Using Bioinformatics Data to inform Therapeutics discovery and development
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The document discusses bioinformatics strategies to enhance computational biology, highlighting lessons learned from collaborations with clients like Zafgen, Inc. It emphasizes the importance of combining public datasets with proprietary data for drug discovery and outlines key roles and expertise needed in the field. Additionally, it touches on deep learning applications and the need for careful consideration of data quality and model interpretability in biological research.
Using Bioinformatics Data to inform Therapeutics discovery and development
- 1. From data to insights and action: Strategies to take your bioinformatics to the next level Eleanor Howe, Diamond Age Data Science Huseyin Mehmet, Zafgen, Inc. December 7, 2018
- 2. What is this talk about? • Who are we? What is computational biology? • Lessons learned from working with our customers • Our ongoing relationship with Zafgen • Q&A
- 3. Eleanor Howe, PhD Background in molecular biology, statistics, programming and computational biology/bioinformatics eleanor@diamondage.com
- 4. Diamond Age Data Science www.diamondage.com Bioinformatics/computational biology consulting Project-based analysis Staff augmentation Pipeline development “Drop-in” bioinformatics department The Diamond Age: or, A Young Lady’s Illustrated Primer by Neal Stephenson
- 5. Team Chris Friedline Sequencing, software engineering Somdutta Saha Computational chemistry and proteomics Bruce Romano Mathematics and data science Nicholas Crawford Human genetics and GWAS Mike DeRan Cancer and diabetes therapeutics, scRNA-seq Max Marin RNA splicing Zarko Boskovic Medicinal chemistry and metabolomics Chris Dwan IT and data security
- 6. A few of our clients
- 7. Computational Biology Computational biology is data science for biology Bioinformatics is sometimes a synonym for computational biology. Other times, bioinformatics refers to software engineering for biology.
- 9. Drug discovery requires evaluation of diverse, complex data • Sequence analysis is very different from proteomics • Knowing the landscape of available datasets is key • Individual bioinformaticians tend to specialize in one sub-field or another
- 10. Public datasets are a gold mine • Cancer Cell-line Encyclopedia • The Cancer Genome Atlas • Gene Expression Omnibus • Dependencies Map (Dep-map) • UK Biobank • DrugBank • VarSome • GTeX
- 11. But the real gems come from your own experiments It’s not possible to validate a drug target using public datasets alone. The public datasets are general, and cover only the most common diseases or disease subtypes. The most useful results come from combining custom-generated data with public data.
- 12. CROs do the basics well • Ocean Ridge, Novogene ($200 transcriptome!) • Good for the basics - RNA-seq, DNA-seq, proteomics, metabolomics • Reasonable standardized analysis pipelines • Challenges: • combining multiple datasets across experiments or across CROs • more involved analysis (e.g. splicing) • Do a thorough cost-comparison when considering an academic collaborator • Also ask them when their student is graduating.
- 13. What additional expertise do you need? Early stage “traditional” therapeutics companies don’t need a full-time computational biologist. Part time can work fine. When the company expands, hire a computational biologist with substantial experience, or an analyst with some kind of advisor available.
- 14. Computational biologist: Experience/training in all three areas Analyst: Biology + programming, with an advisor to help with the statistics Methods developer: Wants to build new analytical tools Know what you need
- 15. What expertise do you need? For Teams: • Cross-discipline expertise -biology, chemistry, computer science, statistics • Communication skills • Lateral thinking
- 16. Expertise gets you fast answers The problem: Get a terabyte of data from a USB hard drive to the cloud in time to analyze a dataset for a conference
- 17. Expertise gets you fast answers The problem: Get a terabyte of data from a USB hard drive to the cloud in time to analyze a dataset for a conference The solution: Bicycle across the Charles 3Gb/s bicycle (latency of 1.2M ms) Datacenter internet connection Markley Data Center
- 18. Deep Learning / Artificial Intelligence Another danger zone
- 19. Deep Learning / Artificial Intelligence Deep learning is “new” in that it’s a more complex version of older technology: a neural network Modern compute power allows for powerful classifiers trained on very large datasets
- 20. The basics of machine learning (and DL) Deep Learning works in a similar way to other types of machine learning. The algorithms use larger datasets and are more complex. But the overall workflow is the same.
- 21. Should you use deep learning? Is your training data: Large. 100,000+ to 1M+ samples Well-annotated. Gene expression data usually isn’t. Representative of the questions you want to answer? In discovery biology, the data is usually not there. Hence “discovery”.
- 22. Good use-cases for deep learning Image processing Diagnostics from histology, radiology High-content screening Biochemical structure/sequence Epitope prediction Protein folding (Deep Mind) Single-cell RNA-seq (potentially)
- 23. Should you use deep learning? (cont) Do you need an interpretable model? Deep learning is a black box Have you tried everything else? Linear models, random forests, other ML techniques These tools are often faster, cheaper, and easier to understand and implement
- 25. Huseyin Mehmet, PhD Vice President and Head of Discovery Research Zafgen, Inc.
- 26. Zafgen, Inc • Publicly traded bio-pharmaceutical company • Founded 12 years ago (IPO in 2014) • Virtual company • Bringing MetAP2 inhibitors to market • Areas of interest: Metabolic disease
- 27. Zafgen and Diamond Age Diamond Age acts as a virtual bioinformatics department for Zafgen • Data Analysis • Data Management • Hypothesis generation • Technology recommendations
- 28. What Diamond Age has done for Zafgen • Transcriptional profiling • Proteomics/phosphoproteomics • Metabolomics • Clinical outcomes • Custom apps for client needs
- 29. The benefits What can Zafgen can do now that it couldn’t before? • Iterative data generation • Cross-dataset analyses • Confidence in analysis results from CROs • Link between pre-clinical and clinical data • Cost efficiencies / value for money