC values of ethanol-water extracts. The ethanol extracts with the highest polyphenol and flavonoid content protected human erythrocytes against bisphenol A-induced damage. Both high field and benchtop NMR spectra of selected extracts, revealed differences in composition caused by extraction solvent and raw material collection date. Moreover, we have shown that benchtop NMR can be used to detect the compositional variation of extracts if the assignment of signals is done previously.

Masserstein: Linear regression of mass spectra by optimal transport.

RATIONALE: The linear regression of mass spectra is a computational problem defined as fitting a linear combination of reference spectra to an experimental one. It is typically used to estimate the relative quantities of selected ions. In this work, we study this problem in an abstract setting to develop new approaches applicable to a diverse range of experiments. METHODS: To overcome the sensitivity of the ordinary least-squares regression to measurement inaccuracies, we base our methods on a non-conventional spectral dissimilarity measure, known as the Wasserstein or the Earth Mover's distance. This distance is based on the notion of the cost of transporting signal between mass spectra, which renders it naturally robust to measurement inaccuracies in the mass domain. RESULTS: Using a data set of 200 mass spectra, we show that our approach is capable of estimating ion proportions accurately without extensive preprocessing of spectra required by other methods. The conclusions are further substantiated using data sets simulated in a way that mimics most of the measurement inaccuracies occurring in real experiments. CONCLUSIONS: We have developed a linear regression algorithm based on the notion of the cost of transporting signal between spectra. Our implementation is available in a Python 3 package called masserstein, which is freely available at https://github.com/mciach/masserstein.

Locus-aware decomposition of gene trees with respect to polytomous species trees.

BACKGROUND: Horizontal gene transfer (HGT), a process of acquisition and fixation of foreign genetic material, is an important biological phenomenon. Several approaches to HGT inference have been proposed. However, most of them either rely on approximate, non-phylogenetic methods or on the tree reconciliation, which is computationally intensive and sensitive to parameter values. RESULTS: We investigate the locus tree inference problem as a possible alternative that combines the advantages of both approaches. We present several algorithms to solve the problem in the parsimony framework. We introduce a novel tree mapping, which allows us to obtain a heuristic solution to the problems of locus tree inference and duplication classification. CONCLUSIONS: Our approach allows for faster comparisons of gene and species trees and improves known algorithms for duplication inference in the presence of polytomies in the species trees. We have implemented our algorithms in a software tool available at https://github.com/mciach/LocusTreeInference.

Estimation of Rates of Reactions Triggered by Electron Transfer in Top-Down Mass Spectrometry.

Electron transfer dissociation (ETD) is a versatile technique used in mass spectrometry for the high-throughput characterization of proteins. It consists of several concurrent reactions triggered by the transfer of an electron from its anion source to sample cations. Transferring an electron causes peptide backbone cleavage while leaving labile post-translational modifications intact. The obtained fragmentation spectra provide valuable information for sequence and structure analyses. In this study, we propose a formal mathematical model of the ETD fragmentation process in the form of a system of stochastic differential equations describing its joint dynamics. Parameters of the model correspond to the rates of occurring reactions. Their estimates for various experimental settings give insight into the dynamics of the ETD process. We estimate the model parameters from the relative quantities of fragmentation products in a given mass spectrum by solving a nonlinear optimization problem. The cost function penalizes for the differences between the analytically derived average number of reaction products and their experimental counterparts. The presented method proves highly robust to noise in silico. Moreover, the model can explain a considerable amount of experimental results for a wide range of instrumentation settings. The implementation of the presented workflow, code-named ETDetective, is freely available under the two-clause BSD license.