From Statistical to Biological Interactions via Omics Integration

From Statistical to Biological Interactions via Omics Integration

245 Pages · 2016 · 8.95 MB · English

presentation and teaching skills that I hope will be useful in my future life journey. My PhD already developed bioinformatics workflows such de novo genome assembly tools among others. learning and systems biology to develop omics-based genetic and epistatic statistical networks. The.

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From Statistical to Biological Interactions via Omics Integration Kyrylo Bessonov Promotor: Prof Dr Dr Kristel Van Steen Chair: Prof Dr Pierre Geurts Inte rnal Jury: Prof Dr Vi ncent Bours Prof Dr Patrick Meyer External Jury: Prof Dr Monika Stoll Prof Dr Benno Schwikowsk i Université de Liège Faculté des Sciences Appliquées Département d'Electricit é, Electronique et Informatique Thèse présentée en vue de l'obtention du Grade de Docteur en Sciences de l'Ingénieur Juin 2016 Summary The XXI century opened a new ‘Bi g Data’ era in which, thanks to rapid technological advancements and appearance of high throughput technologies , vast amounts of unstructured omics data (eg, transcriptomic, genomic, etc) are generated every day This thesis mainly focuses on solving the problems related diverse omics data integration and interaction identification tasks Particular attention is given to useful knowledge extraction in the context of complex diseases including pathological mechanisms with the development of software tool s and pipelines The diseases covered included glioblastoma multiforme, asthma, and ankylosing spondylitis Interactions detection in genomic data requires standardization of the protocols In Chapter 3, w e tested the impact of different settings in a gen ome wide association interaction study (GWAIS) Some of the settings includ ed marker selection strategy, the LD pruning, lower order effects adjustment , analytical tool We were able to show that even small changes in each setting can have drastic impacts requiring careful assessment of proper settings and results comparisons across several analysis protocols The greatest impact was attributed to the input dataset composition highlighting the importance of the marker selection strategy and use of prior kno wledge Expression of genes can be affected by nearby ( ‘cis ’) or distant ( ‘trans ’) genotypes Thus, we developed methodology to identify complex

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