Open PHACTS: The Data Today

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Presentation given at the Open PHACTS project symposium. The slides give an overview of the data in the 2.0 Open PHACTS drug discovery platform and the challenges that have been faced in the Open PHACTS project to reach this stage.

Tecnologia

The Data Today
Alasdair Gray
Heriot-Watt University, Edinburgh, UK
A.J.G.Gray@hw.ac.uk
@gray_alasdair

Dataset Downloaded Version Licence Triples
Bio Assay Ontology CC-By 10,360
CALOHA 8 Apr 2015 2014-01-22 CC-By-ND 14,552
ChEBI 4 Mar 2015 125 CC-By-SA 1,012,056
ChEMBL 18 Feb 2015 20.0 CC-By-SA 445,732,880
ConceptWiki 12 Dec 2013 CC-By-SA 4,331,760
DisGeNET 31 Mar 2015 2.1.0 ODbL 15,011,136
Disease Ontology 2015-05-21 CC-By 188,062
DrugBank 19 Feb 2015 4.1 Non-commercial 4,028,767
ENZYME 2015_11 CC-By-ND 61,467
FDA Adverse Events 9 Jul 2012 CC0 13,557,070
Total: ~3 Billion triples

Dataset Downloaded Version Licence Triples
Gene Ontology 4 Mar 2015 CC-By 1,366,494
Gene Ontology Annotations 17 Feb 2015 CC-By 879,448,347
NCATS OPDDR Nov 2015 Oct 2015 2,643
neXTProt (NP) 1 Feb 2014 1.0 CC-By-ND 215,006,108
OPS Chemical Registry 4 Nov 2014 CC-By-SA 241,986,722
HMDB 3.6 HMDB
MeSH 2015 MeSH
PDB Ligands 2 PDB
OPS Metadata CC-By-SA 2,053
UniProt 2015_11 CC-By-ND 1,131,186,434
WikiPathways 20151118 CC-By 11,781,627
Total: ~3 Billion triples

John Wilbanks consulted for us
A framework built around STANDARD well-understood
Creative Commons licences – and how they interoperate
Deal with the problems by:
Interoperable licences
Appropriate terms
Declare expectations to users and
data publishers
One size won‘t fit all requirements
Data Licensing (Or Lack Of!)

STANDARD_TYPE UNIT_COUNT
---------------- -------
AC50 7
Activity 421
EC50 39
IC50 46
ID50 42
Ki 23
Log IC50 4
Log Ki 7
Potency 11
log IC50 0
STANDARD_TYPE STANDARD_UNITS COUNT(*)
------------------ ------------------ --------
IC50 nM 829448
IC50 ug.mL-1 41000
IC50 38521
IC50 ug/ml 2038
IC50 ug ml-1 509
IC50 mg kg-1 295
IC50 molar ratio 178
IC50 ug 117
IC50 % 113
IC50 uM well-1 52
~ 100 units
>5000 types
Implemented using the Quantities, Units, Dimension, Types
Ontology (http://www.qudt.org/)
Quantitative Data Challenges

ops:OPS437281
✔
ops:OPS380297 ops:OPS380292
is_stereoisomer_of
[ci:CHEMINF_000461]
has_stereoundefined_parent
[ci:CHEMINF_000456] Other relationships
• has part
• is tautomer of
• uncharged counterpart
• isotope
…
Chemical Registration Service Data

Mappings: Computed
Mappings (Comp)
200,000,000+

P12047
X31045
GB:29384
Andy Law's Third Law
“The number of unique identifiers assigned to an individual is
never less than the number of Institutions involved in the study”
http://bioinformatics.roslin.ac.uk/lawslaws/

DrugbankChemSpider PubChem
MesylateImatinib Mesylate
YLMAHDNUQAMNNX-UHFFFAOYSA-N
Are these records the same?
It depends upon your task!

skos:exactMatch
(InChI)
Strict Relaxed
Analysing Browsing
I need to perform an analysis, give me
details of the active compound in Gleevec.

skos:closeMatch
(Drug Name)
skos:closeMatch
(Drug Name)
skos:exactMatch
(InChI)
Strict Relaxed
Analysing Browsing
Which targets are known to interact
with Gleevec?

A lens defines a conceptual view over the data
Specifies operational equivalence conditions
Consists of:
Identifier (URI)
Title
(dct:title)
Description
(dct:description)
Documentation link
(dcat:landingPage)
Creator
(pav:createdBy)
Timestamp
(pav:createdOn)
Equivalence rules
(bdb:linksetJustification)
Scientific Lens
Lenses
34 in total
7 Public
25 Chemistry
2 Gene

Data Governance
Contribution must not be underestimated!!!

Alasdair J G Gray
A.J.G.Gray@hw.ac.uk
www.macs.hw.ac.uk/~ajg33/
@gray_alasdair
Open PHACTS
contact@openphacts.org
openphacts.org
@open_phacts

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Imagine a world where information flows as swiftly as thought itself, making decision-making as fluid as the data driving it. Every moment is critical, and the right tools can significantly boost your organization’s performance. The power of real-time data automation through FME can turn this vision into reality. Aimed at professionals eager to leverage real-time data for enhanced decision-making and efficiency, this webinar will cover the essentials of real-time data and its significance. We’ll explore: FME’s role in real-time event processing, from data intake and analysis to transformation and reporting An overview of leveraging streams vs. automations FME’s impact across various industries highlighted by real-life case studies Live demonstrations on setting up FME workflows for real-time data Practical advice on getting started, best practices, and tips for effective implementation Join us to enhance your skills in real-time data automation with FME, and take your operational capabilities to the next level.

From Event to Action: Accelerate Your Decision Making with Real-Time Automation

Safe Software

As privacy and data protection regulations evolve rapidly, organizations operating in multiple jurisdictions face mounting challenges to ensure compliance and safeguard customer data. With state-specific privacy laws coming up in multiple states this year, it is essential to understand what their unique data protection regulations will require clearly. How will data privacy evolve in the US in 2024? How to stay compliant? Our panellists will guide you through the intricacies of these states' specific data privacy laws, clarifying complex legal frameworks and compliance requirements. This webinar will review: - The essential aspects of each state's privacy landscape and the latest updates - Common compliance challenges faced by organizations operating in multiple states and best practices to achieve regulatory adherence - Valuable insights into potential changes to existing regulations and prepare your organization for the evolving landscape

TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments

TrustArc

Manulife - Insurer Innovation Award 2024

The Digital Insurer

MySQL Webinar, presented on the 25th of April, 2024. Summary: MySQL solutions enable the deployment of diverse Database Architectures tailored to specific needs, including High Availability, Disaster Recovery, and Read Scale-Out. With MySQL Shell's AdminAPI, administrators can seamlessly set up, manage, and monitor these solutions, ensuring efficiency and ease of use in their administration. MySQL Router, on the other hand, provides transparent routing from the application traffic to the backend servers in the architectures, requiring minimal configuration. Completely built in-house and supported by Oracle, these solutions have been adopted by enterprises of all sizes for their business-critical applications. In this presentation, we'll delve into various database architecture solutions to help you choose the right one based on your business requirements. Focusing on technical details and the latest features to maximize the potential of these solutions.

Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...

Miguel Araújo

This presentations targets students or working professionals. You may know Google for search, YouTube, Android, Chrome, and Gmail, but did you know Google has many developer tools, platforms & APIs? This comprehensive yet still high-level overview outlines the most impactful tools for where to run your code, store & analyze your data. It will also inspire you as to what's possible. This talk is 50 minutes in length.

Powerful Google developer tools for immediate impact! (2023-24 C)

wesley chun

This presentation explores the impact of HTML injection attacks on web applications, detailing how attackers exploit vulnerabilities to inject malicious code into web pages. Learn about the potential consequences of such attacks and discover effective mitigation strategies to protect your web applications from HTML injection vulnerabilities. for more information visit https://bostoninstituteofanalytics.org/category/cyber-security-ethical-hacking/

HTML Injection Attacks: Impact and Mitigation Strategies

Boston Institute of Analytics

Webinar Recording: https://www.panagenda.com/webinars/why-teams-call-analytics-is-critical-to-your-entire-business Nothing is as frustrating and noticeable as being in an important call and being unable to see or hear the other person. Not surprising then, that issues with Teams calls are among the most common problems users call their helpdesk for. Having in depth insight into everything relevant going on at the user’s device, local network, ISP and Microsoft itself during the call is crucial for good Microsoft Teams Call quality support. To ensure a quick and adequate solution and to ensure your users get the most out of their Microsoft 365. But did you know that ‘bad calls’ are also an excellent indicator of other problems arising? Precisely because it is so noticeable!? Like the canary in the mine, bad calls can be early indicators of problems. Problems that might otherwise not have been noticed for a while but can have a big impact on productivity and satisfaction. Join this session by Christoph Adler to learn how true Microsoft Teams call quality analytics helped other organizations troubleshoot bad calls and identify and fix problems that impacted Teams calls or the use of Microsoft365 in general. See what it can do to keep your users happy and productive! In this session we will cover - Why CQD data alone is not enough to troubleshoot call problems - The importance of attributing call problems to the right call participant - What call quality analytics can do to help you quickly find, fix-, and prevent problems - Why having retrospective detailed insights matters - Real life examples of how others have used Microsoft Teams call quality monitoring to problem shoot problems with their ISP, network, device health and more.

Why Teams call analytics are critical to your entire business

panagenda

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With more memory available, system performance of three Dell devices increased, which can translate to a better user experience Conclusion When your system has plenty of RAM to meet your needs, you can efficiently access the applications and data you need to finish projects and to-do lists without sacrificing time and focus. Our test results show that with more memory available, three Dell PCs delivered better performance and took less time to complete the Procyon Office Productivity benchmark. These advantages translate to users being able to complete workflows more quickly and multitask more easily. Whether you need the mobility of the Latitude 5440, the creative capabilities of the Precision 3470, or the high performance of the OptiPlex Tower Plus 7010, configuring your system with more RAM can help keep processes running smoothly, enabling you to do more without compromising performance.

Boost PC performance: How more available memory can improve productivity

Principled Technologies

How to Troubleshoot Apps for the Modern Connected Worker

ThousandEyes

AWS Community Day CPH - Three problems of Terraform

Andrey Devyatkin

Open PHACTS: The Data Today

1. The Data Today Alasdair Gray Heriot-Watt University, Edinburgh, UK A.J.G.Gray@hw.ac.uk @gray_alasdair

2. @gray_alasdair Big Data Integration 2

3. Dataset Downloaded Version Licence Triples Bio Assay Ontology CC-By 10,360 CALOHA 8 Apr 2015 2014-01-22 CC-By-ND 14,552 ChEBI 4 Mar 2015 125 CC-By-SA 1,012,056 ChEMBL 18 Feb 2015 20.0 CC-By-SA 445,732,880 ConceptWiki 12 Dec 2013 CC-By-SA 4,331,760 DisGeNET 31 Mar 2015 2.1.0 ODbL 15,011,136 Disease Ontology 2015-05-21 CC-By 188,062 DrugBank 19 Feb 2015 4.1 Non-commercial 4,028,767 ENZYME 2015_11 CC-By-ND 61,467 FDA Adverse Events 9 Jul 2012 CC0 13,557,070 Total: ~3 Billion triples

4. Dataset Downloaded Version Licence Triples Gene Ontology 4 Mar 2015 CC-By 1,366,494 Gene Ontology Annotations 17 Feb 2015 CC-By 879,448,347 NCATS OPDDR Nov 2015 Oct 2015 2,643 neXTProt (NP) 1 Feb 2014 1.0 CC-By-ND 215,006,108 OPS Chemical Registry 4 Nov 2014 CC-By-SA 241,986,722 HMDB 3.6 HMDB MeSH 2015 MeSH PDB Ligands 2 PDB OPS Metadata CC-By-SA 2,053 UniProt 2015_11 CC-By-ND 1,131,186,434 WikiPathways 20151118 CC-By 11,781,627 Total: ~3 Billion triples

5. John Wilbanks consulted for us A framework built around STANDARD well-understood Creative Commons licences – and how they interoperate Deal with the problems by: Interoperable licences Appropriate terms Declare expectations to users and data publishers One size won‘t fit all requirements Data Licensing (Or Lack Of!)

6. Disease Tissue Target Compound Pathway

7. STANDARD_TYPE UNIT_COUNT ---------------- ------- AC50 7 Activity 421 EC50 39 IC50 46 ID50 42 Ki 23 Log IC50 4 Log Ki 7 Potency 11 log IC50 0 STANDARD_TYPE STANDARD_UNITS COUNT(*) ------------------ ------------------ -------- IC50 nM 829448 IC50 ug.mL-1 41000 IC50 38521 IC50 ug/ml 2038 IC50 ug ml-1 509 IC50 mg kg-1 295 IC50 molar ratio 178 IC50 ug 117 IC50 % 113 IC50 uM well-1 52 ~ 100 units >5000 types Implemented using the Quantities, Units, Dimension, Types Ontology (http://www.qudt.org/) Quantitative Data Challenges

8. Quality Assurance

9. ops:OPS437281 ✔ ops:OPS380297 ops:OPS380292 is_stereoisomer_of [ci:CHEMINF_000461] has_stereoundefined_parent [ci:CHEMINF_000456] Other relationships • has part • is tautomer of • uncharged counterpart • isotope … Chemical Registration Service Data

10. Mappings: Raw Mappings (Raw) 25,087,328

11. Mappings: Computed Mappings (Comp) 200,000,000+

12. P12047 X31045 GB:29384 Andy Law's Third Law “The number of unique identifiers assigned to an individual is never less than the number of Institutions involved in the study” http://bioinformatics.roslin.ac.uk/lawslaws/

13. DrugbankChemSpider PubChem MesylateImatinib Mesylate YLMAHDNUQAMNNX-UHFFFAOYSA-N Are these records the same? It depends upon your task!

14. skos:exactMatch (InChI) Strict Relaxed Analysing Browsing I need to perform an analysis, give me details of the active compound in Gleevec.

15. skos:closeMatch (Drug Name) skos:closeMatch (Drug Name) skos:exactMatch (InChI) Strict Relaxed Analysing Browsing Which targets are known to interact with Gleevec?

16. A lens defines a conceptual view over the data Specifies operational equivalence conditions Consists of: Identifier (URI) Title (dct:title) Description (dct:description) Documentation link (dcat:landingPage) Creator (pav:createdBy) Timestamp (pav:createdOn) Equivalence rules (bdb:linksetJustification) Scientific Lens Lenses 34 in total 7 Public 25 Chemistry 2 Gene

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21. Data Governance Contribution must not be underestimated!!!

22. Alasdair J G Gray A.J.G.Gray@hw.ac.uk www.macs.hw.ac.uk/~ajg33/ @gray_alasdair Open PHACTS contact@openphacts.org openphacts.org @open_phacts

Notas do Editor

Data provided by many publishers: some cover other sets, e.g. ChemSpider Originally in many formats: relational, SD files and RDF Worked closely with publishers getting them to publish Raw RDF Metadata descriptions of their data Links between their data and others
~3billion triples 42GB gzip nquads 400GB uncompressed
Getting this informaiton is still hard and manual! ~3billion triples 42GB gzip nquads 400GB uncompressed
~3billion triples 42GB gzip nquads 400GB uncompressed
API: Complex data interactions/relationships Interactions needed to satisfy use cases Gradually added additional types of data and interactions
Quantitative Data Challenges No standard units Even in curated sources! Feedback issues to data providers
Quality Assurance Validation & Standardization Platform Developed by Royal Society of Chemistry http://bit.ly/NZF5VB
CRS Dataset Generation Validate structure: Source data is messy! Identify common problems: Charge imbalance Stereochemistry Compute physiochemical properties Identify related properties based on structure 17 relationship types
230MB gzipped nquads 2 GB uncompressed 238 Mapping sets 43 data sources 11 predicates
Identity Mapping
Example drug: Gleevec Cancer drug for leukemia Lookup in three popular public chemical databases  Different results Chemistry is complicated, often simplified for convenience Data is messy! Are these records the same? It depends on what you are doing with the data! Each captures a subtly different view of the world
Structure Lens Interested in physiochemical properties of Gleevec
Name Lens Interested in biomedical and pharmacological properties sameAs != sameAs depends on your point of view Links relate individual data instances: source, target, predicate, reason. Links are grouped into Linksets which have VoID header providing provenance and justification for the link.
Lens enables certain relationships and disables others Alters links between the data
Builds on OPS document: Checklist and guidance notes! Covers a wider range of use cases Large community buy in – Including EBI
Builds on OPS document: Checklist and guidance notes! Covers a wider range of use cases Large community buy in – Including EBI
Verifying data Verifying linkages Investigating unexpected answers Not to be

Open PHACTS: The Data Today

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