Quantitative multi-omic data analysis and integration with the COSMOS R package Aurelien Dugourd,Katharina Zirngibl,Attila Gabor Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Heidelberg University, Heidelberg, Germany Abstract Multi-omic data sets are a combination of snapshots of different molecular layers of the cell. While they can give comprehensive information into the biological system under study, their analysis and interpretation remains challenging. Each omic layer can be exploited to give specific insights, such as transcription factor activity form transcriptomics and kinase activity from phosphoproteomics (using Omnipath(Türei, Korcsmáros, and Saez-Rodriguez 2016) and dorothea(Garcia-Alonso et al.
POMA: User-friendly Workflow for Metabolomics and Proteomics Data Analysis Pol Castellano Escuder,Cristina Andrés-Lacueva,Alex Sánchez-Pla University of Barcelona Abstract Mass spectrometry, like other high-throughput technologies, usually faces a data mining challenge to provide an understandable output to advance in biomarker discovery and precision medicine. Often, statistical analysis is one of the hard points and it’s critical in the subsequent biological interpretation of the results. Due to this fact combined with the computational programming skills needed for this type of analysis, several bioinformatic tools have emerged to simplify mass spectrometry data analysis.
Short talk: margheRita: an R package for analyzing the entire workflow of mass spectrometry-based metabolic profiles.
margheRita: an R package for analyzing the entire workflow of mass spectrometry-based metabolic profiles. Maria Ulaszewska,Edoardo Bellini,Denise Drago,Valeria Mannella,Marco Morelli,Annapaola Andolfo,Ettore Mosca National Research Council, Institute of Biomedical Technologies, Segrate (Milan), Italy Abstract Mass spectrometry-based metabolic profiling in circulating biofluids and tissues is a promising approach to identify biomarkers for disease prediction, progression and prognosis. Besides, metabolomics is a powerful methodology, which can be applied to personalized medicine for drug development or for biomarker discovery in public healthcare.
Short talk: Introducing idpr: A Package for Profiling and Analyzing Intrinsically Disordered Proteins in R.
Introducing idpr: A Package for Profiling and Analyzing Intrinsically Disordered Proteins in R. William Michael McFadden,Judith L. Yanowitz Magee-Womens Research Institute, Department of Obstetrics, Gynecology, and Reproductive Sciences, University of Pittsburgh School of Medicine, PA 15213, USA Abstract Intrinsically Disordered Proteins (IDPs) are proteins or protein-domains that do not have a single native structure but rather are dynamic peptides that can adopt multiple conformations. The field of “unstructured biology” has gained prominence over the last few decades as it has become apparent that proteins experiencing intrinsic disorder comprise substantial portions of proteomes.