BioAssay Express: Creating and exploiting assay metadata
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The challenge of accurately characterizing bioassays is a real pain point for many drug discovery organizations. Research has shown that some organizations have legacy assay collections exceeding 20,000 protocols, the great majority of which are not accurately characterized. This problem is compounded by the fact that many new protocol registrations are still not following FAIR (Findability, Accessibility, Interoperability, and Reusability) Data principles. BioAssay Express is a tool focused on transforming the traditional protocol description from an unstructured free form text into a well-curated data store based upon FAIR Data principles. By using well-defined annotations for assays, the tool enables precise ontology based searches without having to resort to imprecise keyword searches. This talk explores a number of new important features designed to help scientists accelerate the drug discovery process. Some example use-cases include: enabling drug repositioning projects; improving SAR models; identifying appropriate machine learning data sets; fine-tuning integrative-omic pathways; An aspirational goal for our team is to build a metadata schema based on semantic web vocabularies that is comprehensive to the extent that the text description becomes optional. One of the many possibilities is to take the initial prospective ELN entry for a bioassay protocol and feed it directly to an automated instrument. While there are many challenges involved in creating the ELN-to-robot loop, we will provide some insights into our collaborations with UCSF automation experts. In summary, the ability to quickly and accurately search or analyze bioassay data (public or internal) is a rate limiting problem in drug discovery. We will present the latest developments toward removing this bottleneck. https://plan.core-apps.com/acs_sd2019/abstract/6f58993d-a716-49ad-9b09-609edde5a3f4
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BioAssay Express: Creating and exploiting assay metadata
- 1. CINF 24: BioAssay express: Creating and exploiting assay metadata collaborativedrug.com bioassayexpress.com bae@collaborativedrug.com Philip Cheung American Chemical Society Sunday, August 25, 2019 - 3:10 PM -- Grand Ballroom A, Omni San Diego Hotel
- 2. A little bit about me… Graduated Harvey Mudd College in 1996 B.S. in Biology SAIC – 1996 – 1997 High Throughput Robotics Systems For ISB (Leroy Hood) Orillion 1997 – 1999 WorldCom MCI / PlayStation 2 Event Management System Medibuy.com 1999 – 2001 Global Health Exchange Between Premier Hospital Group Pfizer Global Research and Development 2001 – 2009 Pfizer Global Crystal Structure Database Oncology Project Support (Computational Biology) Ophthalmology Indication Discovery (Machine Learning) Dart NeuroScience (2009-2018) Bioinformatics Group Leader Independent Consultant (2018-Present) Currently Support multiple informatics / Bioinformatics companies in San Francisco, Boston, and San Diego
- 3. So what is assay informatics and why is it exciting?
- 4. The IDEAL Cycle of Assay Management • Plan experiments & capture ideas • Perform experiments & capture data • Analyze data & identify trends • Store & protect the results • Retrieve data & build knowledge - across Concepts / Time / Projects
- 5. The REALISTIC Cycle of Assay Management • Plan experiments & capture ideas • Perform experiments & capture data • Analyze data & identify trends • Store & organize the results • Retrieve data & build knowledge – across Concepts / Time / Projects • Post-It Note Edits & Lost Attributions • Incomplete “Data Dump” & Lost Data • Siloed data & Incomplete information • Lost & non-reproducible data (crisis!) • Inaccessible & unusable data leading to… TIME WASTED & OPPORTUNIES LOST!
- 6. • No Common Vocabulary • Limited Assay Mining/Searching Capabilities Barrier to Collaboration Failure to Provide Insight Even “Best Case” Assay Management is Inefficient
- 7. Efficiently & Quickly Organize Assay Data Machine Readable Format Common Vocabulary through Ontology Markup Introduces Assay Informatics, Providing New Insight by Querying Biologic, Chemical, and Assay Meta Data BioAssay Express Leads the Field of Assay Informatics
- 8. Capture Metadata Unambiguously with BioAssay Express Ambiguous Unstructured Cytoxicity test; incubate hek cells for 24hrs. Add 1 compound from library (tox 21); triplicate. 16 pt dilution. Inc. 48hrs at 37C. Standard kit protocol using thermo Glo kit. Fluorescence in triplicate. Report IC50 Ambiguous Structured Unambiguous Structured Cell Line: Mode of Action: Assay Kit: Detection Method: Detection Instrument: Perturbagen Type: Result: Units: HEK Cytoxcity GLO (Thermo) Fluorescence Tox 21 EC50 Cell Line: Mode of Action: Assay Kit: Detection Method: Detection Instrument: Perturbagen Type: Result: Units: HEK-293T Cell [BTO_0002181] Cytoxicity [BAO_0000090] CytoTox-Glo (Promega) [BAO_0140009] Luminescence [BAO_0000045] EnVision Multilabel Reader [BAO_0000701] Tox 21 Compound Library [BAO_0070002] IC50 [BAO_0000190] nanomolar [UO_0000065] Missing WrongIncompleteSpelling
- 9. BioAssay Express Enables True Assay Informatics Metadata Assays
- 10. That seems like a lot of work? Can Machine Learning help?
- 11. BioAssay Express - In Action! Assay Registration is a Breeze! • Insert Protocol • Text Mining • Expert Ontologies • Predictive Text • Machine Learning • NLP • Correlation Models • Human Curation • Accuracy is key
- 12. Dose Response assay for agonists of 5-Hydroxytryptamine (Serotonin) Receptor Subtype 1A (5HT1A) Assay Description: Widely expressed in the human brain, 5-hydroxytryptamine (5-HT, serotonin) receptors have been shown to have an important role in depression as well as other cognitive and metabolic disorders [1, 2]. Discovering novel modulators of the 5-HT1A serotonin receptor may not only help probe the function of this receptor, but also help better understand the complex relationship among the 5-HT receptor subtypes. Protocol Summary: As with the primary HTS assay, a Chinese Hamster Ovary (CHO) cell line stably transfected with human 5HT1a receptor, the nuclear factor of activated T-cell-beta lactamase (NFAT-BLA) reporter construct and the G-alpha-15 promiscuous coupling protein was used (Invitrogen, part K1083). Cells were cultured in T-175 sq. cm flasks (Corning, part 431080) at 37 deg C and 95% RH. The assay began by dispensing 10 microliters of cell suspension to each test well of a 384 well plate. BioAssay Express: Optimized for Low False Positives
- 13. Assay Fingerprints: Bringing Informatics to Metadata AssayMetadata Assays Assay Property Grid • Generate assay fingerprints • Compare hundreds of assays at a glance • Find, Share, and Innovate Blue boxes = exact match - Blue lines = match inferred from hierarchy • Why are my results different from others doing the ”same” assay? • Has anyone studied this disease variant in neurons? • Did results vary when we switched instrument models? • Has someone else already done my experiment • What other programs have already screened my target? Can I jumpstart my new program with some existing chemistry? • I want to do some machine learning – can I find some other “appropriate” experiments.
- 14. So, I have a database… how do I this apply to my data?
- 15. So how can I use this technology? So let’s take a look at the steps required to process a semi-structured dataset like clinicaltrials.gov
- 16. Structured because there are tables that hold categories of data
- 17. Unfortunately, its unstructured because the content does not map to an ontology consistently.
- 18. So, how do I FAIR-ify my data?
- 19. So how do we go from unstructured to structured data? Step 1 – Review the data you’re importing; Map your data to ontologies ClinicalTrials.gov Step 2 –Perform the import; some percentage will map perfectly. Step 3 -- Curate the remaining data using the BAE’s NLP models.
- 20. Step 1- Map your data to ontologies Open source BioAssay Template Tool https://github.com/cdd/bioassay-template
- 21. Step 2- Import: Generate JSON Files
- 22. Step 2- Import: Or import using CSV File
- 23. Step 2- Some percentage will map out of the box In this example, I imported a small subset of clinical trials.gov – 18825 of the 313472 (~6%) available studies If I was interested in multiple myeloma, I could write a script that was aggressive mapping and assign all of these as “multiple myeloma”, or I could assign only the perfect matches, and let BAE’s NLP help me with the mappings.
- 24. Step 3 – Machine Assisted Curation Importing / Mapping data allows BAE to build Bayesian Models
- 25. Interesting questions you can visualize What cancer trials was Celecoxib used in What other combination therapies were also used in those
- 26. Interesting questions you can visualize What new Interventions are being used in Malaria?
- 27. Interesting questions you can visualize What new Interventions are being used in Malaria?
- 28. Interesting questions you can visualize What new Interventions are being used in Malaria?
- 29. We’re picking up steam * Extensive Proof Of Concept Study
- 30. But let’s talk about Robots now!
- 31. Current Directions -- Projects An aspirational goal for our team is to build a metadata schema based on semantic web vocabularies that is comprehensive to the extent that the text description becomes optional There are many challenges involved in creating the ELN-to-robot loop, here we provide some insights into our collaborations with UCSF automation experts at the Small Molecule Discovery Center.
- 32. The High level goal – ELN to Robot GBG by BioSero Cellario By HiResBio Adapter/Builder Director by Wako (Fuji) - Model the protocols at the step level so we can export them out to automation systems - Import the results back into the system
- 34. Often times they are more complex -- branch and merge
- 35. Break the protocol down into steps • Model the Dependencies • Equipment dependencies • Reagent dependencies • Previous step dependencies • Model the Steps • Protocol Steps
- 36. Sample Protocol https://pubchem.ncbi.nlm.nih.gov/bioassay/346#section=Protocol Plate Layout Plate Action (Centrifuge) Assay Start (Add Compounds) Incubation Readout
- 37. Sample Protocol https://pubchem.ncbi.nlm.nih.gov/bioassay/346#section=Protocol Plate Layout Plate Action (Centrifuge) Assay Start (Add Compounds) Incubation Readout
- 40. Next Steps • Next Steps • Continue our work with UCSF with simple proof of concept protocols • Reach out to Vendors and see if we can integrate into their simulation platforms • Thermo • Wako • Keep reaching out and learning from experts like yourself. http://www.bioassayexpress.com
- 41. Try it out! • Collaborative Drug Discovery • Alex Clark • Hande Kücük McGuinty • Peter Gedeck • Samantha Jeschonek • Barry Bunin • For more info • bae@collaborativedrug.com http://www.bioassayexpress.com
- 42. Smart Drug Discovery Software SavesScientistsTimeSmart Drug Discovery Software Saves Time Session: CINF: Sci-Mix Location: Exhibit Hall B, Date & Time: Monday, Aug 26 8:00 PM
Editor's Notes
- Now At the onset, assay management seems like it should be straightforward. You plan an experiment and capture ideas, perform the experiment and capture data, analyze and identify trends, store and protect the reults, and then retrieve the data to build knowledge.
- BUT in reality what happen is different. Best plans turn into post it note edits and lost attribution, Incomplete data dumps, that lead to results with missing information causing data that is lost and non reproducible. Failure to capture assay correctly in fact greatly attributes to the scientific reproducibility crisis. And then when you try and retrieve data you’re left frustrated, either because you can’t find the data, or you do and it’s not descriptive essentially making it unuseable. This just leads to wasted time and lost opportunities
- But even the best case scenario for assay management is inefficient It lacks a common vocab between scientists (groups or organization) And there’s a limited ability to mine your assays or search efficiently
- Plus, BioAssay Express was built with “FAIR” data in mind, and have scored high marks in that arena. A lofty goal, but by capturing assay meta data in an organized, machine readable, and ontology driven format, we hope that our software product helps reduce the reproducibility crisis and streamlines assay optimization.
- In essencd we provide a solution that turns This human readable, unorganized text into A structured, machine readable format, Allowing you to take fiull advantage of your assay metadata for searching and comparisons across all assays.
- Now the real beauty of BAE is how convenient it is to use. Annotation of an entire protocol can occur in a matter of minutes, and to demonstrate that I’m going to show you a real time annotation. An assay is simply pasted into the text box We click request suggestions And almost immediately these annotation fields begin to populate. First the text is mined for key terms, shown in green. Then the other fields being to populate via predictive text. Using that hybrid machine learning approach, based on the terms entered, BAE predicts associations and suggests additional terms. But we also allow manual curation review. Accuracy is key in the field of assay informatics, and as such we did not want to rely solely on a text mining approach. The hybrid machine learning greatly accelerates human curation.
- Let’s take a closer look at the Text mining aspect. You’ll see that the green fields, those from text mining, are show in green, and match exact phrases or synonyms from the full protocol text. In gray are our predictive text functions, and this AI learns and grows with your personal data (of course privately and securely maintained). One easy area to think about this is in the field of detection methods. We can predict that certain assay kits will require a particular type of physical detection or readout, and that that detection will require a particular instrument reader.
- Instead of just a list of similar assays,we visually provide you with all assay metadata. You can compare hundreds of assays at a glance. Here, we sorted approx. 4000 assays down to about 40 in less than a minute. At the top are your assays that match the search terms and on the right are you metadata fields Blue boxes = match, blue lines = match inferred by the hierarchy. Once you create these assay fingerprints you’re able to beging quickly querying your data and using it to probe new areas of research. You might use this to find out why you assay results are different from others doing the same assay (sometimes as simple as a different instrument detection was used” We might ask to reterive any data done with a particular kit, or on a particular target We can eas ask what studies are being done on that new fav protein that came out in the mass spec screen Or inquire about diversifying our assays (avoiding assay bias) And because we’re capturing this data with a common language, in a easy to compare way, we can start to reduce the errors in reproducibility caused by incomplete assay reporting.
- One of my favorite aspects of BAE // The real beauty and power of BAE though comes from our assay fingerprint. Across the top are all those assays matching your previous search criteria, and on the left are all of those annotation terms. Blue boxes represent the presence of a term and blue lines indicate a presence inferred by the hierarchy. Suddenly that assay metadata becomes a tool to power and query your results. Why are two similar assay giving different results? Why isn’t a particular hit present in one assay? Sometimes that answer is as simple as a different cell line was used, or a different detection instrument with a varying level of sensitivity. But the answers SHOULD come that quick, assay optimization or comparison shouldn’t require hours of literature comparison or holding up dozens of notebook entries. It should be quick, painless, and informative. And that’s what we hope BAE accomplishes
- One of my favorite aspects of BAE // The real beauty and power of BAE though comes from our assay fingerprint. Across the top are all those assays matching your previous search criteria, and on the left are all of those annotation terms. Blue boxes represent the presence of a term and blue lines indicate a presence inferred by the hierarchy. Suddenly that assay metadata becomes a tool to power and query your results. Why are two similar assay giving different results? Why isn’t a particular hit present in one assay? Sometimes that answer is as simple as a different cell line was used, or a different detection instrument with a varying level of sensitivity. But the answers SHOULD come that quick, assay optimization or comparison shouldn’t require hours of literature comparison or holding up dozens of notebook entries. It should be quick, painless, and informative. And that’s what we hope BAE accomplishes
- One of my favorite aspects of BAE // The real beauty and power of BAE though comes from our assay fingerprint. Across the top are all those assays matching your previous search criteria, and on the left are all of those annotation terms. Blue boxes represent the presence of a term and blue lines indicate a presence inferred by the hierarchy. Suddenly that assay metadata becomes a tool to power and query your results. Why are two similar assay giving different results? Why isn’t a particular hit present in one assay? Sometimes that answer is as simple as a different cell line was used, or a different detection instrument with a varying level of sensitivity. But the answers SHOULD come that quick, assay optimization or comparison shouldn’t require hours of literature comparison or holding up dozens of notebook entries. It should be quick, painless, and informative. And that’s what we hope BAE accomplishes
- One of my favorite aspects of BAE // The real beauty and power of BAE though comes from our assay fingerprint. Across the top are all those assays matching your previous search criteria, and on the left are all of those annotation terms. Blue boxes represent the presence of a term and blue lines indicate a presence inferred by the hierarchy. Suddenly that assay metadata becomes a tool to power and query your results. Why are two similar assay giving different results? Why isn’t a particular hit present in one assay? Sometimes that answer is as simple as a different cell line was used, or a different detection instrument with a varying level of sensitivity. But the answers SHOULD come that quick, assay optimization or comparison shouldn’t require hours of literature comparison or holding up dozens of notebook entries. It should be quick, painless, and informative. And that’s what we hope BAE accomplishes
- One of my favorite aspects of BAE // The real beauty and power of BAE though comes from our assay fingerprint. Across the top are all those assays matching your previous search criteria, and on the left are all of those annotation terms. Blue boxes represent the presence of a term and blue lines indicate a presence inferred by the hierarchy. Suddenly that assay metadata becomes a tool to power and query your results. Why are two similar assay giving different results? Why isn’t a particular hit present in one assay? Sometimes that answer is as simple as a different cell line was used, or a different detection instrument with a varying level of sensitivity. But the answers SHOULD come that quick, assay optimization or comparison shouldn’t require hours of literature comparison or holding up dozens of notebook entries. It should be quick, painless, and informative. And that’s what we hope BAE accomplishes
- One of my favorite aspects of BAE // The real beauty and power of BAE though comes from our assay fingerprint. Across the top are all those assays matching your previous search criteria, and on the left are all of those annotation terms. Blue boxes represent the presence of a term and blue lines indicate a presence inferred by the hierarchy. Suddenly that assay metadata becomes a tool to power and query your results. Why are two similar assay giving different results? Why isn’t a particular hit present in one assay? Sometimes that answer is as simple as a different cell line was used, or a different detection instrument with a varying level of sensitivity. But the answers SHOULD come that quick, assay optimization or comparison shouldn’t require hours of literature comparison or holding up dozens of notebook entries. It should be quick, painless, and informative. And that’s what we hope BAE accomplishes
- One of my favorite aspects of BAE // The real beauty and power of BAE though comes from our assay fingerprint. Across the top are all those assays matching your previous search criteria, and on the left are all of those annotation terms. Blue boxes represent the presence of a term and blue lines indicate a presence inferred by the hierarchy. Suddenly that assay metadata becomes a tool to power and query your results. Why are two similar assay giving different results? Why isn’t a particular hit present in one assay? Sometimes that answer is as simple as a different cell line was used, or a different detection instrument with a varying level of sensitivity. But the answers SHOULD come that quick, assay optimization or comparison shouldn’t require hours of literature comparison or holding up dozens of notebook entries. It should be quick, painless, and informative. And that’s what we hope BAE accomplishes
- One of my favorite aspects of BAE // The real beauty and power of BAE though comes from our assay fingerprint. Across the top are all those assays matching your previous search criteria, and on the left are all of those annotation terms. Blue boxes represent the presence of a term and blue lines indicate a presence inferred by the hierarchy. Suddenly that assay metadata becomes a tool to power and query your results. Why are two similar assay giving different results? Why isn’t a particular hit present in one assay? Sometimes that answer is as simple as a different cell line was used, or a different detection instrument with a varying level of sensitivity. But the answers SHOULD come that quick, assay optimization or comparison shouldn’t require hours of literature comparison or holding up dozens of notebook entries. It should be quick, painless, and informative. And that’s what we hope BAE accomplishes
- One of my favorite aspects of BAE // The real beauty and power of BAE though comes from our assay fingerprint. Across the top are all those assays matching your previous search criteria, and on the left are all of those annotation terms. Blue boxes represent the presence of a term and blue lines indicate a presence inferred by the hierarchy. Suddenly that assay metadata becomes a tool to power and query your results. Why are two similar assay giving different results? Why isn’t a particular hit present in one assay? Sometimes that answer is as simple as a different cell line was used, or a different detection instrument with a varying level of sensitivity. But the answers SHOULD come that quick, assay optimization or comparison shouldn’t require hours of literature comparison or holding up dozens of notebook entries. It should be quick, painless, and informative. And that’s what we hope BAE accomplishes
- One of my favorite aspects of BAE // The real beauty and power of BAE though comes from our assay fingerprint. Across the top are all those assays matching your previous search criteria, and on the left are all of those annotation terms. Blue boxes represent the presence of a term and blue lines indicate a presence inferred by the hierarchy. Suddenly that assay metadata becomes a tool to power and query your results. Why are two similar assay giving different results? Why isn’t a particular hit present in one assay? Sometimes that answer is as simple as a different cell line was used, or a different detection instrument with a varying level of sensitivity. But the answers SHOULD come that quick, assay optimization or comparison shouldn’t require hours of literature comparison or holding up dozens of notebook entries. It should be quick, painless, and informative. And that’s what we hope BAE accomplishes
- One of my favorite aspects of BAE // The real beauty and power of BAE though comes from our assay fingerprint. Across the top are all those assays matching your previous search criteria, and on the left are all of those annotation terms. Blue boxes represent the presence of a term and blue lines indicate a presence inferred by the hierarchy. Suddenly that assay metadata becomes a tool to power and query your results. Why are two similar assay giving different results? Why isn’t a particular hit present in one assay? Sometimes that answer is as simple as a different cell line was used, or a different detection instrument with a varying level of sensitivity. But the answers SHOULD come that quick, assay optimization or comparison shouldn’t require hours of literature comparison or holding up dozens of notebook entries. It should be quick, painless, and informative. And that’s what we hope BAE accomplishes
- CDD Vault is a software platform for your secure data management and registration needs, able to capture all kinds of data, from numeric assay data to biological images. Visit my colleague Janice Darlington at Poster XY for more information.