Generating Peptide Mass Spectrometry Ground Truth Data

Very few quantitative evaluations exist for precursor mass spectrometry data due to the lack of tools for enabling the manual feature finding necessary to generate this data. Other lacks the ability to capture, edit, save, and view precursor mass spectrometry data. We present JS-MS 2.0, a software suite that provides a dependency-free, browser-based, one click, cross-platform solution for creating precursor ground truth. The software retains the first version’s capacity for loading, viewing, and navigating MS1 data in 2- and 3-D, and adds tools for capturing, editing, saving and viewing isotopic envelope and extracted isotopic chromatogram features. The software can also be used to view and explore the results of feature finding algorithms. JS-MS 2.0 enables faster creation and inspection of precursor mass spectrometry ground truth data. It is publicly available with a GPL 2.0 license at github.com/optimusmoose/jsms

XFlow: An algorithm for extracting ion chromatograms

Abstract: Mass spectrometry is a fundamental tool for modern proteomics. The increasing availability of mass spectrometry data paired with the increasing sensitivity and fidelity of the instruments necessitates new and more potent analytical methods. To that end, we have created and present XFlow, a feature detection algorithm for extracting ion chromatograms from MS1 LC-MS data. XFlow is a parameter-free procedurally agnostic feature detection algorithm that utilizes the latent properties of ion chromatograms to resolve them from the surrounding noise present in MS1 data. XFlow is designed to function on either profile or centroided data across different resolutions and instruments. This broad applicability lends XFlow strong utility as a one-size-fits-all method for MS1 analysis or target acquisition for MS2. XFlow is written in Java and packaged with JS-MS, an open-source mass spectrometry analysis toolkit

A Review and Evaluation of Techniques for Improved Feature Detection in Mass Spectrometry Data

Mass spectrometry (MS) is used in analysis of chemical samples to identify the molecules present and their quantities. This analytical technique has applications in many fields, from pharmacology to space exploration. Its impacts on medicine are particularly significant, since MS aids in the identification of molecules associated with disease; for instance, in proteomics, MS allows researchers to identify proteins that are associated with autoimmune disorders, cancers, and other conditions. Since the applications are so wide-ranging and the tool is ubiquitous across so many fields, it is critical that the analytical methods used to collect data are sound. Data analysis in MS is challenging. Experiments produce massive amounts of raw data that need to be processed algorithmically in order to generate interpretable results in a process known as feature detection, which is tasked with distinguishing signals associated with the chemical sample being analyzed from signals associated with background noise. These experimentally meaningful signals are also known as features or extracted ion chromatograms (XIC) and are the fundamental signal unit in mass spectrometry. There are many algorithms for analyzing raw mass spectrometry data tasked with distinguishing real isotopic signals from noise. While one or more of the available algorithms are typically chained together for end-to-end mass spectrometry analysis, analysis of each algorithm in isolation provides a specific measurement of the strengths and weaknesses of each algorithm without the confounding effects that can occur when multiple algorithmic tasks are chained together. Though qualitative opinions on extraction algorithm performance abound, quantitative performance has never been publicly ascertained. Quantitative evaluation has not occurred partly due to the lack of an available quantitative ground truth MS1 data set. Because XIC must be distinguished from noise, quality algorithms for this purpose are essential. Background noise is introduced through the mobile phase of the chemical matrix in which the sample of interest is introduced to the MS instrument, and as a result, MS data is full of signals representing low-abundance molecules (i.e. low-intensity signals). Noise generally presents in one of two ways: very low-intensity signals that comprise a majority of the data from an MS experiment, and noise features that are moderately low-intensity and can resemble signals from low-abundance molecules deriving from the actual sample of interest. Like XIC algorithms, noise reduction algorithms have yet to be quantitatively evaluated, to our knowledge; the performance of these algorithms is generally evaluated through consensus with other noise reduction algorithms. Using a recently published, manually-extracted XIC dataset as ground truth data, we evaluate the quality of popular XIC algorithms, including MaxQuant, MZMine2, and several methods from XCMS. XIC algorithms were applied to the manually extracted data using a grid search of possible parameters. Performance varied greatly between different parameter settings, though nearly all algorithms with parameter settings optimized with respect to the number of true positives recovered over 10,000 XIC. We also examine two popular algorithms for reducing background noise, the COmponent Detection Algorithm (CODA) and adaptive iteratively reweighted Penalized Least Squares (airPLS), and compare their performance to the results of feature detection alone using algorithms that achieved the best performance in a previous evaluation. Due to weaknesses inherent in the implementation of these algorithms, both noise reduction algorithms eliminate data identified by feature detection as significant

Quantitative analysis of mass spectrometry proteomics data : Software for improved life science

The rapid advances in life science, including the sequencing of the human genome and numerous other techiques, has given an extraordinary ability to aquire data on biological systems and human disease. Even so, drug development costs are higher than ever, while the rate of new approved treatments is historically low. A potential explanation to this discrepancy might be the difficulty of understanding the biology underlying the acquired data; the difficulty to refine the data to useful knowledge through interpretation. In this thesis the refinement of the complex data from mass spectrometry proteomics is studied. A number of new algorithms and programs are presented and demonstrated to provide increased analytical ability over previously suggested alternatives. With the higher goal of increasing the mass spectrometry laboratory scientific output, pragmatic studies were also performed, to create new set on compression algorithms for reduced storage requirement of mass spectrometry data, and also to characterize instrument stability. The final components of this thesis are the discussion of the technical and instrumental weaknesses associated with the currently employed mass spectrometry proteomics methodology, and the discussion of current lacking academical software quality and the reasons thereof. As a whole, the primary algorithms, the enabling technology, and the weakness discussions all aim to improve the current capability to perform mass spectrometry proteomics. As this technology is crucial to understand the main functional components of biology, proteins, this quest should allow better and higher quality life science data, and ultimately increase the chances of developing new treatments or diagnostics

Ανάπτυξη και εφαρμογή μεθοδολογίας ανάλυσης LC-HRMS για τη μελέτη δειγμάτων ανθρωπίνων κοπράνων και σύγκριση μεταξύ υγιών εθελοντών και ασθενών ατόμων με Φλεγμονώδη Νόσο του Εντέρου (ΦΝΕ) μέσω μεταβολομικών προσεγγίσεων

Τα τελευταία χρόνια τα δείγματα κοπράνων θεωρούνται πολύτιμη επιλογή έρευνας στο πεδίο των βιοεπιστημών αφού μπορούν να συλλεχθούν μη επεμβατικά, σε μεγάλες ποσότητες και να προσφέρουν πολύτιμες πληροφορίες για τις πολύπλοκες αλληλεπιδράσεις μεταξύ της μικροβιακής χλωρίδας και του ατόμου (ξενιστή). Στις μέρες μας είναι πλέον ευρέως γνωστό πως η διαταραχή της ισορροπίας στους πληθυσμούς της εντερικής μικροχλωρίδας συνδέεται με την εμφάνιση πλήθους δυσλειτουργιών και παθήσεων. Ιδιαίτερο ενδιαφέρον σε όλο αυτό το πεδίο παρουσιάζει και η μελέτη της σύστασης των κοπράνων καθώς και η συσχέτιση αυτής με τη διατροφή. Η διερεύνηση των επιπτώσεων που επιφέρουν στην ανθρώπινη υγεία οι διατροφικές συνήθειες και κατ' επέκταση τα φυσικά προϊόντα, αποκαλύπτει ενδιαφέροντα ευρήματα. Στη παρούσα μελέτη σαράντα δείγματα ανθρωπίνων κοπράνων, είκοσι υγιών εθελοντών και είκοσι ασθενών ατόμων με Φλεγμονώδη Νόσο του Εντέρου (ΦΝΕ), μελετήθηκαν. Όσον αφορά τα Φλεγμονώδη Νοσήματα του Εντέρου, οι έρευνες έχουν δείξει ότι τα νοσήματα αυτά σχετίζονται με διαταραχές του ανοσοποιητικού συστήματος που προστατεύει τον οργανισμό από λοιμώξεις, ιούς και μικρόβια. Σήμερα, ιδιαίτερα στον ανεπτυγμένο κόσμο, οι τάσεις εμφάνισης τέτοιων νοσημάτων είναι αυξητικές. Σκοπό της μελέτης αυτής αποτέλεσε η ανάπτυξη κατάλληλης μεθοδολογίας για την ανάλυση δειγμάτων ανθρωπίνων κοπράνων με την τεχνική της υγρής χρωματογραφίας υπερυψηλής απόδοσης συζευγμένη με φασματομετρία μάζας υψηλής διακριτικής ικανότητας (UHPLC-ΗRMS). Επιπλέον, σκοπό αποτέλεσε η σύγκριση μεταξύ υγιών εθελοντών και ασθενών με ΦΝΕ μέσω μεταβολομικής μελέτης. Πιο αναλυτικά, πραγματοποιήθηκε έρευνα για την επιλογή ενός κατάλληλου συστήματος εκχύλισης, με γνώμονα την απόκτηση ενός γενικού και αντιπροσωπευτικού μεταβολικού αποτυπώματος των δειγμάτων κοπράνων. Επίσης, επιχειρήθηκε ανάπτυξη αλλά και επικύρωση κατάλληλης μεθόδου ανάλυσης βασισμένη στη τεχνική UHPLC-ΗRMS. Στο κομμάτι της μεταβολομικής μελέτης, στόχος ήταν η ανεύρεση πιθανών συσχετίσεων μεταξύ των υγιών εθελοντών και των ασθενών με ΦΝΕ αλλά και η ανεύρεση πιθανών μορίων-βιοδεικτών.In recent years, stool specimens have been considered as a valuable research option in the field of life sciences since they can be collected non-invasively in large quantities and provide valuable information on the complex interactions between the microbial flora and the host. Nowadays, it is widely known that the disorder of equilibrium in the intestinal microflora population is associated with the occurrence of a variety of malfunctions and diseases. Of particular interest in this field is the study of faecal composition as well as its correlation with nutrition. Exploring the effects of eating habits and, by extension, natural products on human health, are revealing interesting findings. In the present study, forty samples of human faeces, twenty of healthy volunteers and twenty of patients with Inflammatory Bowel Disease (IBD) were studied. Recent findings has shown that these diseases are related to disorders of the immune system that protect the body from infections, viruses and microbes. Today, especially in the developed world, trends in such diseases are increasing. The purpose of this study was to develop a suitable methodology for the analysis of human stool specimens by ultra high performance liquid chromatography coupled with high resolution mass spectrometry (UHPLC-HRMS). In addition, the aim was to compare healthy volunteers and patients with IBD through a metabolomic study. More specifically, research has been carried out to select a suitable extraction system in order to obtain a general and representative metabolic fingerprint of stool specimens. Also, it was attempted to develop and validate an analytical method based on the UHPLC-HRMS technique. In the part of the metabolomic study, the aim was to find possible correlations between healthy volunteers and patients with IBD, as well as possible biomarkers