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Clinical Bioinformatics for Biomarker Discovery in Targeted Metabolomics

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Application of Clinical Bioinformatics

Part of the book series: Translational Bioinformatics ((TRBIO,volume 11))

Abstract

In this chapter, methods of clinical bioinformatics in targeted metabolomics are discussed, with an emphasis on the discovery of metabolic biomarkers. The reader is introduced to general aspects such as initiatives in metabolomics standardization, regulatory guidelines and software validation, and is presented an overview of the bioinformatics workflow in metabolomics. Engineering-based concepts of clinical bioinformatics in supporting the storage and automated analysis of samples, the integration of data in public repositories, and in the management of data using metabolomics application software are discussed. Chemometrics algorithms for data processing are summarized, modalities of biostatistics and data analysis presented, as well as data mining and machine learning approaches, aiming at the discovery of biomarkers in targeted metabolomics. Methods of data interpretation in the context of annotated biochemical pathways are suggested, theoretical concepts of metabolic modeling and engineering are introduced, and the in-silico modeling and simulation of molecular processes is briefly touched. Finally, a short outlook on future perspectives in the application of clinical bioinformatics in targeted metabolomics is given, e.g. on the development of integrated mass spectrometry solutions, ready for routine clinical usage in laboratory medicine, or on the application of concepts of artificial intelligence in laboratory automation – liquid handling robots, autonomously performing experiments and generating hypotheses.

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Abbreviations

ACToR:

Aggregated Computational Toxicology Resource

ANN:

artificial neural network

ANOVA:

analysis-of-variance

ArMet:

Architecture for a Metabolomics Experiment

ATP:

adenosine triphosphate

BBMRI:

Biobanking and Biomolecular Resources Research Infrastructure

BGI:

Beijing Genomics Institute

ChEBI:

Chemical Entities of Biological Interest

COSMOS:

COordination Of Standards In MetabOlomicS

CO2 :

carbon dioxide

CV:

coefficient of variation

C2:

acetylcarnitine

C5:

valerylcarnitine

DBSCAN:

density-based spatial clustering of applications with noise

DRCC:

Data Repository and Coordination Centre

ELIXIR:

European life-sciences Infrastructure for biological Information

EMA:

European Medicines Agency

EPA:

Environmental Protection Agency

FDA:

Food and Drug Administration

GAMP:

Good Automated Manufacturing Practice

GWAS:

genome-wide association studies

HMDB:

Human Metabolome Database

H2O:

water

ICH:

International Conference on Harmonization

ISA:

Investigation Study Assay

ISO:

International Organization of Standardization

ISPE:

International Society for Pharmaceutical Engineering

JDAMP:

Joint Committee on Atomic and Molecular Physical Data

KDD:

knowledge discovery in databases

KEGG:

Kyoto Encyclopedia of Genes and Genomes

LIMS:

laboratory information management systems

LLOQ:

lower limit of quantitation

MBRole:

Metabolite Biological Role

MeMo:

Metabolic Modeling

MFC:

maximum fold change

MIBBI:

Minimum Information for Biological and Biomedical investigations

MIAMET:

Minimum Information About METabolomics experiments

MS:

mass spectrometry

MSEA:

Metabolite Set Enrichment Analysis

MSI:

Metabolomics Standards Initiative

NADH:

nicotinamide adenine dinucleotide

netCDF:

Network Common Data Format

NH3 :

ammonia

ODE:

ordinary differential equation

OMIM:

Online Mendelian Inheritance in Man

ORA:

overrepresentation analysis

PCA:

principal component analysis

PLS:

partial least squares

PRIMe:

Platform for RIKEN Metabolomics

QC:

quality control

RCMRC:

Regional Comprehensive Metabolomics Research Cores

RF:

random forest

SFR:

stacked feature ranking

SMPDB:

Small Molecule Pathway Database

SOP:

Standard Operating Procedure

SSP:

single sample profiling

SVM:

support vector machines

T3DB:

Toxin Target/Target Database

XML:

eXtensible Markup Language.

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Authors and Affiliations

  1. Research Group for Clinical Bioinformatics, Institute of Electrical and Biomedical Engineering (IEBE), University for Health Sciences, Medical Informatics and Technology (UMIT), 6060, Hall in Tirol, Austria

    Marc Breit, Michael Netzer & Klaus M. Weinberger

  2. Institute of Health Care Engineering with European Notified Body of Medical Devices, Graz University of Technology, Stremayrgasse 16, A-8010, Graz, Austria

    Marc Breit & Christian Baumgartner Ph.D.

  3. sAnalytiCo Ltd., Forsyth House, Cromac Square, Belfast, BT2 8LA, UK

    Klaus M. Weinberger

  4. Weinberger & Weinberger Life Sciences Consulting, Weidach 82, 6414, Mieming, Austria

    Klaus M. Weinberger

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  1. Marc Breit
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  2. Christian Baumgartner Ph.D.
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  3. Michael Netzer
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  4. Klaus M. Weinberger
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Correspondence to Marc Breit .

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Editors and Affiliations

  1. Shanghai Inst.of Clinical Bioinformatics, Zhongshan Hospital, Fudan University, Shang Hai, China

    Xiangdong Wang

  2. Institute of Health Care Engineering, Graz University of Technology, Graz, Austria

    Christian Baumgartner

  3. School Of Medicine & Medical Science, University College Dublin, Dublin 4, Ireland

    Denis C. Shields

  4. Health and Tropical Medicine, Tulane University School of Public Healt, New Orleans, Louisiana, USA

    Hong-Wen Deng

  5. Section of Clinical Bioinformatics, Swiss Institute of Bioinformatics, Switzerland

    Jacques S Beckmann

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Breit, M., Baumgartner, C., Netzer, M., Weinberger, K.M. (2016). Clinical Bioinformatics for Biomarker Discovery in Targeted Metabolomics. In: Wang, X., Baumgartner, C., Shields, D., Deng, HW., Beckmann, J. (eds) Application of Clinical Bioinformatics. Translational Bioinformatics, vol 11. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-7543-4_8

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