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Tuesday, September 16, 2014

Do a postdoc with eNanoMapper

CC-BY-SA from Zherebetskyy @ WP.
Details will still have to follow as they are being worked out, but with Cristian Munteanu having accepted an associate professorship, I need a new postdoc to fill his place, and I am reopening the position I had almost a year ago. Do you like to works in a systems biology group (BiGCaT), are pro Open Science, like to work on tools for safe-by-design nanomaterials, and have skills in one or more of bioinformatics, chemoinformatics, statistics, coding, ontologies, then this position may be something for you.

The primary project for this position is eNanoMapper and you will be working within the large European NanoSafety Cluster network, though interactions are not limited to the EU.

If you have interest and cannot wait until the details of the position come out, please send me an email. first.lastname @ maastrichtuniversity.nl. General questions about eNanoMapper and our BiGCaT solutions for nanosafety are also welcome in the comments.

Sunday, September 14, 2014

CDK: Element and Isotope information

When reading files the format in one way or another has implicit information you may need for some algorithms. Element and isotope information is a key example. Typically, the element symbol is provided in the file, but not the mass number or isotope implied. You would need to read the format specification what properties are implicitly meant. The idea here is that information about elements and isotopes is pretty standardized by other organizations such as the IUPAC. Such default element and isotope properties are exposed in the CDK by the classes Elements and Isotopes. I am extending my Groovy Cheminformatics with the CDK with these bits.

Elements
The Elements class provides information about the element's atomic number, symbol, periodic table group and period, covalent radius and Van der Waals radius, and Pauling electronegativity (Groovy code):

Elements lithium = Elements.Lithium
println "atomic number: " + lithium.number()
println "symbol: " + lithium.symbol()
println "periodic group: " + lithium.group()
println "periodic period: " + lithium.period()
println "covalent radius: " + lithium.covalentRadius()
println "Vanderwaals radius: " + lithium.vdwRadius()
println "electronegativity: " + lithium.electronegativity()

For example, for lithium this gives:

atomic number: 3
symbol: Li
periodic group: 1
periodic period: 2
covalent radius: 1.34
Vanderwaals radius: 2.2
electronegativity: 0.98

Isotopes
Similarly, there is the Isotopes class to help you look up isotope information. For example, you can get all isotopes for an element or just the major isotope:

isofac = Isotopes.getInstance();
isotopes = isofac.getIsotopes("H");
majorIsotope = isofac.getMajorIsotope("H")
for (isotope in isotopes) {
  print "${isotope.massNumber}${isotope.symbol}: " +
    "${isotope.exactMass} ${isotope.naturalAbundance}%"
  if (majorIsotope.massNumber == isotope.massNumber)
    print " (major isotope)"
  println ""
}

For hydrogen this gives:

1H: 1.007825032 99.9885% (major isotope)
2H: 2.014101778 0.0115%
3H: 3.016049278 0.0%
4H: 4.02781 0.0%
5H: 5.03531 0.0%
6H: 6.04494 0.0%
7H: 7.05275 0.0%

This class is also used by the getMajorIsotopeMass() method in the MolecularFormulaManipulator class to calculate the monoisotopic mass of a molecule:

molFormula = MolecularFormulaManipulator
  .getMolecularFormula(
    "C2H6O",
    SilentChemObjectBuilder.getInstance()
  )
println "Monoisotopic mass: " +
  MolecularFormulaManipulator.getMajorIsotopeMass(
    molFormula
  )

The output for ethanol looks like:

Monoisotopic mass: 46.041864812

Saturday, September 13, 2014

CDK 1.5.8, Zenodo, GitHub, and DOIs

Screenshot from John blog post.
John released CDK 1.5.8, which has a few nice goodies, like a new renderer. The full changelog is available. Interesting aspect of this release is, that it uses one ZENODO to make the release citable with a DOI. And that is relevant because it simplifies (making it a lot cheaper!) to track the impact of it, e.g. with #altmetrics. And that matters too, because no one really has a clue on how to decide which scientist is better than another, and which scientist should and should not get funding. Where we know peer review of literature is severely limited, we happily accept it to determine career future.

Anyways, so, we have a DOI now for a CDK release. So, everything using the CDK in research can cite this specific CDK release in their papers with this DOI. Of course, most publishers still don't support providing reference lists as a list of DOIs and often do not show the DOI there, but all this is a step forward. John listed the DOI with a nicely ZENODO-provided icon in the release post.

If you follow the DOI you go to the ZENODO website (they effectively act as a publishing platform). It is this page that I want to continue talking about, and in particular about the list of authors. The webpage provides two alternatives. The first is the most prominent one if you visit the page first:


This looks pretty good, I think. It seems to have picked up a list of authors, and looking at the list, not from the standard AUTHORS file, but from the commit messages. However, that is unsuited for the CDK, with a repository history in CVS, via SVN, to Git, but only the latter show up. The list seems sorted by the amount of contributions, but note that Christoph is missing. His work predates the Git era.

The second list of "authors" is given in the bottom right of the page, as "Cite As":


This suggestion is different, though it seems reasonable to assume the et al. (missing dot) refers to the rest of the authors of the first list. In the BibTeX export the full author list shows up again, supporting that idea.

Correct citation?
This makes me wonder: whom are the authors of this release? Clearly, this version includes code from all authors in some sort of way. Code from some original authors may have been long replaced with newer code. And we noted the problem of missing authors, because of the right version control history.

An alternative is to consider this release as a product of those people who have contributed patches since the previous release. In fact, this is something we noted as important in the past and now always report when making a release. For the 1.5.8 release that list looks like:


That is, this approach basically takes an accepted approach in publishing: papers describing updates of running projects involve only the people that contributed to that released work.

Therefore, I think the proper citation for this CDK 1.5.8 release should be:
    John May, Egon Willighagen, Mark Vine, Oliver Stücker, Andy Howlett, Mark Williamson, Sambit Gaan, Alison Choy (2014). cdk: CDK Release 1.5.8. ZENODO. 10.5281/zenodo.11681
Also note the correct spelling of the author names, though one can argue that they should have correctly spelled their names in the Git commit messages. Here are some challenges for GitHub in adopting the ORCID, I guess.

The question is, however, how do we get ZENODO to do this they way we want it to do? I think the above citation makes much more sense, but others may have good reasons why the current practice is better. What should ZENODO pick up to get the author provenance from?

Sunday, September 07, 2014

Open knowledge dissemination (with @IFTTT)

An important part of science is communication. That is why we publish. New insights are useless if they sit on some desk. Instead, reuse counts. This communication is not just about the facts, but also a means to establish research networks. Efficient research requires this: you cannot be an expert in everything or at least not be experienced with everything. That is, for most things you do, there is another researcher that can do it faster. This is probably one of the reasons why many Open Science projects actually work, despite limited funding: they are very efficient.

Readers of my blog know a bit about my research, and know how important data exchange is to me. But similarly, allowing people to know what I do. You can see that in my literature: I strive towards knowledge integration (think UserScripts, think CMLRSS, think Linked Open Drug Data) and efficient methods for data exchange. Just because I need this to get statistically significant patterns. After all, my background is chemometrics primarily. Cheminformatics was my hobby, explaining the mashup.

FriendFeed was a brilliant platform for disseminating research and also for exchange of data. Actually, it is a brilliant platform, but when they sold themselves to FaceBook, it got a lot quieter there. And, as said, communication needs a community, and without listeners it is just not the same. Scientists just moved to different social platforms, and it is no surprise FriendFeed didn't show up in Richard van Noorden's recent analysis. A lot of good things happened on FriendFeed, but one was that it used RSS feeds and users could indicate which information sources they liked to show up there. Try my FriendFeed account. Better even was that listeners could select which of my information sources they do not want to listen to. For example, if you were not interested in Flickr images of Person X, but the others sources were interesting, you just silenced that source. Brilliant!

But this feature of using RSS to aggregate dissemination channels is not repeated by other networks, and If This Then That fills that gap. Unlike FriendFeed it does not aggregate it, but send the items to external social networks (and many other systems), including FaceBook and Twitter. It does a lot more than RSS feeds (e.g. check out the Android app), but that is an important one for me and the point of this blog post.

Actually, they have taking the idea of sending around events to a next level, allowing you to tune how it shows up. An example of that is what you see in the screenshot: I played with how news items from the RSS with changes I made to WikiPathways are shown. After a few iterations, I ended up with this "recipe":


The grey boxes is information from the RSS feed. The first iteration (see bottom tweet in the screenshot) only contained a custom perfix "Wikipathways edit:" followed by the {{EntryTitle}} and {{EntryUrl}}. I realized that would the commit message it would not be fun, and added the {{EntryContent}} (one bot last tweet in screenshot). Then I realized that having "Pathway" twice (once from my prefix, once from the {{EntryTitle}}, was not nice to look at either, and I ended up with the above "Wiki{{EntryTitle}} edit:", as visible in the top tweet in the screenshot. Too bad the RSS feed of WikiPathways doesn't have graphics :(

At this moment I am using a few outlets: I use Twitter to send about anything, like I did with FriendFeed. Sadly, Twitter doesn't have the same power to select which tweets you like to listen to. Not interested in the changes I make to WikiPathways? Sorry, you'll have to live with it. Well, you can also try my FaceBook account, where I route fewer things. But there you can like and comment, but I will not respond.

Anyway, my message is, give IFTTT a try!

Saturday, September 06, 2014

First steps in Open Notebook Science

Scheme 2 from this Beilstein Journal of Organic
Chemistry paper
by Frank Hahn et al.
I blogged a few weeks back I blogged about my first Open Notebook Science entry. The post suggest I will look at a few ONS service providers, but, honestly, Open Notebook Science Network serves my needs well.

What I have in mind, and will soon advocate, is that the total synthesis approach from organic chemistry fits chem- and bioinformatics research. It may not be perfect, and perhaps somewhat artificial (no pun intended), but I like the idea.

Compound to Compound
Basically, a lab notebook entry should be a step of something larger. You don't write Bioclipse from scratch. You don't do a metabolomics pathway enrichment analysis in one step, either. It's steps, each one taking you from one state to another. Ah, another nice analogy (see automata theory)! In terms of organic chemistry, from one compound to another. The importance here is that the analogy shows that there is no step you should not report. The same applies to cheminformatics: you cannot report a QSAR model without explaining how your cleaned up that SDF file you got from paper X (which still commonly is practised).

Methods Sections
Organic chemistry literature has well-defined templates on how to report the method for a reaction, including minimal reporting standards for the experimental results. For example, you must report chemical shifts, an elemental composition. In cheminformatics we do not have such templates, but there is no reason not too. Another feature that must be reported is the yield.

Reaction yield
The analogy with organic chemistry continues: each step has a yield. We must report this. I am not sure how, and this is one of the things I am exploring and will be part of my argument. In fact, the point of keeping track of variance introduced is something I have been advocating for longer. I think it really matters. We, as a research field, now publish a lot of cheminformatics and chemometrics work, without taking into account the yield of methods, though, for obvious reasons, very much more in chemometrics than in cheminformatics. I won't go into that now, but there is indeed a good part of benchmark work, but the point is, any cheminformatics "reaction" step should be benchmarked.

Total synthesis
The final aspect is, is that by taking this analogy, there is a clear protocol how cheminformatics, or bioinformatics, work must be reported: as a sequence of detailed small steps. It also means that intermediate "products" can be continued with in multiple ways: you get a directed graph of methods you applied and results you got.

You get something like this:

Created with Graphviz Workspace.

The EWx codes refer to entries in my lab notebook:
  1. EW4: Finding nodes in Anopheles gambiae pathways with IUPAC names
  2. EW5: Finding nodes in Homo sapiens pathways with IUPAC names
  3. EW6: Finding nodes in Rattus norvegicus pathways with IUPAC names
  4. EW7: converting metabolite Labels into DataNodes in WikiPathways GPML

Open Notebook Science
Of course, the above applies also if you do not do Open Notebook Science (ONS). In fact, the above outline is not really different from how I did my research before. However, I see value in using the ONS approach here. By having it Open, it

  1. requires me to be as detailed as possible
  2. allows others to repeat it
Combine this with the advantage of the total synthesis analogy:
  1. "reactions" can be performed in reasonable time
  2. easy branching of the synthesis
  3. clear methodology that can be repeated for other "compounds
  4. step towards minimal reporting standards for cheminformatics methods
  5. clear reporting structure that is compatible with journal requirements
OK, that is more or less the paper I want to write up and submit to the Jean-Claude Bradley Memorial Issue in the Journal of Cheminformatics and Chemistry Central. It is an idea, something that helps me, and I hope more people find useful bits in this approach.