Monday, 24 August 2015

Take 3 for the environment

One of the organisations represented at last week’s Oceans of Plastic event at Taronga Zoo was Take 3:

The ‘Take 3’ message is simple: take 3 pieces of rubbish with you when you leave the beach, waterway or… anywhere and you have made a difference.

Marine debris, particularly plastic, has a disastrous impact in our oceans on marine life and, ultimately, us. We can greatly reduce the amount of marine debris in our oceans by preventing it from getting there in the first place! We encourage people to Refuse disposable plastic, Reduce, Re-Use, Recycle and Respond by picking up rubbish.

We want to educate the world about this complex problem by inspiring simple actions.

It really is a simple idea that can make a big difference: visit the Take 3 website to find out more. There’s also a bunch of videos at the site if you’re not sure why plastic is such a big deal.

Friday, 21 August 2015

Bioinformatics is just like bench science and should be treated as such

A bad workman blames his tools. A bad life scientist blames bioinformatics. OK, so that’s a little unfair but so is the level of criticism levelled at bioinformatics by people who should know better. If you are a bioinformatician, it is inevitable that you will run up against the question of whether you ever do “real” science.

If you are unlucky, it will be as blunt as that. At the end of a bioinformatics seminar earlier this year, someone actually asked (in what was meant to be a good-natured way): “Is bioinformatics real? I give the same data to two different bioinformaticians and get completely different answers!” Often, it is is in the subtle form of: “are you going to validate that in the lab?” - as if validating it another way would not itself be valid.

If you are a bioinformatician and are asked a question like that, the correct answer is: it’s as real as [insert appropriate “wet” discipline of choice]. For bioinformatics is science and like all science it can be done well, or it can be done badly. It can generate meaningful results, or meaningless results.

If you want to get meaningful results, you have to treat it like a science, rather than a “black box” of magic. What do I mean by that? Here are my not-quite-buzzfeed-worthy, “8 shocking ways that bioinformatics is just like bench science”:

1. Experience and Training. You wouldn’t hand someone a wet lab protocol for, say, Southern blots, give them a key to your lab and say, “off you go” without first giving them some training and, preferably, the opportunity learn from someone who already knows the procedure. If you do, expect bad results. Bioinformatics is no different. Just because a two year old can work a computer these days, that does not mean that bioinformatics is easy. A two year can also press “Start” on a PCR machine.

2. Optimising your workflow. If you were doing a PCR, you wouldn’t just find a random paper you like that also did a PCR, copy the Mg2+ concentration etc. and then bung it in PCR machine and run the default cycle. Likewise, you should not just stick your data into a bioinformatics program and automatically expect it to do the right thing. Just as to be a good molecular biologist, you need to be (or know) someone who knows a bit of chemistry to understand what’s going on, to be a good bioinformatician, you need to be (or know) someone who knows a bit of molecular biology (and chemistry! and sometimes physics) to understand (a) the data you are putting into a program/workflow, and (b) what the best way to process that data is. If you make the wrong assumptions of your data, you will get the wrong answer. (And if different people make different assumptions, they will probably get different answers.) Computers just do what they are told - don’t blame them if you tell them to do the wrong thing. (It is also important not to get “target fixated” on perfect optimisation; just like for bench science, the performance of your bioinformatics workflow only needs to be as good as your experiment/question demands.)

3. Planning. You wouldn’t start a bench experiment without planning it first. Just because bioinformatics is not time-dependent, that doesn’t mean that you shouldn’t plan your analysis before you start. Know what your final output is going to be and work backwards. Making decisions as you go along is a great way to make bad decisions. Sure, have a play to work out how things work but then go back and do it properly from beginning to end.

4. Lab notes. You wouldn’t just stick a tube labelled “20/8/15 mouse 3 PCR” in the freezer and expect to remember what it was and how it was made 3 months later. Instead, you would (hopefully!) keep a meticulous record of primers and reaction conditions etc. in your lab book. Bioinformatics needs the same record-keeping mentality. Program version numbers, dates and settings are important. Write them down. You will almost certainly end up running an analysis more than once, and it won’t always be the last run that you end up using. You do not want publications to be held up because you are having to re-run your bioinformatics just to work out what settings you settled on.

5. Labelling. Even with a well kept lab book, you wouldn’t store samples or extracted DNA in tubes labelled “tube 1”, “tube 2” etc. for every experiment. If someone rearranges your freezer - or there is an emergency freezer swap following power/equipment failure - you could quickly get muddled up. Likewise, don’t call your files things like “sequence.fasta”. You’re just asking to accidentally analyse the wrong data. Include multiple failsafes so that if you enter the wrong directory, for example, your file names won’t be found. (A pet hate of mine is bioinformatics software that outputs the same generic file names each time it is run for lazy scripting.)

6. Reproducibility. Bioinformatics is - or should be - extremely reproducible in a trivial way. If you put the same data into the same program with the same settings, you should get the same answer. In this sense, it should have the edge over bench work - you do not need to repeat experiments. Right? Well, not really. Just because it should be consistent in how it goes wrong, you cannot be sure that a bioinformatics tool is not getting confused by some subtle nuance or peculiarity of your data. Try with another tool that does the same job, or change a setting that should make no difference, and check that you get qualitatively the same answer. (Better still, try changing a setting that should make a predictable difference and make sure that it does.) Just as you can get a misleading lab result if you mislabel your tubes or add the wrong buffer, you can get a misleading bioinformatics result if you mislabel your data or use the wrong parameter settings.

7. Validation. Bioinformatics often receives a certain amount of flak that it is not “real” and everything needs to be validated. This is true, up to a point. The forgotten point is that nothing is real and everything needs to be validated. In the lab, you rarely actually measure or observe something directly - you are inferring reality from things you can measure (e.g. fluorescence) based on what you think you know about the system (e.g. what you’ve labelled) and certain assumptions (e.g. lack of off-target binding). You then have to perform additional experiments - and/or bioinformatic analysis of your data - to test that your assumptions appear to be good and that there are not alternative explanations for your observations. Bioinformatics is no different. NO different. You make assumptions and you make inferences based on observed outputs. These assumptions and inferences need to be tested. This might be by “validation” in the lab. It might be by independent analysis of other data. The only reason the former is more common is that one often needs to generate new data, which clearly bioinformatics cannot do. However, if the data already exists, there is no reason why bioinformatics cannot be used to validate other bioinformatics, or even bench experiments.

8. Limits. Regrettably, bioinformatics is not a magic wand. (Sadly, we are not bioinformagicians.) It cannot correct poor experimental design. It cannot overcome a lack of statistical power. Just like at the bench, if you design an experiment poorly, include confounding variables or overlook covariates, you might not be measuring what you think and/or you might not have any signal from your analysis. It is tempting to think that bioinformatics is more limited that bench science because we cannot collect our own data, but this would be wrong. Bench data is the raw material on which bioinformatics is performed. We can collect new data from other data - much of sequence analysis is doing just this. Of course, if our particular study focus of interest has no data, we need to generate it. But if you want to study the affect of a certain drug on a certain cell line and either/both do not exist, you have to generate that too.

So, what can we do about it? Bioinformaticians have to take some of the responsibility, largely because we are the ones that write software that perpetuates the myth that understanding parameters is not important. What do I mean by that? Well, often the documentation or “help” for bioinformatics tools is poorly written and poorly maintained - if it exists at all. When it does exist, it is usually written with expert users in mind. The novice is flooded with parameters and does not know which ones are important, or when. One solution is to write a series of protocols in the same vein as bench protocols, highlighting when one might want to change certain parameters - and which parameters are most important. (I am no saint in this department, sadly. If nothing else, this post has made me more determined to do better.)

The bottom line is quite simple, though:

Bioinformatics is science. Full stop. It is no better than other science. It is no worse than other science. People do it right. People do it wrong. However, if you are worried that it’s not real, the chances are that either you are doing it wrong, or you have deluded yourself about the “reality” of observations from bench science.

Thursday, 20 August 2015

A handsome beast

Today, I decided to take the day off and went to Taronga Zoo in advance of the Oceans of Plastic event. Like most people with an interest in conservation and animal welfare, I have mixed feelings about zoos. In an ideal world, we wouldn’t need them; unfortunately, we do not live in an ideal world and zoos are an increasingly important bastion of biodiversity.

They are also a place where you can go and marvel at nature. And what a marvellous chap this is:

How anyone can look at that and think, “I really must shoot that” (other than with a camera), is totally beyond me.

Wednesday, 19 August 2015

Oceans of Plastic - Beat the Microbeads (and more)

Tomorrow (Thursday, 20 August 2015 6:00 PM to 8:00 PM (AEST)) there is a Taronga Zoo Science Week event at the zoo: Oceans of Plastic.

I’ll be helping set up through the Sydney Society for Conservation Biology and the event continues the theme of this month’s Conservation Cafe with Prof Emma Johnston, which was on human impacts of marine ecosystems. It was a really interesting morning, and I am looking forwards to learning more.

One thing that struck me that morning was how little I actually knew about the products I used and their impact on the environment. I was shamefully unaware of plastic microbeads in cosmetics like facewash, for example, which are a massive issue.

I’m even more ashamed to say that my (ex-)facewash is on the “Beat the Bead red list” as containing polyethylene (PE) microbeads. I meant to check after the Conservation cafe but completely forgot. This in turn reminded of something else I pondered that day, not just regarding plastic pollution: isn’t it time that we put more pressure on supermarkets to have environmentally aware labelling of products. We already have it for a few things, like tuna (and, sadly, the unhelpful blanket labelling of GM products), but there are so many different considerations - water, energy, waste etc. - that I feel like something more comprehensive is required.

They have asked for questions for tomorrow’s panel, so mine is this:

Could/should we have “environmental impact” traffic lights on goods in supermarkets, akin to the nutritional value traffic lights on food?

I’m not sure if it will get asked but I’ll be interested to hear the panel’s thoughts if it is.

In the meantime, Beat the Microbead have a Warning: Plastics Inside! App. The idea is that you scan or look up your products before you buy. (I tried it on my face scrub and the scan failed but it was in the lookup list.) Alternatively, just go old school and look at the ingredients! (The main ones are: polyethylene (PE), polypropylene (PP), polyethylene terephthalate (PET) and polymethyl methacrylate (PMMA).)

Anyway… if you are in Sydney and looking for a fun and informative (and cheap!) evening, you can get tickets for Oceans of Plastic on Eventbrite.

Tuesday, 18 August 2015

OMICS Group conferences are still to be avoided at all costs

Regrettably, my most popular post of the week is pretty much always: OMICS Group Conferences - Sham or Scam? (Either way, don’t go to one!) [I’d much rather it was one of the evolution posts - maybe Artificial Selection versus Natural Selection.]

A few weeks back, I was contacted by an ABC journalist doing an investigation of OMICS Group conferences, which have been trying to break into the Australian market. The show, Predatory publishers criticised for ‘unethical, unprincipled’ tactics, is fully available (complete with transcript) and an interesting, disturbing and upsetting in equal measure.

In the end, I declined to do an on-the-record interview - as another anonymous academic on the show said:

“Having been duped, it is somewhat embarrassing and you don’t really want to wave that around too much on national radio. Yes, I don’t want to be known as someone who was so easily drawn in.”

Nevertheless, I am still glad that I wrote the post. Predatory publishers and conferences are a scourge on academia and need to be exposed. Hopefully, Hagar Cohen’s ABC show will help. (They seem to know their brand is toxic and now avoid using it in emails - but they are still easy to spot!)

Friday, 7 August 2015

UNSW are robot soccer world champions!

Following the first the first innings performance of their cricket team at Trent Bridge, the Aussies need a sporting success story. Happily, the UNSW Australia Robocup team has been able to provide such a story.

Yesterday’s piece in The Conversation by team captain Sean Harris, How we won the world robot soccer championship, gives an interesting insight into the competition and how our team was able to overcome the Germans in the final.

It makes for strangely compelling viewing. It’s pretty awesome to consider that these are autonomous robots - this is no Robot Wars scenario where the humans are controlling from the sideline. Of course, it is rather unrealistic as soccer: when players fall over (sometimes themselves but usually following contact), they get straight back up again!

After watching the first couple of minutes for authenticity, I recommend dialling the speed up to 2x for a bit more action. Watch for the great Maradona moment by UNSW #3 (screenshot top) at about 21:30 (dribbling round everyone, not scoring with his hand!) followed shortly afterwards by a fantastic long range strike by my personal “robot of the match”, UNSW #5!

Sunday, 2 August 2015

Please sponsor me for City2Surf (one week to go!)

This time next week, I’ll be running the 14km City2Surf fun run.

I’m not going to pretend that I signed up for this “for charity” - it’s a fitness motivator - but, at the same time, I’d love to raise some money for a cause that I care about and will be "running for the panda" to support wildlife conservation.

Before signing up, I’d never run 14km before, and the City2Surf route also includes the notorious 2km “heartbreak hill” in the middle.

Donating to my supporter page will really help me get up that hill!