OmixAnalyzer – A Web-Based System for Management and Analysis of High-Throughput Omics Data Sets

Stoltmann, Thomas; Zimmermann, Karin; Koschmieder, André; Leser, Ulf

doi:10.1007/978-3-642-39437-9_4

Cited by 2 publications

(1 citation statement)

References 16 publications

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“…We also build on our own long-standing experience in working with data from the area of systems biology and systems medicine. This includes both technical expertise in integration of such data and its storage in relational databases and information systems (e.g., [ 32 – 35 ]), and practical application of such data in molecular oncology - and the insight of which information our own treatment decisions are influenced by.…”

Section: Methodsmentioning

confidence: 99%

Variant information systems for precision oncology

Starlinger

Pallarz

Ševa

et al. 2018

BMC Med Inform Decis Mak

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BackgroundThe decreasing cost of obtaining high-quality calls of genomic variants and the increasing availability of clinically relevant data on such variants are important drivers for personalized oncology. To allow rational genome-based decisions in diagnosis and treatment, clinicians need intuitive access to up-to-date and comprehensive variant information, encompassing, for instance, prevalence in populations and diseases, functional impact at the molecular level, associations to druggable targets, or results from clinical trials. In practice, collecting such comprehensive information on genomic variants is difficult since the underlying data is dispersed over a multitude of distributed, heterogeneous, sometimes conflicting, and quickly evolving data sources. To work efficiently, clinicians require powerful Variant Information Systems (VIS) which automatically collect and aggregate available evidences from such data sources without suppressing existing uncertainty.MethodsWe address the most important cornerstones of modeling a VIS: We take from emerging community standards regarding the necessary breadth of variant information and procedures for their clinical assessment, long standing experience in implementing biomedical databases and information systems, our own clinical record of diagnosis and treatment of cancer patients based on molecular profiles, and extensive literature review to derive a set of design principles along which we develop a relational data model for variant level data. In addition, we characterize a number of public variant data sources, and describe a data integration pipeline to integrate their data into a VIS.ResultsWe provide a number of contributions that are fundamental to the design and implementation of a comprehensive, operational VIS. In particular, we (a) present a relational data model to accurately reflect data extracted from public databases relevant for clinical variant interpretation, (b) introduce a fault tolerant and performant integration pipeline for public variant data sources, and (c) offer recommendations regarding a number of intricate challenges encountered when integrating variant data for clincal interpretation.ConclusionThe analysis of requirements for representation of variant level data in an operational data model, together with the implementation-ready relational data model presented here, and the instructional description of methods to acquire comprehensive information to fill it, are an important step towards variant information systems for genomic medicine.Electronic supplementary materialThe online version of this article (10.1186/s12911-018-0665-z) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

Variant information systems for precision oncology

Starlinger

Pallarz

Ševa

et al. 2018

BMC Med Inform Decis Mak

Self Cite

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Review of Statistical Learning Methods in Integrated Omics Studies (An Integrated Information Science)

Zeng

Lumley

2018

Bioinform Biol Insights

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Integrated omics is becoming a new channel for investigating the complex molecular system in modern biological science and sets a foundation for systematic learning for precision medicine. The statistical/machine learning methods that have emerged in the past decade for integrated omics are not only innovative but also multidisciplinary with integrated knowledge in biology, medicine, statistics, machine learning, and artificial intelligence. Here, we review the nontrivial classes of learning methods from the statistical aspects and streamline these learning methods within the statistical learning framework. The intriguing findings from the review are that the methods used are generalizable to other disciplines with complex systematic structure, and the integrated omics is part of an integrated information science which has collated and integrated different types of information for inferences and decision making. We review the statistical learning methods of exploratory and supervised learning from 42 publications. We also discuss the strengths and limitations of the extended principal component analysis, cluster analysis, network analysis, and regression methods. Statistical techniques such as penalization for sparsity induction when there are fewer observations than the number of features and using Bayesian approach when there are prior knowledge to be integrated are also included in the commentary. For the completeness of the review, a table of currently available software and packages from 23 publications for omics are summarized in the appendix.

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OmixAnalyzer – A Web-Based System for Management and Analysis of High-Throughput Omics Data Sets

Cited by 2 publications

References 16 publications

Variant information systems for precision oncology

Variant information systems for precision oncology

Review of Statistical Learning Methods in Integrated Omics Studies (An Integrated Information Science)

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