http://www.flickr.com/photos/alicebartlett/ / CC BY-NC 2.0

• MajorGroove.org pitches itself as a ‘forum for biologists’, which it is, however most of the content currently focuses on X-ray crystallography and associated techniques. It is currently in ‘bootstrap mode’, which means that reputation requirements are a little less strict until the userbase and site activity has grown to a critical size. Is there even a need for a Stack Exchange forum for biological crystallography ? Macromolecular crystallography already has a single, central, de facto standard forum – the CCP4BB mailing list. While it may be antiquated by Web2.0 standards, CCP4BB works well for a lot of people, and there is a huge amount of useful and important information buried in it’s archives. For many crystallographers, it seems CCP4BB would only be extracted from their “cold dead hands”. Despite this, I think the Stack Overflow format will be very beneficial for people new to the field.  As a side note - I discovered MajorGroove via Graeme Winters XIA2 blog right around the time when I was considering kickstarting a “Stack Overflow for crystallography”. At the moment it seems that a small userbase of crystallographers is already established on MajorGroove and there would be no purpose for another near identical forum. Even if questions about other techniques in the biosciences start to dilute out the structural biology, one click on the ‘crystallography‘ tag or the ‘ccp4‘ tag, and you can get straight to the good stuff. (In fact this feature was deemed useful enough by Google that they decided to bless the ‘android‘ tag on Stack Overflow as the official Android Q&A forum).
• NMRWiki Q&A (http://qa.nmrwiki.org/) is a StackExchange-clone for magnetic resonances, mostly focused on NMR, but also open to EPR/ESR and MRI users. It’s not actually running on the StackExchange platform, but uses the open source OSQA / CNPROG clone, built on top of Django. As far as I know, there is no “CCP4BB for NMR”, which makes the NMRWiki Q&A site potentially even more valuable to structural biologists than it’s crystallography centric cousin, MajorGroove. Back when I was doing my PhD using protein NMR spectroscopy as my primary technique, there were very few good resources like this online – I do less NMR these days, but you can bet that I’ll be using the NMRWiki Q&A and it’s associated wiki to refresh my memory and catch up on need methodological developments in the future.
• BioStar (http://biostar.stackexchange.com/), a StackExchange for bioinformatics, computational genomics and systems biology questions and answers. This one is busier and better established than the above mentioned forums, probably by virtue of the fact the bioinformaticians spend more time in front of the computer than your average molecular biologist or structural biologist.
• And, for a bit of fun: Skeptic Exchange (http://exchange.bristolskeptics.co.uk/), which covers rational questions and answers to various topics including pseudoscience, faith healing, the supernatural and alternative medicine.

Want more ? There are a bunch of science related StackExchanges listed under “Science” here: http://meta.stackexchange.com/questions/4/list-of-stackexchange-sites .. and digging back through the FriendFeed archives I see Matt Todd initiated a concise listing (which if I’d seen, I probably never would have started this post).

And now, the latest* news Stack Exchange 2.0 will be ‘free‘. It looks like they are trying to structure the new Stack Exchange ecosystem a bit like the Usenet hierarchy (comp.*, rec.* etc), with a fairly involved discussion, proposal and acceptance process for new sites – it’s unclear yet whether this approach is going to work out better than just open sourcing the whole shebang, but time will tell. My guess is that BioStar, MajorGroove and probably even an incarnation of NMRWiki Q&A will eventually become part of this formalized ecosystem.

On one hand making StackExchange sites free to run is great – it lowers the barrier to entry to allow many more sites to emerge and operate. On the other hand, as we have seen with the acquisition of FriendFeed by Facebook, not having a clear revenue stream can ultimately leave communities  (such as The Life Scientists) without any certainty in a sites future, potentially impacting growth and participation. Personally I’m much more inclined to invest time in a site if it is something like Wikipedia, where I know my contributions are very likely to live on, in some form, for decades (centuries ?) to come. Ideally the archives of these new Stack Exchange sites could become useful online resources for decades to come – but with a single company at the helm and a “Web 2.0 business model”, continued operation for even a decade seems unlikely. The one saving grace: all content on the new Stack Exchange sites will be licensed under a Creative Commons license – so if Stack Exchange itself is acquired and shut down, we will always be able to preemptively leech the archives and provide them online elsewhere. Maybe it’s strange that I’m already thinking about archiving the new Stack Exchange upon it’s demise before it’s even begun, but I think it’s important to take the long term view with our data and recorded wisdom. Unlike when in 1994 when GeoCities (R.I.P) was started, teh Internets is no longer a fad – the hard disks connected to it are fast becoming the sum of all accessible human knowledge, so we’d better make sure we can retain the good bits for a little longer than 10 years.

* – as all too common these days .. I’m a little behind the curve on this one. I meant to finish this post a month ago, but with a busy time pre-holiday, then the actual holiday, a month has gone by.

Like many of my online friends, I blame FriendFeed. I find commenting on a FriendFeed post a much more productive way of having a conversation around some new development sweeping the web than writing a dedicated blog post. Still, despite this being my “year of FriendFeed”, I started writing a few blog posts / articles / essays this year which never made it out of the Drafts folder. There is a positive side to unpublished drafts – they serve to nicely organize some thoughts, even if they are ultimately never shared. Anyhow, it’s time to clean them out and move on – and as part of that process – here are the highlights of my posts that never were.

“Why biohacking cannot come of age”

I wrote quite a long essay around the time that synthetic biology was getting lots of press, and just before DIYbio appeared on the scene (as a side note: the name “DIYbio” is PR genius  – taking the ‘hacking’ out of biohacking to help avoid misinterpretation by the mass media was a smart move). The opening of this defunct post pretty much sums up it’s contention:

“A healthy biohacking ecosystem requires the participation of hobbyists, and will fail to flourish in the same way ‘Information Technology’ and ‘The Internet’ have flourished if participants remain confined to academic and commercial labs.”.

The old Silicon Valley example (myth?) of the two guys, both called Steve, launching technology from their garage was cited. I then went on to state the obvious – current regulatory frameworks surrounding recombinant DNA and genetic modification make most serious pursuits by hobbyists acting alone legally dubious. Ultimately, I chickened out and decided it was better left unpublished, but a highly modified version my emerge one day. Key links:

• The case of Professor Steven Kurtz
• “The bio-security framework is going to collapse. — Drew Endy“
• Good chemistry kits are hard to buy these days
• The “precautionary principle”

IceCondor – continuous location tracking

Around the end of 2008 when I was momentarily in employment limbo, I began to write an Android mobile geolocation app and started playing with Don Park’s IceCondor. I decided to highlight it with a blog post, but never got around to ultimately publishing it. Essentially, IceCondor is/was a location sharing app, but unlike BrightKite, FireEagle, Google Latitude, Foursquare (& Twitter, these days), IceCondor does continuous location tracking. eg, your GPS location can be shared every 30 seconds via 3G on your Android device (although high frequency updates eat the battery quickly, so lower frequency updates are more practical). IceConder (initially) didn’t include any privacy settings – all locations were openly shared online, with individuals identifiable via their OpenID. As far as I could tell, the only two individuals that gave it any significant use were Don Park, and myself. My main point for writing about IceCondor was to argue that wilfully sharing your location in realtime and opting out of some privacy may actually be safer that not sharing your location. I believe that for most people there is more chance of being randomly mugged than actively stalked, so letting people know where you are is a Good Thing(tm). Don has since changed the focus of IceCondor (at least in the version on the Android Market) to be a simple GeoRSS reader. I get the impression that he is working on other things these days, but the original software and it’s potential uses are pretty cool – it lives on at GitHub, and I notice he has been poking at it again recently.

(Re)-discovering Pymol

I get a little sad thinking about this particular post. I’d planned to write about some lesser known functions of Pymol that I had recently discovered (namely the -p, -R and -G commandline options), but never got round to investigating them thoroughly enough to warrant a blog post. Some time after starting the draft and then leaving it to languish, the author of Pymol, Warren DeLano, tragically passed away at the age of 37. I never met Warren, but I was a grateful user of his amazing software, and I wish his family well over what must have been a difficult festive season without him.

Protein sequence clustering tools

I planned to write an article comparing protein sequence clustering tools. I still might, but here is the unannotated list so far:

• CLANS
• Blastclust
• CD-HIT
• MCL / TribeMCL ( http://micans.org/mcl/ )
• an excellent list of sequence clustering tools on Wikipedia

Spam as an indicator of social network success ?

Surely there are already multiple essays on this topic by social media and internet culture enthusiasts. I’ve only searched briefly. The idea for this post was stimulated by some advertising that was sent to me via my delicious inbox (On an unrelated note: 2009 was the year I moved to Diigo for social bookmarking). This spam wasn’t as indiscriminant as the usual “enlarge your whatever” you expect by email, but some fairly niche advertising for cheminformatics software …  while probably not spam in the strictest sense, it was nonetheless “spammish” in nature since numerous others were also targeted (via delicious “for:” tags). Neil Saunders also noted that he had seen some spam on Slideshare. Key ideas:

• Is spam an indicator of social network self-sustainability, ‘viral growth’ or ‘critical mass’ ?
• or is it an indicator that ‘stationary phase’, the slowing of growth, has begun ?
• Just as “the network interprets censorship as damage and routes around it“, does spam “interpret small networks as inviable, and avoid them” ?
• How does this relate to the cost / reward – ie. cost of spamming vs. potential audience – see Economics of Spam.

Synthetic biology 4.0: reflections on the state of play

This is one I’d totally forgotten about until now, from late 2008, written shortly after I’d attended the Synthetic Biology 4.0 conference in Hong Kong. It contained the picture below, along with lots of opinion.

Gartner’s hype cycle

On re-reading it, I’ve decided to make some final changes and retro-publish it anyway. It’s not the most coherent article I’ve ever written, and some of my opinions have probably changed in the last 12 months, but I couldn’t bring myself to just trash it.

More thoughts on Biopython from a non-contributing shoegazer

This post was a little bit of a rant/analysis that probably better belongs on the Biopython development mailing list. It was started by Chris Lasher lamenting that academic researchers are rarely encouraged to work on tools like Biopython, and continued summarizing various peoples ideas on why Bioperl still remains in dominant usage, over Biopython. My main conclusion (if there was one), was that the Biopython team over the years has tended to do a good job by maintaining a high standard of quality by deprecating unused, undocumented and unit test-less code … but sometimes perfect has been the enemy of good. Plus, Bioperl had a head start

The Golden ratio in molecular biology ?

This one has been sitting in Drafts since 2007. I really should just dump it, but the idea still appeals to me. The Golden ratio does appear in nature at the macroscopic level, so why not at the micro- or nano- scale ?

Here’s a choice quote from my notes that may explain why I haven’t yet finished this post:

I think one difficulty in searching for this type of stuff is that the Golden ratio is popular with those into “numerical mysticism”, so if PubMed gives you naught, you have to wade through a lot of kooky pseudoscience in the Google hits before you find the “real science”.

Maybe it will see the light of day in 2010, you never know.

Computation in a single cell … how many logic gates would fit ?

Well … you tell me

Here is my take on an approach to user submitted content to Uniprot. Essentially users should be able to add/change annotations piecewise, directly via the standard Uniprot web page for each protein record. These changes would ‘go live’ immediately, but since a large part of the value in Uniprot lies in its curation by expert annotators, the interface would also provide a very clear separation between user-submitted ‘uncurated’ annotations and the current expertly curated annotations.

I’ve made some mockups of how some parts of the UI may look in my little fantasy world:

• User login box at top (eg, OpenID)
• A History tab at the top.
• User submitted changes tab.
• Maybe a “Discussion” tab, ala Wikipedia (not pictured).
• Each field, or block of related fields, would have an Add/edit button at the top right of the block. (I’ve chosen the Universal Edit Button as an example)

Aftertought: Maybe putting these features under tabs isn’t quite the best place, since the existing tabs are ‘actions’ that can be taken rather than ‘extra info’ to be viewed. This UI detail could certainly be refined.

This proposal has many wiki-like features (history, attribution, open editing, curation by trusted users and potentially page/section locking) but doesn’t really fit my definition of a wiki since the input format is not free-form wiki-text, but is instead constrained by the interface to enforce the submission of (mostly) structured data (eg, a traditional data entry into an HTML form, or in-line editing of fields).

Any authenticated user would be able to add or edit fields by clicking on the “Add/edit annotations” button associated with that block (see mockup above). They would then be sent to a page where they can click to edit a particular field (in this case a point mutation and associated change in function), or click “Add new” to add a new mutation field and fill out the details (I didn’t make a mockup picture for this .. use your imagination). They also must specify one of the standard “evidence codes” from a dropdown box for each change/addition, including the PMID of a publication if relevant. User submissions are automatically flagged with some type of ‘user submitted’ flag too, and a username. Homologs (from UniRef clusters) could also be listed here to remind the user that certain annotations might need to be propogated to other members of the same family, if required (otherwise the curators would do this part, when applicable, for the next Uniprot release). For all I know, Uniprot may already have an interface similar to this, already in use by their professional curators. In effect, I’d like to see the 37signals “One interface” dictum applied.

User submitted changes would not automatically go live on the main Uniprot record page, but can be seen by clicking the “User submitted” tab at the top. Alternatively, the user submitted annotations could be put at the bottom of the page, like most blog comments, but clearly differentiated from the curated data by colour and other visual queues. The REST API could be told to include/exclude uncurated user annotations in responses by an extra query flag in the request (eg &userannotations=true). Uniprot curators can periodically review user submitted annotations and integrate them into the official Uniprot release as they see fit.

Under the History tab, the history of changes to that Uniprot record, both by user submitted changes and by Uniprot release would be available. This functionality is already mostly available under “Entry history->Complete history” at the bottom of the page, but user submitted annotations would also be included here with appropriate diff colouring (eg, coloured differently to curated changes, until they are officially accepted).

Providing user pages at a URL: http://www.uniprot.org/user/some_sensible_username with an associated RSS/ATOM feed would encourage participation by highlighting individual user contributions, and potentially allow a Wikipedia-like community of expert/fanatical annotators to emerge.

The Discussion tab would be used in much the same way Wikipedia Talk pages are – passive users, contributors and curators would be able to discuss the finer details of any submitted annotations. I’m of two minds about this one, since anyone who has read Wikipedia Talk pages knows things can get quite ugly there sometimes. On the other hand, the communication it allows would be important for building a community of annotators and helping clarify contributions.

PS: I’m a Uniprot fanboy. Can you tell ?

I think comfort with meaninglessness is an important skill in studying biology (and probably other sciences too). Many times, during the description of a system, various acronyms are thrown about as labels for entities (or ‘actors’) in that system. An important skill of the scientist is being able to follow how all the actors in the system relate to each other, without necessarily knowing anything about the specific properties of those actors. There are lots of protein and gene names which often bear very little meaning relative to the biological entity that they label, and fixating on what ‘the name’ means simply distracts from the true nature of the entity.

Example: TPR proteins are a superfamily of protein fold, often involved in protein-protein interactions. I have sometimes been asked at poster presentations, or the occasional talk: “What does TPR stand for ?”. “TPR” is an acronym for “tetratricopeptide repeat” … you may be able to glean from that expansion that the protein fold is composed of repeat sequences 34 amino acids long – but that is only one small aspect of the family, and isn’t the important point. Yet many molecular biologists appear uncomfortable with an “undefined” acronym, and insist on having it expanded to reveal the full name. TPR is just a convenient label for the superfamily … it could equally have been called GrratBlat or 5450520A, it would still be the same thing. The point is, you shouldn’t have to ask what TPR stands for. Sure, it’s a curiosity, and some protein names can be amusing (Sonic Hedgehog, or “Just Another Kinase” come to mind), it may also contain some meaning, but first and foremost it’s a label – something to link the entity to all the other descriptive information about it’s structure, function, localisation and regulation. Like many classes of protein, the original name was given at a time when little was actually known about the thing, and typically the meaning embedded in the name should be ignored lest it bias our interpretation about what that protein really does.

Summary of opinion: Molecular biologists should become comfortable with the notion that a name is just a label – meaningless without the associated metadata.

All of this is probably second nature those who studied philosophy (or computer science, or linguistics) … I’m guessing it is an issue of semantics. I really should have taken some of those subjects back in my undergrad days …

1. What is your blog about?

I often ask myself this question. Being a scientist, I always feel like it should be about science, and the various biological systems I work with. I reality, it is about programming, bioinformatics, the web with the tiniest bit of structural biology thrown in. I used to blog about Linux related things occasionally, but I split that off into another blog (which gets 10 times more traffic ).

2. What will you never write about?

I’d rather not rule anything out. I’ve avoided politics, largely because all my “blogging peers” also largely avoid it. Sometime I wonder why, but I think I’m beginning to understand; blogging about political issues may seem important, but eventually it just becomes a constant ‘rah rah rah‘. That would get boring. So I’m saving it for when it really matters (the proposed ‘filtering’ of internet connections at the ISP level by the Australian government is coming close. There, I did it ).

3. Have you ever considered leaving science?

Yes. But I did cheat and read Bora’s answer to this one (by accident).
I think it would be impossible to leave ‘science’ unless I became permanently brain damaged or something. Leave research … sure, it crosses my mind from time to time.

4. What would you do instead?

I’d try my best to get a job in video game development, or web application development (or both … developing a web-based game !). I’d try to work my experience as a scientist into this endeavour. Not sure how successful I would be, but I’m trying to slowly build up a portfolio, just in case

5. What do you think will science blogging be like in 5 years?

I think research groups will be tied together more and more by their blogs. A bit like the way RRRosie Redfield runs things. Not necessarily blanket Open Notebook Science (give it 10 years), but certainly a greater level of open, public communication of ideas.

6. What is the most extraordinary thing that happened to you because of blogging?

It’s not that exciting, but it’s the best I’ve got; I was contacted by an real journalist with some questions about the role of cloud computing for academic research. I didn’t really feel qualified to comment a length, since it largely stemmed from a ‘what if’ type idea I blogged about. But I gave my perspective anyhow.

7. Did you write a blog post or comment you later regretted?

Yes, quite often. I have a tendancy to always be in a hurry when commenting, coupled with a desire to not be overly self-censoring. I’m never trying to sound like a kook, troll or A-hole … it just turns out that way sometimes. Personally I find it takes a long time to write a concise, clear comment, beyond “LOL”. The FriendFeed comment box is always too small for me, but that’s a good thing, since it forces me to summarise.

8. When did you first learn about science blogging?

Not exactly sure, but I think it was via nodalpoint.org the bioinformatics weblog. I wrote one or two posts for nodalpoint. Around the time I was writing my PhD thesis, a few netziens of nodalpoint started blogs (or I discovered that they had blogs outside of nodalpoint). The most influential for me were Neil Saunders’ “What You’re Doing is Rather Desparate”, and well as Pedro Beltrao’s “Public Rambling” and Duncan Hull’s “O’Really ?”. I thought, “I can do that”, so I started a Blogger blog. One year later I moved to a self-hosted WordPress blog. But lets not get side tracked with a boring meta-discussion about blogging software.

9. What do your colleagues at work say about your blogging?

Mostly, they are unaware. Occasionally I have dropped a comment about “my blog”, but no one has really jumped on it. Which is fine, since I don’t really blog about anything that most of them have any interest in.

Well, that’s it. Now to read everyone else’s responses ……

(Originally via Blog Around the Clock [Bora Zivkovic], via FriendFeed)

I recently needed to make a simple, two dimensional figure of a beta-barrel membrane protein. I went hunting for programs that might take a sequence and/or structure and produce a pretty looking diagram to save me constructing everything by hand. Here are two I found and tried.

TMRPres2D

Ioannis C. Spyropoulos, Theodore D. Liakopoulos, Pantelis G. Bagos and Stavros J. Hamodrakas TMRPres2D: high quality visual representation of transmembrane protein models Bioinformatics. 2004; 20: 3258-3260. (link)

Pros:

• Cross-platform (Java)
• Simple interface, GUI (zero learning curve)
• Lots of input options (defines transmembrane regions directly from SwissProt or PIR annotations online, takes input from several transmembrane region predictors)
• Lots of output formats and options (Postscript, gif, jpg, png, svg, bmp)
• Various colouring options (hydrophobicity, charge, “printer friendly”)
• Makes reasonable looking diagrams of helical transmembrane proteins

Cons:

• Doesn’t handle beta-barrel membrane proteins gracefully (strand drawing is overlapped, messy).
• The membrane is assumed to be a eukaryotic plasma membrane, with labels “cytoplasmic/extracellular” (which should be, for instance, “extracellular/periplasm” for a bacterial outer membrane protein). This is easily changed on the diagram with external editing.

TeXtopo

Beitz, E. (2000), TeXtopo: shaded membrane protein topology plots in LaTeX2e. Bioinformatics 16: 1050-1051. (link). See the original publication or Professor Eric Beitz’s site for a better example than my image.

Pros:

• Beautiful, clean, publication quality diagrams, courtesy of LaTeX
• Multiple input options (Swissprot format, PHD, HMMTOP, user defined)
• Multiple sequence annotation options including colouring by various physiochemical properties (hydrophobicity, charge), sequence conservation or user defined schemes.
• Will depict membrane embedded half-loops and lipid anchors.
• Versatile output (Postscript, pdf, dvi, basically anything that LaTex can be rendered as)
• Also can generate attractive looking helical wheel plots
• Did I mention the output is clean and looks great … ?

Cons:

• Steep learning curve for the uninitiated, despite extensive documentation (ie LaTeX code, no GUI)
• No support for beta-barrel membrane proteins

If I ever need to make a 2D diagram of a helical membrane protein for a publication, TeXtopo would be my first choice. For quickly getting an overview of some transmembrane prediction results or a protein with defined tranmembrane regions in Uniprot, TMRPres2D is the quickest and easiest method.

In the end, since neither program would do a decent job at cleanly depicting the strands of a beta-barrel in a simple 2D plot, I ended up coding my own hackish solution (svg_barrel.tar.gz or svg_barrel_gui_win32.zip) using Python and a tweaked version of SVGdraw.py. This allowed me to generate some SVG graphics to use as a starting point, and then hand edit the result in Inkscape to align strands to loosely match the real hydrogen bonding patterns. I also added some simple beizer curves for the loops, since neat placement of loop residues was the tricky part that I decided I didn’t have time to tackle.

Here’s the end result, after hand editing:

And here is the 3D version, as a point of reference:

The 2D vector diagram could do with some work to aid in a more accurate representation (unfortunately ‘flat’ views of a 3D barrel always have to make some compromises), but it does the job. The goal was to keep it simple … simple it is. One day I may extend this code to actually use known structure coordinates to automatically align the strands (saving tedious manual alignment), and write some code that properly lays out the loops.

Anyone know any other programs of similar functionality I’ve missed ?

There were the usual complaints on Slashdot that FoldIt doesn’t have a Linux version. Well, I’m happy to report that it seems to run alright using Wine (on Ubuntu Hardy Heron). I couldn’t log in to try the competitive puzzles, but I suspect the server is just in the midst of a Slashdotting. I’ll try later.

From the FoldIt FAQ:

Can humans really help computers fold proteins?
We’re collecting data to find out if humans’ pattern-recognition and puzzle-solving abilities make them more efficient than existing computer programs at pattern-folding tasks. If this turns out to be true, we can then teach human strategies to computers and fold proteins faster than ever!

Not sure where I saw it, but I remember reading an argument that the future of crowdsourcing would be to not just blindly trust the whole crowd, but also identify experts in the crowd and weight their predictions more strongly. I’d say this is will be the case with ‘manual’ protein folding – just like some players become l33t at first-person-shooters (like my favorite, RTCW: Enemy Territory which depsite enjoying, I’m not so l33t at), and could beat any AI player that doesn’t cheat… some people will probably become pretty good at folding up proteins. Maybe FoldIt will identify them, and they can make their gaming skills useful, and teach their tricks to software to automate the process. Or maybe it will just remain a fun-ish puzzle game

The way Lars’ BuzzCloud is constructed by extracting phrases ending in -ics, -ology, -omy, -phy, -chemistry, -medicine, or -sciences etc reminded me of a stupid little CGI application I wrote a few years back … the Biotech company name generator. When you take common prefixes like “Gene-”, “Pept-” or “Chemi-” and suffixes like “-omics” or “-agen” etc, it’s amazing how often Googling the name turns up a real honest-to-goodness biotech company.

Feel free to comment on any “biotechie” suffixes and prefixes that I should add … the hardcoded list in the script isn’t that long.

For the purposes of full disclosure, this pipette has NOT resulted in data that has made it into Nature or Science (and in hindsight then the Cell paper may be considered to be a fluke). However, we choose to believe that that is the fault of both the editorial staff of these journals, and many a short-sighted peer-reviewer, rather than the pipette itself. Nonetheless, you may want to calibrate the pipette upon its arrival.

Fluke or not, a sentient pipette that produces Cell papers has got to be worth more than the mere $48 it’s currently sitting at ! Sadly (or maybe happily for them), it looks like the seller is leaving bench science and selling up their gear. I’ve no idea who it is, but they live in the same suburb as me, so there’s a good chance this is a fairly well-published senior scientist that I’ve crossed paths with at some stage. Also, don’t miss the (*) footnote at the bottom of the auction info about impact factors …