Mass spectrometry, a popular technique for elucidating the molecular contents of experimental samples, creates data sets comprised of millions of three-dimensional (m/z, retention time, intensity) data points that correspond to the types and quantities of analyzed molecules. Open and commercial MS data formats are arranged by retention time, creating latency when accessing data across multiple m/z. Existing MS storage and retrieval methods have been developed to overcome the limitations of retention time-based data formats, but do not provide certain features such as dynamic summarization and storage and retrieval of point meta-data (such as signal cluster membership), precluding efficient viewing applications and certain data-processing approaches. This manuscript describes MzTree, a spatial database designed to provide real-time storage and retrieval of dynamically summarized standard and augmented MS data with fast performance in both m/z and RT directions. Performance is reported on real data with comparisons against related published retrieval systems.

BackgroundDespite the ubiquity of mass spectrometry (MS), data processing tools can be surprisingly limited. To date, there is no stand-alone, cross-platform 3-D visualizer for MS data. Available visualization toolkits require large libraries with multiple dependencies and are not well suited for custom MS data processing modules, such as MS storage systems or data processing algorithms.ResultsWe present JS-MS, a 3-D, modular JavaScript client application for viewing MS data. JS-MS provides several advantages over existing MS viewers, such as a dependency-free, browser-based, one click, cross-platform install and better navigation interfaces. The client includes a modular Java backend with a novel streaming.mzML parser to demonstrate the API-based serving of MS data to the viewer.ConclusionsJS-MS enables custom MS data processing and evaluation by providing fast, 3-D visualization using improved navigation without dependencies. JS-MS is publicly available with a GPLv2 license at github.com/optimusmoose/jsms.

Liquid chromatography mass spectrometry is a popular technique for high throughput analysis of biological samples. Identification and quantification of molecular species via mass spectrometry output requires postexperimental computational analysis of the raw instrument output. While tandem mass spectrometry remains a primary method for identification and quantification, species-resolved precursor data provides a rich source of unexploited information. Several algorithms have been proposed to resolve raw precursor signals into species-resolved isotopic envelopes. Many methods are particularly dependent on user parameters, and because they lack a means to optimize parameters, tend to perform poorly. To this end we present XNet, a parameter-less Bayesian machine learning approach to isotopic envelope extraction through the clustering of extracted ion chromatograms. We evaluate the performance of XNet and other prevalent methods on a quantitative ground truth data set. XNet is publicly available with an Apache license.

LC–MS precursor (MS1) data are used increasingly often in conjunction with MS/MS data for the quantification, validation, and other computational mass spectrometry tasks. The efficacy of MS1 data on downstream tasks is dependent on the coverage and accuracy of the MS1 isotopic envelope extraction algorithms that delineate them from the dense backgrounds common in complex samples. Although several algorithms for extracted ion chromatogram (XIC) clustering exist, their performance has not yet been quantified, in part due to the difficulty of obtaining, isolating, and running some algorithms and in part due to the lack of quantitative MS1 ground truth. Using a newly available manually annotated ground truth data set, we measure the performance of several popular XIC clustering algorithms in time, coverage, and accuracy of resulting isotopic envelopes. We intend this work to provide a benchmark against which future algorithms can be scored.