Chemistry data: Distortion and dissemination in the Internet Era
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Communication of chemistry in the internet era, while it has improved, remains challenged in terms of the exchange of data in a lossless fashion. While there are moves afoot within the publishing industry to produce “data journals”, including embracing some of the new approaches for making data available to the community, many challenges remain. Chemistry data sharing, at even the most basic level, remains a challenge for many chemistry journals. The vast majority of chemistry data is provided as PDF files or trapped on webpages and therefore not available for reuse and repurposing without a significant amount of effort to extract the data. Some of the responsibility resides with the scientists who need to be educated and encouraged in the adoption of appropriate exchange formats and utilization of online platforms for data hosting and dissemination. There are certain practices which, if adopted, could increase both the availability and utility of data for the community. This includes recognition that data, in itself, has value above and beyond inclusion in peer-reviewed publications, the adoption of standard (not necessarily open) formats, clear data licensing, and distribution of the data across multiple platforms. This presentation will provide an overview of ongoing efforts within the National Center for Computational Toxicology to publish chemistry data, both in databases and associated with peer-reviewed publications, in a manner that makes our data and models consumable by the community. This abstract does not reflect U.S. EPA policy.
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Chemistry data: Distortion and dissemination in the Internet Era
- 1. Chemistry data: Distortion and dissemination in the Internet Era Antony Williams1, Chris Grulke1, Katie Paul-Friedman1, and Emma Schymanski2 1) National Center for Computational Toxicology, U.S. Environmental Protection Agency, RTP, NC 2) Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, Luxembourg August 2018 ACS Fall Meeting, Boston http://www.orcid.org/0000-0002-2668-4821 The views expressed in this presentation are those of the author and do not necessarily reflect the views or policies of the U.S. EPA
- 4. DSSTox_v2 PublicCurated 5. Low 2. Low 6. Untrusted 1. High 3. High 4. Med 7. Incomplete validated Public_Untrusted Public_Low Public_Medium Public_High DSSTox_Low DSSTox_High 7269 16K 33K 101K 590K ~ 310K pending ~ 150K pending ~ 150K substances in top 4 QC quality bins Distribution of curated data
- 5. Record Information Quality Flags 4
- 6. ChemReg Curation The ALANWOOD Pesticide Set 5
- 7. The CompTox Dashboard for Structure Identification by MS 6
- 8. Collaborative Data Curation • Mapping between our data (and websites) has resulted in collaborative data curation • Collaboration with Emma Schymanski re. the NORMAN Suspects Exchange https://www.norman-network.com/?q=node/236 • It has highlighted data quality issues – and these are not unexpected! 7
- 10. Example: NORMAN Priority List 9
- 11. Mapping on Two Identifiers Lookup Based on Name 10
- 12. Mapping on Two Identifiers Lookup Based on CASRN 11
- 14. Example: NORMAN Priority List 13
- 15. Example: NORMAN Priority List 14
- 16. >23 NORMAN Lists Available 15
- 17. Aggregating Data • We aggregate data from various sources • Downloading is “easy” • Databasing is “easy” • Data curation is a laborious and painful task 16
- 18. Online Today 17
- 19. We Curated These Public Data to Build Prediction Models Public data should be curated prior to modeling Different Compounds
- 22. Property Initial file Curated Data Curated QSAR ready AOP 818 818 745 BCF 685 618 608 BioHC 175 151 150 Biowin 1265 1196 1171 BP 5890 5591 5436 HL 1829 1758 1711 KM 631 548 541 KOA 308 277 270 LogP 15809 14544 14041 MP 10051 9120 8656 PC 788 750 735 VP 3037 2840 2716 WF 5764 5076 4836 WS 2348 2046 2010 Curation to QSAR Ready Files
- 23. LogP dataset: 15,809 structures • CAS Checksum: 12163 valid, 3646 invalid (>23%) • Invalid names: 555 • Invalid SMILES 133 • Valence errors: 322 Molfile, 3782 SMILES (>24%) • Duplicates check: – 31 DUPLICATE MOLFILES – 626 DUPLICATE SMILES – 531 DUPLICATE NAMES • SMILES vs. Molfiles (structure check) – 1279 differ in stereochemistry (~8%) – 362 “Covalent Halogens” – 191 differ as tautomers – 436 are different compounds (~3%)
- 24. Workflow Details and Data 23 OPERA Models: https://github.com/kmansouri/OPERA
- 25. Sourcing Chemical Hazard Data 24
- 26. Hazard Data from “ToxVal_DB” • ToxVal Database contains following data: –30,050 chemicals –772,721 toxicity values –29 sources of data –21,507 sub-sources –4585 journals cited –69,833 literature citations 25
- 27. How can we curate our data? • Crowdsourcing is well proven nowadays • Comments can be added at a record level • Submitted comments are reviewed by administrators and responded to 26
- 29. Reviewer comments are public 28
- 30. Comments to date • The majority of comments to date: – Structure and names/CASRN do not match – Add additional synonyms – Request to add specific property data – Structure layout/depiction needs improving 29
- 31. Crowdsourcing Comments Single Cell Commenting added • Highlight an alphanumeric text string 30
- 33. In Vitro Bioassay Screening ToxCast and Tox21 32
- 34. Bioactivity Data • 100s of thousands of bioactivity curves to review • Impossible to review every one manually • Now accepting public Crowdsourced Comments • Public crowdsourcing will not suffice!!! 33
- 35. Internal Review of 25,000 curves 34 Screenshot of entry page for Beta R Shiny Application for NCCT users Brown & Paul-Friedman
- 36. Internal Review of 25,000 curves A “good fit” bioactivity curve 35
- 37. Internal Review of 25,000 curves 36 …and gain-loss fit with a cell viability assay (makes little sense) Single-Point in middle of concentration range drives ACTIVE Hit Call
- 38. Internal Review of 25,000 curves Abnormally High-Noise 37
- 39. Internal Curve Review Results • Internal curve review has resulted in: – Instances of correction of fitting procedures in the ToxCast Pipeline – Identification of issues with source data – Identification of additional flags or filters that could be used, depending on the application of ToxCast data – a beta implementation of quality assurance for HTS data – Brown & Paul-Friedman, Uncertainty in ToxCast Curve-Fitting: Quantitative and Qualitative Descriptors Inform a Model to Predict Reproducible Fits (in preparation) 38
- 40. Chemical structures as text 39
- 41. How do I extract structures? • Copy-Paste doesn’t work • This is not the way a publisher should deliver chemistry. But this is on the AUTHOR! 40
- 43. Try hand-drawing Algal Toxins! 42
- 44. Think of files in multiple formats! • SMILES are hyper-dependent on good layout algorithms. It’s not easy! 43 [H][C@]12OC3(C[C@ @H](C)[C@H]1O)C[C @@]1([H])CCCC4(CC C5(O4)O[C@@]([H])( CC[C@@]5(C)O)CC(= C)CCCC4=NC[C@H]( C)[C@@H](C)CC44C CC(=C[C@]4([H])[C@] 2([H])O3)C(O)=O)O1
- 45. Names and CASRNs are NOT structures • In our domain most chemicals are text – chemical names and CAS Numbers 44
- 47. Active vs Deleted CASRN 46
- 48. Tricky mapping by CASRN This one has 316 Deleted CASRN 47
- 49. PLEASE use our curated data 48
- 50. Thirty Years of Experience • What has changed? – Cheminformatics has progressed – The internet proliferates data access – Standards have progressed (InChI) and are improving – Anybody can access/download millions of structures! • What hasn’t changed? – Minor progress presenting structures in publications – Delivery via PDFs still dominates – Mandates on scientists are very unlikely 49
- 51. Conclusion • The CompTox Dashboard provides access to data for ~760,000 chemicals • Sourced and aggregated data from multiple resources • Data quality is very random, source-to-source. • We work hard to curate data - PLEASE USE IT! • Our data is not perfect - we have a long way to go! 50
- 52. Contact Antony Williams US EPA Office of Research and Development National Center for Computational Toxicology (NCCT) Williams.Antony@epa.gov ORCID: https://orcid.org/0000-0002-2668-4821 51
Editor's Notes
- Comments from Katie: In terms of verbal comments, you might note that we are simply reviewing the uncertainty related to the curve-fitting itself. Sometime people conflate uncertainty in response, e.g., assay interference due to autofluorescence or cytotoxicity, with curve-fitting uncertainty. These are two distinct sources of uncertainty. An autofluorescent compound should produce a beautiful curve in an assay that measures fluorescence as an indirect marker of some bioactivity. These curves will get an “active” score. When filtering the data for use – say you only want to know the chemicals that definitively activate some receptor – then you might want to layer in not only the reproducibility of the curve fitting, but also other information such as the cytotoxicity burst, information on autofluorescence or nonspecific protein inhibition, etc. This is probably elementary to you, but it is a point I like to make because so many people think that we should auto-filter data for these different sources of assay interference. I am okay with pre-set options to select for filtering, but different applications will require different levels of stringency in terms of filtering.