The Multiomics Playbook: 4th Edition

We are pleased to announce the launch of the latest edition of the Multiomics Playbook, which offers a contributor-led exploration of experimental platforms and data integration strategies, exemplified by case studies across drug discovery, precision medicine, and digital biological simulations.

Report Contributors include:

• Slavé Petrovski, Vice President, AstraZeneca Centre for Genomics Research
• Gonçalo Abecasis, Vice President, Chief Genomics and Data Science Officer, Regeneron
• Mangal Prakash, Senior Principal Scientist, Johnson & Johnson Innovative Medicine
• Xu Zhang, Programme Manager, National Cancer Institute, NIH
• Arpita Kulkarni, Director, Functional Genomics & Bioinformatics Core, Harvard Medical School
• Peng Gao, Assistant Professor, Harvard T.H. Chan School of Public Health
• Lindsay Caesar, Assistant Professor, James Madison University
• Fabiola Curion, Postdoctoral Researcher, Helmholtz Center Munich
• Nadav Yayon, Senior Post-Doc (Teichmann Group), Wellcome Sanger Institute 
• Manik Garg, Senior Data Scientist, AstraZeneca Centre for Genomics Research
• Luisa Solis Soto, Associate Professor, MD Anderson Cancer Center
• Kimberly Glass, Assistant Professor of Medicine, Brigham and Women's Hospital
• Sandra Safo, Associate Professor, University of Minnesota
• Andreas Wicki, Director, Comprehensive Cancer Center Zurich

Contents:

Chapter 1: Combining protein and nucleic acids

Biological processes are grounded in the Central Dogma, each layer offering unique insights. Bringing these modalities together builds a clearer picture of cellular biology, while generating a wealth of data for future investigation. Here, we explore a selection of commercial and open-source platforms that combine protein and nucleic acid detection, detailing their benefits and limitations, while providing recent use cases. The crucial role of the wet laboratory scientist is also discussed as we move into this digital era.

Chapter 2: Spatial multiomics 

Multiomics analysis has shown great promise for investigating tissue, allowing the interrogation of multiple modalities that are complemented by morphological and spatial context. This chapter discusses the types of single omes that can be combined in spatial studies, considering both commercial and open-source applications. Digital pathology is highly compatible with spatial multiomics; histology is routinely performed, and we explore how this morphological information can bolster spatial studies and insights. 

Chapter 3: Beyond the central dogma 

The applications of multiomics research extend far beyond the Central Dogma. Multimodal techniques are being used to map the secretome, interactome and hologenome, amongst others. This chapter explores the possibilities of multiomics research at a systems biology level, from how endophenotype research is bridging scales between molecules and phenotypes, to how multimodal analysis of the exposome can shed light on the biological impact of an individual’s environment. 

Chapter 4: Multimodal integration and analysis 

One of the key challenges facing multiomics research is the integration of data. A wealth of commercial and open-source solutions exists, with many offering machine learning capabilities to draw insights from seemingly disparate data. Here, you will discover novel publications discussing the key challenges facing data integration, alongside interviews and insights from the authors giving clarity and expert advice. Topics include data heterogeneity, the curse of dimensionality and solutions for those without bioinformatics expertise. 

Chapter 5: Applications for precision medicine 

Multiomics is revolutionising the field of precision medicine. From bolstering drug discovery and development, to improving patient diagnosis and stratification, multimodal testing is paving the way for personalised medicine and holistic healthcare. This chapter investigates several case studies that exemplify the use of multiomics in precision medicine, such as the development of personalised vaccines and the use of electronic health records in multimodal platforms. 

Chapter 6: Applications for the digital era 

The ever-increasing abundance of multimodal data provides extensive opportunities for the discovery of novel insights through mining and reanalysis. From atlases to simulations, biological language models and synthetic datasets, we explore how multiomics data is informing the digital era, and how we all have a role to play in generating data that will be useful beyond its initial purpose.