Liquid chromatography-mass spectrometry is widely used for comparative replicate sample analysis in proteomics, lipidomics and metabolomics. Before statistical comparison, registration must be established to match corresponding analytes from run to run. Alignment, the most popular correspondence approach, consists of constructing a function that warps the content of runs to most closely match a given reference sample. To date, dozens of correspondence algorithms have been proposed, creating a daunting challenge for practitioners in algorithm selection. Yet, existing reviews have highlighted only a few approaches. In this review, we describe 50 correspondence algorithms to facilitate practical algorithm selection. We elucidate the motivation for correspondence and analyze the limitations of current approaches, which include prohibitive runtimes, numerous user parameters, model limitations and the need for reference samples. We suggest and describe a paradigm shift for overcoming current correspondence limitations by building on known liquid chromatography-mass spectrometry behavior.

UNLABELLED: Countless proteomics data processing algorithms have been proposed, yet few have been critically evaluated due to lack of labeled data (data with known identities and quantities). Although labeling techniques exist, they are limited in terms of confidence and accuracy. In silico simulators have recently been used to create complex data with known identities and quantities. We propose Java Mass Spectrometry Simulator (JAMSS): a fast, self-contained in silico simulator capable of generating simulated MS and LC-MS runs while providing meta information on the provenance of each generated signal. JAMSS improves upon previous in silico simulators in terms of its ease to install, minimal parameters, graphical user interface, multithreading capability, retention time shift model and reproducibility. AVAILABILITY AND IMPLEMENTATION: The simulator creates mzML 1.1.0. It is open source software licensed under the GPLv3. The software and source are available at https://github.com/optimusmoose/JAMSS.

MOTIVATION: Modern lipidomics is largely dependent upon structural ontologies because of the great diversity exhibited in the lipidome, but no automated lipid classification exists to facilitate this partitioning. The size of the putative lipidome far exceeds the number currently classified, despite a decade of work. Automated classification would benefit ongoing classification efforts by decreasing the time needed and increasing the accuracy of classification while providing classifications for mass spectral identification algorithms. RESULTS: We introduce a tool that automates classification into the LIPID MAPS ontology of known lipids with >95% accuracy and novel lipids with 63% accuracy. The classification is based upon simple chemical characteristics and modern machine learning algorithms. The decision trees produced are intelligible and can be used to clarify implicit assumptions about the current LIPID MAPS classification scheme. These characteristics and decision trees are made available to facilitate alternative implementations. We also discovered many hundreds of lipids that are currently misclassified in the LIPID MAPS database, strongly underscoring the need for automated classification. AVAILABILITY AND IMPLEMENTATION: Source code and chemical characteristic lists as SMARTS search strings are available under an open-source license at https://www.github.com/princelab/lipid_classifier.

BACKGROUND: Liquid chromatography-mass spectrometry is a popular technique for high-throughput protein, lipid, and metabolite comparative analysis. Such statistical comparison of millions of data points requires the generation of an inter-run correspondence. Though many techniques for generating this correspondence exist, few if any, address certain well-known run-to-run LC-MS behaviors such as elution order swaps, unbounded retention time swaps, missing data, and significant differences in abundance. Moreover, not all extant correspondence methods leverage the rich discriminating information offered by isotope envelope extraction informed by isotope trace extraction. To date, no attempt has been made to create a formal generalization of extant algorithms for these problems. RESULTS: By enumerating extant objective functions for these problems, we elucidate discrepancies between known LC-MS data behavior and extant approaches. We propose novel objective functions that more closely model known LC-MS behavior. CONCLUSIONS: Through instantiating the proposed objective functions in the form of novel algorithms, practitioners can more accurately capture the known behavior of isotope traces, isotopic envelopes, and replicate LC-MS data, ultimately providing for improved quantitative accuracy.

BACKGROUND: The comparison of analyte mass spectrometry precursor (MS1) signal is central to many proteomic (and other -omic) workflows. Standard vocabularies for mass spectrometry exist and provide good coverage for most experimental applications yet are insufficient for concise and unambiguous description of data concepts spanning the range of signal provenance from a molecular perspective (e.g. from charged peptides down to fine isotopes). Without a standard unambiguous nomenclature, literature searches, algorithm reproducibility and algorithm evaluation for MS-omics data processing are nearly impossible. RESULTS: We show how terms from current official ontologies are too vague or ambiguous to explicitly map molecular entities to MS signals and we illustrate the inconsistency and ambiguity of current colloquially used terms. We also propose a set of terms for MS1 signal that uniquely, succinctly and intuitively describe data concepts spanning the range of signal provenance from full molecule downs to fine isotopes. We suggest that additional community discussion of these terms should precede any further standardization efforts. We propose a novel nomenclature that spans the range of the required granularity to describe MS data processing from the perspective of the molecular provenance of the MS signal. CONCLUSIONS: The proposed nomenclature provides a chain of succinct and unique terms spanning the signal created by a charged molecule down through each of its constituent subsignals. We suggest that additional community discussion of these terms should precede any further standardization efforts.

As the field of bioinformatics research continues to grow, more and more novel techniques are proposed to meet new challenges and improvements upon solutions to long-standing problems. These include data processing techniques and wet lab protocol techniques. Although the literature is consistently thorough in experimental detail and variable-controlling rigor for wet lab protocol techniques, bioinformatics techniques tend to be less described and less controlled. As the validation or rejection of hypotheses rests on the experiment's ability to isolate and measure a variable of interest, we urge the importance of reducing confounding variables in bioinformatics techniques during mass spectrometry experimentation.

MOTIVATION: Isotope trace (IT) detection is a fundamental step for liquid or gas chromatography mass spectrometry (XC-MS) data analysis that faces a multitude of technical challenges on complex samples. The Kalman filter (KF) application to IT detection addresses some of these challenges; it discriminates closely eluting ITs in the m/z dimension, flexibly handles heteroscedastic m/z variances and does not bin the m/z axis. Yet, the behavior of this KF application has not been fully characterized, as no cost-free open-source implementation exists and incomplete evaluation standards for IT detection persist. RESULTS: Massifquant is an open-source solution for KF IT detection that has been subjected to novel and rigorous methods of performance evaluation. The presented evaluation with accompanying annotations and optimization guide sets a new standard for comparative IT detection. Compared with centWave, matchedFilter and MZMine2-alternative IT detection engines-Massifquant detected more true ITs in a real LC-MS complex sample, especially low-intensity ITs. It also offers competitive specificity and equally effective quantitation accuracy. AVAILABILITY AND IMPLEMENTATION: Massifquant is integrated into XCMS with GPL license ≥ 2.0 and hosted by Bioconductor: http://bioconductor.org. Annotation data are archived at http://hdl.lib.byu.edu/1877/3232. Parameter optimization code and documentation is hosted at https://github.com/topherconley/optimize-it.

BACKGROUND: For decades, mass spectrometry data has been analyzed to investigate a wide array of research interests, including disease diagnostics, biological and chemical theory, genomics, and drug development. Progress towards solving any of these disparate problems depends upon overcoming the common challenge of interpreting the large data sets generated. Despite interim successes, many data interpretation problems in mass spectrometry are still challenging. Further, though these challenges are inherently interdisciplinary in nature, the significant domain-specific knowledge gap between disciplines makes interdisciplinary contributions difficult. RESULTS: This paper provides an introduction to the burgeoning field of computational mass spectrometry. We illustrate key concepts, vocabulary, and open problems in MS-omics, as well as provide invaluable resources such as open data sets and key search terms and references. CONCLUSIONS: This paper will facilitate contributions from mathematicians, computer scientists, and statisticians to MS-omics that will fundamentally improve results over existing approaches and inform novel algorithmic solutions to open problems.

MOTIVATION: Quantification of lipids is a primary goal in lipidomics. In direct infusion/injection (or shotgun) lipidomics, accurate downstream identification and quantitation requires accurate summarization of repetitive peak measurements. Imprecise peak summarization multiplies downstream error by propagating into species identification and intensity estimation. To our knowledge, this is the first analysis of direct infusion peak summarization in the literature. RESULTS: We present two novel peak summarization algorithms for direct infusion samples and compare them with an off-machine ad hoc summarization algorithm as well as with the propriety Xcalibur algorithm. Our statistical agglomeration algorithm reduces peakwise error by 38% mass/charge (m/z) and 44% (intensity) compared with the ad hoc method over three datasets. Pointwise error is reduced by 23% (m/z). Compared with Xcalibur, our statistical agglomeration algorithm produces 68% less m/z error and 51% less intensity error on average on two comparable datasets. AVAILABILITY: The source code for Statistical Agglomeration and the datasets used are freely available for non-commercial purposes at https://github.com/optimusmoose/statistical_agglomeration. Modified Bin Aggolmeration is freely available in MSpire, an open source mass spectrometry package at https://github.com/princelab/mspire/.

Abstract Contact: [email protected]

SUMMARY: Quality control in mass spectrometry-based proteomics remains subjective, labor-intensive and inconsistent between laboratories. We introduce Metriculator, a software designed to facilitate long-term storage of extensive performance metrics as introduced by NIST in 2010. Metriculator features a web interface that generates interactive comparison plots for contextual understanding of metric values and an automated metric generation toolkit. The comparison plots are designed for at-a-glance determination of outliers and trends in the datasets, together with relevant statistical comparisons. Easy-to-use quantitative comparisons and a framework for integration plugins will encourage a culture of quality assurance within the proteomics community. AVAILABILITY AND IMPLEMENTATION: Available under the MIT license at http://github.com/princelab/metriculator.

MOTIVATION: Tandem mass spectrometry (MS/MS) offers fast and reliable characterization of complex protein mixtures, but suffers from low sensitivity in protein identification. In a typical shotgun proteomics experiment, it is assumed that all proteins are equally likely to be present. However, there is often other information available, e.g. the probability of a protein's presence is likely to correlate with its mRNA concentration. RESULTS: We develop a Bayesian score that estimates the posterior probability of a protein's presence in the sample given its identification in an MS/MS experiment and its mRNA concentration measured under similar experimental conditions. Our method, MSpresso, substantially increases the number of proteins identified in an MS/MS experiment at the same error rate, e.g. in yeast, MSpresso increases the number of proteins identified by approximately 40%. We apply MSpresso to data from different MS/MS instruments, experimental conditions and organisms (Escherichia coli, human), and predict 19-63% more proteins across the different datasets. MSpresso demonstrates that incorporating prior knowledge of protein presence into shotgun proteomics experiments can substantially improve protein identification scores. AVAILABILITY AND IMPLEMENTATION: Software is available upon request from the authors. Mass spectrometry datasets and supplementary information are available from (http://www.marcottelab.org/MSpresso/).

UNLABELLED: Mass spectrometry-based proteomics stands to gain from additional analysis of its data, but its large, complex datasets make demands on speed and memory usage requiring special consideration from scripting languages. The software library 'mspire'-developed in the Ruby programming language-offers quick and memory-efficient readers for standard xml proteomics formats, converters for intermediate file types in typical proteomics spectral-identification work flows (including the Bioworks .srf format), and modules for the calculation of peptide false identification rates. AVAILABILITY: Freely available at http://mspire.rubyforge.org. Additional data models, usage information, and methods available at http://bioinformatics.icmb.utexas.edu/mspire

MOTIVATION: We reformulate the problem of comparing mass-spectra by mapping spectra to a vector space model. Our search method leverages a metric space indexing algorithm to produce an initial candidate set, which can be followed by any fine ranking scheme. RESULTS: We consider three distance measures integrated into a multi-vantage point index structure. Of these, a semi-metric fuzzy-cosine distance using peptide precursor mass constraints performs the best. The index acts as a coarse, lossless filter with respect to the SEQUEST and ProFound scoring schemes, reducing the number of distance computations and returned candidates for fine filtering to about 0.5% and 0.02% of the database respectively. The fuzzy cosine distance term improves specificity over a peptide precursor mass filter, reducing the number of returned candidates by an order of magnitude. Run time measurements suggest proportional speedups in overall search times. Using an implementation of ProFound's Bayesian score as an example of a fine filter on a test set of Escherichia coli protein fragmentation spectra, the top results of our sample system are consistent with that of SEQUEST.