Multi-Omic Cloud Strategies
Discover how leading biotech teams turn fragmented multi-omic data into validated, decision-ready biological insight - and the data management, processing and AI strategy that makes it possible in the cloud.
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From Fragmented Data to Biological Insight
The infrastructure covered in this session exists for one reason - to get from raw multi-omic data to validated biological insight, faster and more reliably. After watching, you'll understand:
Why many multi-omic projects fail before the analysis even begins
How to structure data so multi-omic research is reproducible and reusable
How to integrate RNA and protein datasets at scale - even when they're measured in different units
How to reduce processing bottlenecks and avoid unnecessary infrastructure cost
What foundations need to be in place before introducing AI and machine learning to your workflows
Three Problems This Webinar Addresses
Craig, Gerard and Matt structure the session around the three layers that determine whether a multi-omic strategy actually works in practice.
Data Management
Without a clear data strategy, teams end up with siloed datasets, inconsistent naming, prolonged access requests and no single source of truth - the foundation every other step depends on.
Data Processing
Bioinformatics pipelines are software in their own right - with maintenance, runtime, cost and security challenges that compound when working across diverse multi-omic file formats.
AI and Machine Learning
Getting real value from multi-omic AI requires the right team structure, tooling and infrastructure - not just access to open-source models and methods.
What Poor Multi-Omic Infrastructure Actually Costs
These are real figures and outcomes referenced during the session - from clients Sonrai has worked with directly.
47%
of webinar attendees identified "no data strategy" as a top challenge
Polled live during the session
Multi-Million
dollar storage costs quoted for large clinical datasets
A cost Sonrai describes as largely avoidable
2 weeks → 20s
data processing time for a phase 2 trial's translational data package
After automating harmonisation and validation
7
multimodal datasets unified into one ranked view in the live demo
41 tissues, 2 modalities, terabytes of raw data
Questions This Webinar Helps Answer
The session covers practical answers for teams trying to make multi-omic data genuinely usable, not just stored.
How Can I Improve Access, Governance and Reuse of Research Data?
Craig and Gerard walk through the practical questions that reveal whether your data is genuinely reusable across teams - and what a flexible, self-service approach to data access looks like in practice.
How Do I Bring Different Omics and Tissue Types Into One Analysis?
See a live demonstration combining RNA and protein data across 41 tissues from 7 different datasets into a single ranked view - regardless of the original units of measurement.
How Do I Make Bioinformatics Pipelines Safe and Reproducible?
Matt covers the hidden risks in open-source pipelines, how Nextflow and the NF-core community fit in, and how one client's data processing time dropped from two weeks to twenty seconds.
What Does It Take to Get "AI Ready" for Multi-Omics?
Understand the team structure, tooling and infrastructure that need to be in place before multi-omic AI delivers real value - illustrated with a brief look at a forthcoming case study.
What You'll Learn
A Real Multi-Omic Analysis, Done Right
Craig introduces the common problems Sonrai sees across biotech and pharma, then Matt demonstrates a real client project live - integrating 7 multimodal datasets across 41 tissues into a single Weaver Plot, validating a biomarker hypothesis in minutes.
Cloud Strategies for Storage, Processing and Management
Gerard covers the data mesh approach, FAIR principles and rule-based access control, then Matt explains the hidden risks of bioinformatics pipelines and demonstrates a Nextflow-based pipeline that cut processing time from two weeks to twenty seconds.
AI and Machine Learning for Multi-Omics
Matt outlines what it takes to get AI-ready as an organisation and the role of multidisciplinary teams - then previews a forthcoming case study using multi-omic factor analysis as one example of what becomes possible on the right foundations, followed by audience Q&A throughout.
Validate Biomarker Hypotheses Across Multiple Omics Datasets
A client presented seven multimodal datasets - RNA and protein expression across 41 tissues, normalised in five different units, with terabytes of raw data and millions of potential combinations - to identify targets that might cause toxicity in healthy tissue.
- Integrating RNA and protein data measured in different units (iBAQ, FPKM)
- An ontology mapping algorithm to harmonise inconsistent tissue naming
- The Weaver Plot - a configurable, weighted ranking across all datasets
- Live hypothesis testing: validating OLFM4 expression in intestinal tissue
- All built on Sonrai Discovery with publicly available datasets
Unified View Across Mismatched Units
RNA data measured in FPKM and protein data measured in iBAQ were ranked side by side - giving a consensus view across all seven datasets without needing to renormalise everything to a common scale.
Hypothesis Validated in Minutes
Starting from a hypothesis that OLFM4 would be expressed in colon or intestinal tissue, the Weaver Plot confirmed intestine ranked top across the datasets - consistent with the hypothesised toxicity signal.
Solving the Naming Problem
The same tissue - colon cancer - was described three different ways across the source datasets. An ontology mapping algorithm standardised this automatically, a recurring challenge in multi-omic integration.
What Made This Possible
- Backend storage built for flexible, scalable multi-omic integration
- SQL query engine identifies shared index columns (Hugo gene symbols)
- Adjustable dataset weightings let users favour RNA or protein evidence
Built for Teams Managing Multi-Omic Data at Scale
Particularly relevant if your organisation is handling multiple omics data types and struggling to bring them together reliably.
Translational Scientists
Wanting to combine RNA, protein and other omics data across tissues and studies to validate biomarker hypotheses without manual cross-referencing.
Bioinformaticians
Maintaining pipelines and looking for ways to reduce the maintenance burden, runtime costs and security risks of open-source bioinformatics workflows.
Data and IT Leadership
Responsible for data strategy, storage costs and security - particularly where data access requests, siloed teams or unclear data ownership are slowing research down.
R&D and Clinical Operations Leads
Working with translational data packages from clinical trials who need faster, more reliable processing and a clear path toward AI-enabled analysis.
Meet the Speakers
Platform, engineering and data science perspectives from Sonrai's team.
Dr Craig Davison
Platform Specialist, Sonrai Analytics
Craig holds a PhD in molecular biology and has 9 years of precision medicine research experience. He hosts this session and introduces the common data challenges Sonrai sees across biotech and pharma.
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Dr Matt Alderdice
Head of Data Science, Sonrai Analytics
Matt has a PhD in genomics and molecular pathology, with over 10 years of precision medicine experience including multi-omics. He leads the live Weaver Plot demonstration and the pipeline automation case study.
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Gerard Loughran
Head of Engineering, Sonrai Analytics
Gerard has deep experience in data management, cloud infrastructure, compliance and cyber security. He covers data mesh architecture, FAIR principles and rule-based access control in this session.
LinkedIn ↗Access the Full Session
Watch the full Weaver Plot demonstration across 7 multimodal datasets, the data mesh and FAIR strategy walkthrough, the pipeline automation case study, and the introduction to multi-omic factor analysis - plus audience Q&A throughout.
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