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Multi-omic Data Integration


Stable, predictive biomarkers and interpretable disease signatures are seen as a significant step towards personalized medicine. In this perspective, integration of multi-omic data coming from genomics, transcriptomics, glycomics, proteomics, metabolomics is a powerful strategy to reconstruct and analyse complex multi-dimensional interactions, enabling deeper mechanistic and medical insight. At the same time, there is a rising concern that much of such different omic data –although often publicly and freely available- lie in databases and repositories underutilised or not used at all. Issues coming from lack of standardisation and shared biological identities are also well-known. From these considerations, a novel, pressing request arises from the life sciences to design methodologies and approaches that allow for these data to be interpreted as a whole, i.e. as intertwined molecular signatures containing genes, proteins, mRNAs and miRNAs, able to capture inter-layers connections and complexity. This book collects papers discussing data integration approaches and methods of several types and extents, their application in understanding the pathogenesis of specific diseases or in identifying candidate biomarkers to exploit the full benefit of multi-omic datasets and their intrinsic information content.

Table of Contents

05 - Editorial: Multi-omic data integration

Christine Nardini, Jennifer Dent and Paolo Tieri

07 - Multi-omics analysis identifies genes mediating the extension of cell walls inthe Arabidopsis thaliana root elongation zone

Michael H. Wilson, Tara J. Holman, Iben Sørensen, Ester Cancho-Sanchez, DarrenM. Wells, Ranjan Swarup, J. Paul Knox, William G. T. Willats, Susana Ubeda-Tomás,Michael Holdsworth, Malcolm J. Bennett, Kris Vissenberg and T. Charlie Hodgman

19 - Comparative transcriptomics and metabolomics in a rhesus macaque drug administration study

Kevin J. Lee, Weiwei Yin, Dalia Arafat, Yan Tang, Karan Uppal, ViLinh Tran, MonicaCabrera-Mora, Stacey Lapp, Alberto Moreno, Esmeralda Meyer, Jeremy D. DeBarry,Suman Pakala, Vishal Nayak, Jessica C. Kissinger, Dean P. Jones, Mary Galinski,Mark P. Styczynski and Greg Gibson

38 - Multi-omic landscape of rheumatoid arthritis: re-evaluation of drug adverse effects

Paolo Tieri, XiaoYuan Zhou, Lisha Zhu and Christine Nardini

48 - From molecular signatures to predictive biomarkers: modeling disease pathophysiology and drug mechanism of action

Andreas Heinzel, Paul Perco, Gert Mayer, Rainer Oberbauer, Arno Lukas and Bernd Mayer

59 - How to build personalized multi-omics comorbidity profiles

Mohammad Ali Moni and Pietro Liò

78 - Fingerprints of a message: integrating positional information on thetranscriptome

Erik Dassi and Alessandro Quattrone

85 - Integrating multi-omic features exploiting Chromosome Conformation Capture data

Ivan Merelli, Fabio Tordini, Maurizio Drocco, Marco Aldinucci, Pietro Liò and Luciano Milanesi

96 - Computational modeling of heterogeneity and function of CD4+ T cells

Adria Carbo, Raquel Hontecillas, Tricity Andrew, Kristin Eden, Yongguo Mei, Stefan Hoops and Josep Bassaganya-Riera

107 - Understanding gene regulatory mechanisms by integrating ChIP-seq andRNA-seq data: statistical solutions to biological problems 

Claudia Angelini and Valerio Costa

115 - Integrative workflows for metagenomic analysis

Efthymios Ladoukakis, Fragiskos N. Kolisis and Aristotelis A. Chatziioannou

126 - Integrative analysis of multiple diverse omics datasets by sparse group multitask regression

Dongdong Lin, Jigang Zhang, Jingyao Li, Hao He, Hong-Wen Deng andYu-Ping Wang