Rapid advances in sequencing technologies have enabled the collection of vast quantities of omics data. The coupling of omics technologies on a single sample provides a much more realistic view of the biological phenotype and offers the opportunity to enhance our molecular understanding of biological systems in healthy and diseased states.
However, the integration and interpretation of these diverse, large-scale datasets present a significant bottleneck to these studies. Everything from carefully thought-out study design to the utilisation of advanced computational tools must be considered to make use of these datasets and address novel questions.
Join us for Multi-Omics ONLINE, a virtual three-part series, where we delve into the technologies and approaches fuelling the next era of omics analyses.
Webinar 1: Multi-omics technologies and study design
Tuesday 18th January, 3pm GMT/ 4pm CET/ 10am EST
The coupling of different omics-technologies and carefully thought-out study design influences the downstream analysis processes. This webinar will highlight case studies that demonstrate how you can gain unparalleled insights using novel approaches to analyse these complex datasets.
Talks include:
Integration strategies of multi-omics data for machine learning analysis – Milan Picard, Doctoral Student, CHU de Quebec, Universite Laval
A user-friendly multi-omics data harmonisation R pipeline – Tyrone Chen, PhD Student, Monash University
10x Genomics Chromium: Solutions for the analysis of multimodal features in single cells – Hannes Arnold, Senior Science & Technology Advisor, 10x Genomics
Plus Q&A with our speakers
Webinar 2: Data integration and interpretation to unveil novel insights
Tuesday 25th January, 3pm GMT/ 4pm CET/ 10am EST
One of the most significant challenges of multi-omics analyses is the integration and interpretation of this data, requiring computational and complex statistical methods to gain insights. This webinar will focus on methods of integrating and interpreting diverse datasets, with case studies of how this is advancing biological research.
Talks include:
Signature Regulatory Clustering (SiRCle) model to understand cancer regulation through semi-supervised integration – Christina Schmidt, PhD Student, University of Cambridge
Therapy-induced evolution of human lung cancer revealed by single cell RNA sequencing– Wei Wu, Full Professional Researcher, University of San Francisco
Integrating multi-omics to disentangle host-microbiota interactions– Maria Zimmermann-Kogadeeva, Group Leader, EMBL-EBI
MetaboAnalyst 4.0 for metabolomics data analysis, interpretation, and integration with other omics data – Jianguo (Jeff) Xia, Assistant Professor Institute of Parasitology and Department of Animal Science, McGill University
Plus Q&A with our speakers
Webinar 3: Multi-omics in drug discovery and precision medicine
Tuesday 1st February, 3pm GMT/ 4pm CET/ 10am EST
The study of multiple analytes in tandem provides the opportunity to identify novel drug targets or can be applied to monitor system changes that will provide insights into precision medicine approaches. This webinar will focus on case studies of how multi-omics is uncovering the changes that can influence medicine and drug development.
Talks include:
Multi-omics integration identifies a novel proliferative drive in chronic lymphocytic leukemia– Junyan Lu, Research Staff Scientist, Machine Learning Specialist, EMBL-EBI
The identification of phenotype-specific networks from paired gene expression-cell shape imaging data – Evangelia Petsalaki, Research Group Leader, EMBL-EBI
Plus Q&A with our speakers
Speakers
Milan Picard, Doctoral Student, CHU de Quebec, Universite Laval
Tyrone Chen, PhD Student, Monash University
Hannes Arnold, Senior Science & Technology Advisor, 10x Genomics
Christina Schmidt, PhD Student, University of Cambridge
Wei Wu, Full Professional Researcher, University of San Francisco
Maria Zimmermann-Kogadeeva, Group Leader, EMBL-EBI
Jianguo (Jeff) Xia, Assistant Professor, Institute of Parasitology and Department of Animal Science, McGill University
Junyan Lu, Research Staff Scientist, Machine Learning Specialist, EMBL-EBI
Evangelia Petsalaki, Research Group Leader, EMBL-EBI
