Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine

Despite the tremendous success, pitfalls have been observed in every step of a clinical metabolomics workflow, which impedes the internal validity of the study. Furthermore, the demand for logistics, instrumentations, and computational resources for metabolic phenotyping studies has far exceeded our expectations. In this conceptual review, we will cover inclusive barriers of a metabolomics-based clinical study and suggest potential solutions in the hope of enhancing study robustness, usability, and transferability. The importance of quality assurance and quality control procedures is discussed, followed by a practical rule containing five phases, including two additional "pre-pre-" and "post-post-" analytical steps. Besides, we will elucidate the potential involvement of machine learning and demonstrate that the need for automated data mining algorithms to improve the quality of future research is undeniable. Consequently, we propose a comprehensive metabolomics framework, along with an appropriate checklist refined from current guidelines and our previously published assessment, in the attempt to accurately translate achievements in metabolomics into clinical and epidemiological research. Furthermore, the integration of multifaceted multi-omics approaches with metabolomics as the pillar member is in urgent need. When combining with other social or nutritional factors, we can gather complete omics profiles for a particular disease. Our discussion reflects the current obstacles and potential solutions toward the progressing trend of utilizing metabolomics in clinical research to create the next-generation healthcare system.11Ysciescopu

A machine learning approach to explore the spectra intensity pattern of peptides using tandem mass spectrometry data

Background: A better understanding of the mechanisms involved in gas-phase fragmentation of peptides is essential for the development of more reliable algorithms for high-throughput protein identification using mass spectrometry (MS). Current methodologies depend predominantly on the use of derived m/z values of fragment ions, and, the knowledge provided by the intensity information present in MS/MS spectra has not been fully exploited. Indeed spectrum intensity information is very rarely utilized in the algorithms currently in use for high-throughput protein identification. Results: In this work, a Bayesian neural network approach is employed to analyze ion intensity information present in 13878 different MS/MS spectra. The influence of a library of 35 features on peptide fragmentation is examined under different proton mobility conditions. Useful rules involved in peptide fragmentation are found and subsets of features which have significant influence on fragmentation pathway of peptides are characterised. An intensity model is built based on the selected features and the model can make an accurate prediction of the intensity patterns for given MS/MS spectra. The predictions include not only the mean values of spectra intensity but also the variances that can be used to tolerate noises and system biases within experimental MS/MS spectra. Conclusion: The intensity patterns of fragmentation spectra are informative and can be used to analyze the influence of various characteristics of fragmented peptides on their fragmentation pathway. The features with significant influence can be used in turn to predict spectra intensities. Such information can help develop more reliable algorithms for peptide and protein identification

Sparse Proteomics Analysis - A compressed sensing-based approach for feature selection and classification of high-dimensional proteomics mass spectrometry data

Background: High-throughput proteomics techniques, such as mass spectrometry (MS)-based approaches, produce very high-dimensional data-sets. In a clinical setting one is often interested in how mass spectra differ between patients of different classes, for example spectra from healthy patients vs. spectra from patients having a particular disease. Machine learning algorithms are needed to (a) identify these discriminating features and (b) classify unknown spectra based on this feature set. Since the acquired data is usually noisy, the algorithms should be robust against noise and outliers, while the identified feature set should be as small as possible. Results: We present a new algorithm, Sparse Proteomics Analysis (SPA), based on the theory of compressed sensing that allows us to identify a minimal discriminating set of features from mass spectrometry data-sets. We show (1) how our method performs on artificial and real-world data-sets, (2) that its performance is competitive with standard (and widely used) algorithms for analyzing proteomics data, and (3) that it is robust against random and systematic noise. We further demonstrate the applicability of our algorithm to two previously published clinical data-sets

Enhanced label-free discovery proteomics through improved data analysis and knowledge enrichment

Mass spectrometry (MS)-based proteomics has evolved into an important tool applied in fundamental biological research as well as biomedicine and medical research. The rapid developments of technology have required the establishment of data processing algorithms, protocols and workflows. The successful application of such software tools allows for the maturation of instrumental raw data into biological and medical knowledge. However, as the choice of algorithms is vast, the selection of suitable processing tools for various data types and research questions is not trivial. In this thesis, MS data processing related to the label-free technology is systematically considered. Essential questions, such as normalization, choice of preprocessing software, missing values and imputation, are reviewed in-depth. Considerations related to preprocessing of the raw data are complemented with exploration of methods for analyzing the processed data into practical knowledge. In particular, longitudinal differential expression is reviewed in detail, and a novel approach well-suited for noisy longitudinal high-througput data with missing values is suggested. Knowledge enrichment through integrated functional enrichment and network analysis is introduced for intuitive and information-rich delivery of the results. Effective visualization of such integrated networks enables the fast screening of results for the most promising candidates (e.g. clusters of co-expressing proteins with disease-related functions) for further validation and research. Finally, conclusions related to the prepreprocessing of the raw data are combined with considerations regarding longitudinal differential expression and integrated knowledge enrichment into guidelines for a potential label-free discovery proteomics workflow. Such proposed data processing workflow with practical suggestions for each distinct step, can act as a basis for transforming the label-free raw MS data into applicable knowledge.Massaspektrometriaan (MS) pohjautuva proteomiikka on kehittynyt tehokkaaksi työkaluksi, jota hyödynnetään niin biologisessa kuin lääketieteellisessäkin tutkimuksessa. Alan nopea kehitys on synnyttänyt erikoistuneita algoritmeja, protokollia ja ohjelmistoja datan käsittelyä varten. Näiden ohjelmistotyökalujen oikeaoppinen käyttö lopulta mahdollistaa datan tehokkaan esikäsittelyn, analysoinnin ja jatkojalostuksen biologiseksi tai lääketieteelliseksi ymmärrykseksi. Mahdollisten vaihtoehtojen suuresta määrästä johtuen sopivan ohjelmistotyökalun valinta ei usein kuitenkaan ole yksiselitteistä ja ongelmatonta. Tässä väitöskirjassa tarkastellaan leimaamattomaan proteomiikkaan liittyviä laskennallisia työkaluja. Väitöskirjassa käydään läpi keskeisiä kysymyksiä datan normalisoinnista sopivan esikäsittelyohjelmiston valintaan ja puuttuvien arvojen käsittelyyn. Datan esikäsittelyn lisäksi tarkastellaan datan tilastollista jatkoanalysointia sekä erityisesti erilaisen ekspression havaitsemista pitkittäistutkimuksissa. Väitöskirjassa esitellään uusi, kohinaiselle ja puuttuvia arvoja sisältävälle suurikapasiteetti-pitkittäismittausdatalle soveltuva menetelmä erilaisen ekspression havaitsemiseksi. Uuden tilastollisen menetelmän lisäksi väitöskirjassa tarkastellaan havaittujen tilastollisten löydösten rikastusta käytännön ymmärrykseksi integroitujen rikastumis- ja verkkoanalyysien kautta. Tällaisten funktionaalisten verkkojen tehokas visualisointi mahdollistaa keskeisten tulosten nopean tulkinnan ja kiinnostavimpien löydösten valinnan jatkotutkimuksia varten. Lopuksi datan esikäsittelyyn ja pitkittäistutkimusten tilastollisen jatkokäsittelyyn liittyvät johtopäätökset yhdistetään tiedollisen rikastamisen kanssa. Näihin pohdintoihin perustuen esitellään mahdollinen työnkulku leimaamattoman MS proteomiikkadatan käsittelylle raakadatasta hyödynnettäviksi löydöksiksi sekä edelleen käytännön biologiseksi ja lääketieteelliseksi ymmärrykseksi

Statistical Workflow for Feature Selection in Human Metabolomics Data.

High-throughput metabolomics investigations, when conducted in large human cohorts, represent a potentially powerful tool for elucidating the biochemical diversity underlying human health and disease. Large-scale metabolomics data sources, generated using either targeted or nontargeted platforms, are becoming more common. Appropriate statistical analysis of these complex high-dimensional data will be critical for extracting meaningful results from such large-scale human metabolomics studies. Therefore, we consider the statistical analytical approaches that have been employed in prior human metabolomics studies. Based on the lessons learned and collective experience to date in the field, we offer a step-by-step framework for pursuing statistical analyses of cohort-based human metabolomics data, with a focus on feature selection. We discuss the range of options and approaches that may be employed at each stage of data management, analysis, and interpretation and offer guidance on the analytical decisions that need to be considered over the course of implementing a data analysis workflow. Certain pervasive analytical challenges facing the field warrant ongoing focused research. Addressing these challenges, particularly those related to analyzing human metabolomics data, will allow for more standardization of as well as advances in how research in the field is practiced. In turn, such major analytical advances will lead to substantial improvements in the overall contributions of human metabolomics investigations

Liquid Chromatography Mass Spectrometry-Based Proteomics: Biological and Technological Aspects

Mass spectrometry-based proteomics has become the tool of choice for identifying and quantifying the proteome of an organism. Though recent years have seen a tremendous improvement in instrument performance and the computational tools used, significant challenges remain, and there are many opportunities for statisticians to make important contributions. In the most widely used "bottom-up" approach to proteomics, complex mixtures of proteins are first subjected to enzymatic cleavage, the resulting peptide products are separated based on chemical or physical properties and analyzed using a mass spectrometer. The two fundamental challenges in the analysis of bottom-up MS-based proteomics are as follows: (1) Identifying the proteins that are present in a sample, and (2) Quantifying the abundance levels of the identified proteins. Both of these challenges require knowledge of the biological and technological context that gives rise to observed data, as well as the application of sound statistical principles for estimation and inference. We present an overview of bottom-up proteomics and outline the key statistical issues that arise in protein identification and quantification.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS341 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Data analysis tools for mass spectrometry proteomics

ABSTRACT Proteins are large biomolecules which consist of amino acid chains. They differ from one another in their amino acid sequences, which are mainly dictated by the nucleotide sequence of their corresponding genes. Proteins fold into specific threedimensional structures that determine their activity. Because many of the proteins act as catalytes in biochemical reactions, they are considered as the executive molecules in the cells and therefore their research is fundamental in biotechnology and medicine. Currently the most common method to investigate the activity, interactions, and functions of proteins on a large scale, is high-throughput mass spectrometry (MS). The mass spectrometers are used for measuring the molecule masses, or more specifically, their mass-to-charge ratios. Typically the proteins are digested into peptides and their masses are measured by mass spectrometry. The masses are matched against known sequences to acquire peptide identifications, and subsequently, the proteins from which the peptides were originated are quantified. The data that are gathered from these experiments contain a lot of noise, leading to loss of relevant information and even to wrong conclusions. The noise can be related, for example, to differences in the sample preparation or to technical limitations of the analysis equipment. In addition, assumptions regarding the data might be wrong or the chosen statistical methods might not be suitable. Taken together, these can lead to irreproducible results. Developing algorithms and computational tools to overcome the underlying issues is of most importance. Thus, this work aims to develop new computational tools to address these problems. In this PhD Thesis, the performance of existing label-free proteomics methods are evaluated and new statistical data analysis methods are proposed. The tested methods include several widely used normalization methods, which are thoroughly evaluated using multiple gold standard datasets. Various statistical methods for differential expression analysis are also evaluated. Furthermore, new methods to calculate differential expression statistic are developed and their superior performance compared to the existing methods is shown using a wide set of metrics. The tools are published as open source software packages.TIIVISTELMÄ Proteiinit ovat aminohappoketjuista muodostuvia isoja biomolekyylejä. Ne eroavat toisistaan aminohappojen järjestyksen osalta, mikä pääosin määräytyy proteiineja koodaavien geenien perusteella. Lisäksi proteiinit laskostuvat kolmiulotteisiksi rakenteiksi, jotka osaltaan määrittelevät niiden toimintaa. Koska proteiinit toimivat katalyytteinä biokemiallisissa reaktioissa, niillä katsotaan olevan keskeinen rooli soluissa ja siksi myös niiden tutkimusta pidetään tärkeänä. Tällä hetkellä yleisin menetelmä laajamittaiseen proteiinien aktiivisuuden, interaktioiden sekä funktioiden tutkimiseen on suurikapasiteettinen massaspektrometria (MS). Massaspektrometreja käytetään mittaamaan molekyylien massoja – tai tarkemmin massan ja varauksen suhdetta. Tyypillisesti proteiinit hajotetaan peptideiksi massojen mittausta varten. Massaspektrometrillä havaittuja massoja verrataan tunnetuista proteiinisekvensseistä koottua tietokantaa vasten, jotta peptidit voidaan tunnistaa. Peptidien myötä myös proteiinit on mahdollista päätellä ja kvantitoida. Kokeissa kerätty data sisältää normaalisti runsaasti kohinaa, joka saattaa johtaa olennaisen tiedon hukkumiseen ja jopa pahimmillaan johtaa vääriin johtopäätöksiin. Tämä kohina voi johtua esimerkiksi näytteen käsittelystä johtuvista eroista tai mittalaitteiden teknisistä rajoitteista. Lisäksi olettamukset datan luonteesta saattavat olla virheellisiä tai käytetään datalle soveltumattomia tilastollisia malleja. Pahimmillaan tämä johtaa tilanteisiin, joissa tutkimuksen tuloksia ei pystytä toistamaan. Erilaisten laskennallisten työkalujen sekä algoritmien kehittäminen näiden ongelmien ehkäisemiseksi onkin ensiarvoisen tärkeää tutkimusten luotettavuuden kannalta. Tässä työssä keskitytäänkin sovelluksiin, joilla pyritään ratkaisemaan tällä osa-alueella ilmeneviä ongelmia. Tutkimuksessa vertaillaan yleisesti käytössä olevia kvantitatiivisen proteomiikan ohjelmistoja ja yleisimpiä datan normalisointimenetelmiä, sekä kehitetään uusia datan analysointityökaluja. Menetelmien keskinäiset vertailut suoritetaan useiden sellaisten standardiaineistojen kanssa, joiden todellinen sisältö tiedetään. Tutkimuksessa vertaillaan lisäksi joukko tilastollisia menetelmiä näytteiden välisten erojen havaitsemiseen sekä kehitetään kokonaan uusia tehokkaita menetelmiä ja osoitetaan niiden parempi suorituskyky suhteessa aikaisempiin menetelmiin. Kaikki tutkimuksessa kehitetyt työkalut on julkaistu avoimen lähdekoodin sovelluksina