We have now integrated this curation badge into the author and community pages on the (not so) new WikiPathways website too. Authors can now find curation reports for pathways they started and also for the community pages:
A second new feature is the “Citations” tab on both pages, which link to Europe PMC with a dedicated search for articles mentioning those author or community pathways:
We hope you like it!
]]>Is that a big issue? Hell, yes. Where do you think the FAIR ideas came from? And why FAIR in ten years has not brought about the change it was hoping for?
For me, my fascination for curation started as a student, around 1995, with the Dictionary on Organic Chemistry. At that time, my interest came from wanting to learn about chemistry and biology. During my M.Sc. and PhD, it was obvious how essential it was to derivating correct scientific conclusions from your experiment. Data, knowledge, and software alike, imo. And because curation is expensive, not having to repeat it, I prefer to do it as Open Science.
Of course, curation has been part of doing science, but to a large extens is separate step from doing science. It is done by database developers, librarians, and chemo- and bioinformaticians. For example, Chemical Abstracts Service (CAS) started over 100 years ago and started indexing chemical structures in 1965. The curation is an ongoing process, also for old records.
Biocuration is getting more and more attention:
The recognition and rewarding by having the International Society for Biocuration (ISB, Scholia page) should not be underestimated (doi:10.1038/455047A). Their Annual International Biocuration Conferences have been running since 2005. And with their awards, they give the biocuration work recognition and, literally, rewarding:
I don’t have a good curation CV. For a large extend because the curation has been part of a study. The curation itself does not get recognized, and only the journal article does. With datasets slowly getting more recognition, so does data curation, but data curation is not really part of how we do FAIR at this moment, and via this route not getting the attention it gets.
But since I have been updating my CV anyway, I dug up some curation I am proud of:
I am also curation my blog, which was originally in blogger.com but being ported to Markdown with extra annotation. That includes updating URLs and annotation of blog posts with chemicals, grants, and intention-typed citations.
Of course, I have my Wikipedia edits, and contributed to projects like Bioregistry.io, FAIRsharing, regularly submit missed mentions to Altmetric.com, etc. There is a long tail in curation. And there is a lot of curation hidden in my literature list.
And that long tail matters to me. I want every researcher to pick up the challenge to curate their own research output. Put your experimental data in databases, add important provenance, get the details rights. This is essential to reduce the cost of doing research, and that is more important than ever.
BTW, I must note that our bioinformatics team colleagues too have done a tremendous amount of biocuration, in WikiPathways (Denise, Freddie, Susan), in nanosafety (Jeaphianne, Ammar), and in toxicology (Marvin), just to name a few. Often together with B.Sc. and M.Sc. students (which can work really well).
And I hope this makes it clear why I am delighted to was nominated last week for an ISB Excellence in Biocuration Advanced Career Award. The list of past awardees is impressive, as are the other nominations: Laurel Cooper, Oregon State University/USA, Steven Marygold, University of Cambridge/UK, Saurabh Raghuvanshi, University of Delhi/India, and Kimberly Van Auken, California Institute of Technology/USA.
It’s an honor to be listed along these other nominees and being nominated is a great recognition! With a thank you to the person who proposed my nomination.
]]>So, when I was asked to work with Chemical Abstracts Service (CAS) on a new, bigger than ever version of Common Chemistry (which started as a project between CAS and Wikipedia), I welcomed the project. I don’t quite remember the first meetings, but roughly my task became to work with the new content and match this against Wikidata and Wikipedia. It aligned well with BridgeDb, Scholia, and our metabolomics research, so I even could find sufficient research time for it. This work is now published in the JCIM: CAS Common Chemistry in 2021: Expanding Access to Trusted Chemical Information for the Scientific Community (doi:10.1021/acs.jcim.2c00268).
Figure 2 from the article. Detailed record for caffeine in CAS Common Chemistry (image: CC-BY).
About Wikidata, the paper writes (CC-BY):
The latest release of CAS Common Chemistry has also supported updates and corrections to CAS RNs in Wikidata and Wikipedia. (22) InChIKeys were calculated from CAS SMILES using Bacting 0.0.31 (23) with the Chemistry Development Kit 2.7.1 (24) and were matched with content in Wikidata. The CAS RNs were then compared. References to CAS Common Chemistry were added for CAS RNs that matched. Mismatches have been shared with the Wikidata and Wikipedia communities so that they can manually review and correct the misleading entries using CAS Common Chemistry as a reference. Because Wikidata also curates identifiers from other data sources, validated CAS RNs in Wikidata may also be used to cross-reference with other resources. Scripts are provided in the Supporting Information.
The alignment is a continuous process, as new chemical compounds get added to Wikidata on a weekly basis. The comparison of Common Chemistry with Wikidata and Wikipedia resulted in a wealth of curation data, e.g. inconsistent CAS numbers linked to InChIKeys, where Common Chemistry had a different match than Wikidata or Wikipedia.
CAS registry numbers were not added to Wikidata in this process, only confirmed or reported as different. The latter allowed manual curation by the community, which it did. Reports look like this. When a InChIKey-CAS RN combination in Wikidata was confirmed, it was recorded as a reference, like this:
Screenshot of Wikidata with two references, one reflecting a confirmation
by the English Wikipedia (potentially the result of the original Common Chemistry
project) and the second as outcome of the now published project.
Thanks to everyone on this project and Andrea Jacobs particularly for leading this open science project.
]]>
It giet oan! That it a Frisian phrase for something unlike is going to happen, like and particularly related to the Elfstedentocht.
ChemCuration 2019 is a go. The website is online, the Twitter account and hashtag are ready, we got a poster prize, and here is the call for posters!
]]>On December 3 the first ChemCuration conference will take place. ChemCuration 2019 is a one day, online-only conference around data curation and curated data in the chemistry domain. During the entire conference day, you can participate by tweeting about the poster that you uploaded, along with the meeting hashtag, and responding to questions about your poster in the 24 hours of the conference day. The poster must be available in an online repository (e.g. Zenodo or Figshare) under the CCZero, CC-BY or CC-BY-SA license prior to the conference.
This is the meeting scope: anything around data curation and curated data of open science data in chemistry. This includes but is not limited to: 1. a new release of curated open data; 2. FAIR metadata around open data; and 3. open source tools for data curation.
How do I participate in ChemCuration?
You can participate in this online poster conference by presenting your poster on Twitter during the conference day. You do this by first archiving your poster via Figshare or Zenodo, with an open license (e.g. CCZero or CC-BY). Then, during the day you tweet an image of (part of) your digital poster with the #chemcur2019 hashtag, a short summary, and a link to your online poster with its DOI. The archived poster should be a regular A0 poster (WxH = 841 x 1189 mm or 33.1 x 46.8 in)Do I need to register?
Registration is not obligatory to participate. However, if you would like to be eligible for a poster prize, then registration is required, by Nov. 30th, 2019. The registration form is found at https://github.com/chemcuration/chemcuration2019/issues/new/chooseMore information can be found on the website (https://chemcuration.github.io/chemcuration2019/) and on Twitter https://twitter.com/chemcuration
Now that the ChemCuration 2019 online poster conference is nearing, and my upcoming talks about chemistry in Wikidata (also needing curation), and the much longer process of curation of metabolite (-like) structures in WikiPathways, I decided that something I tweeted earlier this week is actually quite useful, and therefore something I should really write up in my lab notebook.
Glucuronide is an example (biological) functional group. And there are several databases that represent the stereochemistry now always correct. That is an interoperability (and thus FAIR) problem. Correcting this is not trivial, particularly if you have to redraw the same glucuronide group again and again.
So, not looking forward to that, I invested a bit of time to find a SMILES trick. What if I had a SMILES snippet that I could easily copy/paste and attach to the SMILES of the chemical structure it is attached to? Here goes.
O1[C@H](C(O)=O)[C@H]([C@H](O)[C@@H](O)[C@@H]1O9)O.
I just realized that the original 3 I used can better be
a 9, which is less likely to occur in the SMILES of the rest of the molecule. The period at the end is also deliberate.
That way, I can just copy past the SMILES of the rest directly after that period. Then I get a disconnected structure, but
I only have to put a 9 next to the atom that is binding to the glucuronide. So, let’s see the R group is methane, I get:
O1[C@H](C(O)=O)[C@H]([C@H](O)[C@@H](O)[C@@H]1O9)O.C9
Now, next stop: CoA and other common biological tags.
Found my way back to my room a few kilometers from the San Francisco city center, after a third day at the WikiPathways 2018 Summit at the Gladstone Institutes in Mission Bay, celebrating 10 years of the project, which I only joined some six and a half years ago.
The Summit was awesome and the whole trip was awesome. The flight was long, with a stop in Seattle. I always get a bit nervous of lay-overs (having missed my plane twice before…), but a stop in Seattle is interesting, with a great view of Mt. Rainier, which is also from an airplane quite a sight. Alex picked us up from the airport and the Airbnb is great (HT to Annie for being a great host), from which we can even see the Golden Gate Bridge.
The Sunday was surreal. With some 27 degrees Celsius the choice to visit the beach and stand, for the first time, in the Pacific was great. I had the great pleasure to meet Dario and his family and played volleyball at a beach for the first time in some 28 years. Apparently, there was an airshow nearby and several shows were visible from our site, including a very long show by the Blue Angels. Thanks for a great afternoon!
Sunday evening Adam hosted us for an WikiPathways team dinner. His place gave a great view on San Francisco, the Bay Bridge, etc. Because Chris was paying attention, we actually got to see the SpaceX rocket launch (no, my photo is not so impressive :). Well, I cannot express in words how cool that is, to see a rocket escape the earth gravity with your own eyes.
And the Summit had not even started yet.
I will have quite a lot to write up about the meeting itself. It was a great line up of speakers, great workshops, awesome discussions, and a high density of very knowledgeable people. I think we need 5M to implement just the ideas that came up in the past three days. And it would be well invested. Anyway, more about that later. Make sure to keep an eye on the GitHub repo for WikiPathways.
That leave me only, right now, to return to the title of this post. And below they are, my two contributions to this summit:
Just some bit of cleaning I scripted today for a number of toxicology end points in a database published some time ago the zero-APC Open Access (CC_BY) journal Beilstein of Journal of Nanotechnology, NanoE-Tox (doi:10.3762/bjnano.6.183).
The curation I am doing is to redistribute the data in the eNanoMapper database (see doi:10.3762/bjnano.6.165) and thus with ontology annotation (see doi:10.1186/s13326-015-0005-5):
recognizedToxicities = [
"EC10": "http://www.bioassayontology.org/bao#BAO_0001263",
"EC20": "http://www.bioassayontology.org/bao#BAO_0001235",
"EC25": "http://www.bioassayontology.org/bao#BAO_0001264",
"EC30": "http://www.bioassayontology.org/bao#BAO_0000599",
"EC50": "http://www.bioassayontology.org/bao#BAO_0000188",
"EC80": "http://purl.enanomapper.org/onto/ENM_0000053",
"EC90": "http://www.bioassayontology.org/bao#BAO_0001237",
"IC50": "http://www.bioassayontology.org/bao#BAO_0000190",
"LC50": "http://www.bioassayontology.org/bao#BAO_0002145",
"MIC": "http://www.bioassayontology.org/bao#BAO_0002146",
"NOEC": "http://purl.enanomapper.org/onto/ENM_0000060",
"NOEL": "http://purl.enanomapper.org/onto/ENM_0000056"
]
With 402(!) variants left. Many do not have an ontology term yet, and I filed a feature request.
Units:
recognizedUnits = [
"g/L": "g/L",
"g/l": "g/l",
"mg/L": "mg/L",
"mg/ml": "mg/ml",
"mg/mL": "mg/mL",
"µg/L of food": "µg/L",
"µg/L": "µg/L",
"µg/mL": "µg/mL",
"mg Ag/L": "mg/L",
"mg Cu/L": "mg/L",
"mg Zn/L": "mg/L",
"µg dissolved Cu/L": "µg/L",
"µg dissolved Zn/L": "µg/L",
"µg Ag/L": "µg/L",
"fmol/L": "fmol/L",
"mmol/g": "mmol/g",
"nmol/g fresh weight": "nmol/g",
"µg Cu/g": "µg/g",
"mg Ag/kg": "mg/kg",
"mg Zn/kg": "mg/kg",
"mg Zn/kg d.w.": "mg/kg",
"mg/kg of dry feed": "mg/kg",
"mg/kg": "mg/kg",
"g/kg": "g/kg",
"µg/g dry weight sediment": "µg/g",
"µg/g": "µg/g"
]
Oh, and don’t get me started on actual values, with endpoint values, as ranges, errors, etc. That variety is not the problem, but the lack of FAIR-ness makes the whole really hard to process. I now have something like:
prop = prop.replace(",", ".")
if (prop.substring(1).contains("-")) {
rdf.addTypedDataProperty(
store, endpointIRI, "${oboNS}STATO_0000035",
prop, "${xsdNS}string"
)
rdf.addDataProperty(
store, endpointIRI, "${ssoNS}has-unit", units
)
} else if (prop.contains("±")) {
rdf.addTypedDataProperty(
store, endpointIRI, "${oboNS}STATO_0000035",
prop, "${xsdNS}string"
)
rdf.addDataProperty(
store, endpointIRI, "${ssoNS}has-unit", units
)
} else if (prop.contains("<")) {
} else {
rdf.addTypedDataProperty(
store, endpointIRI, "${ssoNS}has-value", prop,
"${xsdNS}double"
)
rdf.addDataProperty(
store, endpointIRI, "${ssoNS}has-unit", units
)
}
But let me make clear: I can actually do this, add more data to the eNanoMapper database (with Nina), only because the developers of this database made their data available under an Open license (CC-BY, to be precise), allowing me to reuse, modify (change format), and redistribute it. Thanks to the authors. Data curation is expensive, whether I do it, or if the authors of the database did. They already did a lot of data curation. But only because of Open licenses, we only have to do this once.
]]>
Doing searches in RDF stores is commonly done with SPARQL queries. I have been using this with the semantic web translation of WikiPathways by Andra to find common content issues, though sometimes combined with some additional Java code. For example, find PubMed identifiers that are not numbers.
Based on Ryan’s work on interactions, a more complex curation query I recently wrote in reply to issues that Alex ran into with converting pathways to BioPax, is to find interactions that convert a gene to another gene. Such occurred in WikiPathways because graphically you do not see the difference. I originally had this query:
SELECT (str(?organismName) as ?organism) ?page
?gene1 ?gene2 ?interaction
WHERE {
?gene1 a wp:GeneProduct .
?gene2 a wp:GeneProduct .
?interaction wp:source ?gene1 ;
wp:target ?gene2 ;
a wp:Conversion ;
dcterms:isPartOf ?pathway .
?pathway foaf:page ?page ;
wp:organismName ?organismName .
} ORDER BY ASC(?organism)
This query properly found all gene-gene conversions to be fixed. However, it was also horribly slow with my JUnit/Apache Jena set up. The queries runs very efficiently on the Virtuoso-based SPARQL end point. I had been trying to speed it up in the past, but without much success. Instead, I ended up batching the testing on our Jenkins instance. But this got a bit silly, with at some point subsets of less than 100 pathways.
So, I turned to twitter, and quite soon got
three useful
leads. The first two suggestions did not help, but helped me rule out the problem.
Of course, there is literature about optimizing, like this recent paper by Antonis (doi:10.1016/j.websem.2014.11.003),
but I haven’t been able to convert this knowledge into practical steps either. After ruling out these options (though I kept the
sameTerm() suggestion), and realized it had to be the first two triples with the variables ?gene1 and ?gene2. So,
I tried using FILTER there too,
resulting with this query:
WHERE {
?interaction wp:source ?gene1 ;
wp:target ?gene2 ;
a wp:Conversion ;
dcterms:isPartOf ?pathway .
?pathway foaf:page ?page ;
wp:organismName ?organismName .
FILTER (!sameTerm(?gene1, ?gene2))
FILTER (?gene1 a wp:GeneProduct)
FILTER (?gene2 a wp:GeneProduct)
} ORDER BY ASC(?organism)
That did it! The time to run a query halved. Not so surprising, in retrospect, but it all depends on the SPARQL engine: which parts does it run first. Apparently, Jena’s SPARQL engine starts at the top. This seems to be confirmed by the third comment I got. However, I always understood engine can also start at the bottom.
But that’s not all. This speed up made me wonder something else. The problem clearly seems to engine approach to run parts of the query. So, what if I remove further choices in what to run first? That leads me to a second observation. It helps significantly if you reduce the number of subgraphs it should later “merge”. Instead, if possible, use property paths. That again, about halved the runtime of the query. I ended up with the below query, which, obviously, no longer give me access to the pathway resources, but I can live with that:
WHERE {
?interaction wp:source ?gene1 ;
wp:target ?gene2 ;
a wp:Conversion ;
dcterms:isPartOf/foaf:page ?pathway ;
dcterms:isPartOf/wp:organismName ?organismName .
FILTER (!sameTerm(?gene1, ?gene2))
FILTER EXISTS {?gene1 a wp:GeneProduct}
FILTER EXISTS {?gene2 a wp:GeneProduct}
} ORDER BY ASC(?organism)
I’m hoping these two observations may help other with using Apache Jena with unit and integrated testing of RDF generation too.
]]>
If you have the relevant information in the pathway, these pathways can help a lot in helping understanding of what is biologically going on. And, of course, used for exactly that a lot.
Because press releases have become an interesting tool in knowledge dissemination, I wanted to learn what it involved to get one out. This involved the people as PLOS Computational Biology and the press offices of the Gladstone Institutes and our Maastricht University (press release 1, press release 2 EN/NL). There is already one thing I learned in retrospect, and I am pissed with myself that I did not think of this: you should always have a graphics supporting your story. I have been doing this for a long time in my blog now (sometimes I still forget), but did not think of that in the press release. The press release was picked up by three outlets, though all basically as we presented it to them (thanks to Altmetric.com):
But what makes me appreciate this piece of work, and WikiPathways itself, is how it creates a central hub of biological knowledge. Pathway databases capture knowledge not easily embedded an generally structured (relational) databases. As such, expression this in the RDF format seems simple enough. The thing I really love about this approach, is that your queries become machine readable stories, particularly when you start using human readable variants of SPARQL for this. And you can share these queries with the online scientific community with, for example, myExperiment.
There are two applications how I have used SPARQL on WikiPathways data for metabolomics: 1. curation; 2. statistics. Data analysis is harder, because in the RDF world resources scientific lenses are needed to accommodate for the chemical structural-temporal complexity of metabolites. For curation, we have long used SPARQL for unit tests to support the curation of WikiPathways. Moreover, I have manually used the SPARQL end point to find curation tasks. But now that the paper is out, I can blog about this more. For now, many examples SPARQL queries can be found in the WikiPathways wiki. It features several queries showing statistics, but also some for curation. This is an example query I use to improve the interoperability of WikiPathways with Wikidata (also for BridgeDb):
SELECT DISTINCT ?metabolite WHERE {
?metabolite a wp:Metabolite .
OPTIONAL { ?metabolite wp:bdbWikidata ?wikidata . }
FILTER (!BOUND(?wikidata))
}
Feel free to give this query a go at sparql.wikipathways.org!
This papers completes a nice triptych of three papers about WikiPathways in the past 6 months. Thanks to whole community and the very many contributors! All three papers are linked below.
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