Gregynog Statistical Conference 2016

The Gregynog Statistical Conference is a long running conference, now in its 52nd year. This conference has been running since 1965. The conference has such a long history that its origins predate box-and-whisker plots, bootstrapping and the R language. But statistics has clearly been relevant and important for the last 52 years and will no doubt remain so for the next 52 years.

Gregynog Hall, where the conference is held every year, is in the heart of mid Wales. It is a beautiful old mansion bequeathed to the University of Wales by the Davies sisters, and now used for conferences, music festivals and educational activities, such as our computer science undergraduate weekends away.

This year the conference main themes seemed to be modelling of epidemics, using variants of S-I-R models, MCMC and Markov models in general. Another topic for discussion was p-values, following the Friday evening after-dinner talk on this subject by David Colquhoun. The statistical power of experiments and meta-studies to combine data from smaller studies was also a recurring theme. Some of the talks I enjoyed were by Ruth King, who described how to include time spent in each state (dwell time) in a Markov model, and Simon Spencer who explained S-I-R epidemic models and went on to use MCMC and importance sampling to estimate his model parameters. Also Chris Jewell, who described the challenges of modelling vector-borne disease outbreaks in cattle in New Zealand, while at the same time providing real-time advice to government on how to manage the course of the disease.

The poster session was a little haphazard. Somehow the posterboards hadn't arrived so posters were bluetacked to the cupboards, blackboards and walls. But the range of topics was good, from Sam Nicholls' work on modelling the metahaplome in metagenomics, to students from Warwick working on the approximation of integration and partial derivatives using Gaussian functions, and a meta-analysis of studies on delayed rewards and delayed penalties (receiving £10 today instead of £20 next week, vs minus £10 today instead of minus £20 next week).

Hopefully another new statistics lecturer will be joining our maths department shortly, as we're recruiting at the moment. Statistics underlies almost every area of research now, particularly in the sciences. We do need to make sure that we keep talking to the expert statisticians regularly.

Lovelace Colloquium 2016

This year's Lovelace Colloquium was held at Sheffield Hallam University, last week (March 31st). Sheffield Hallam proved to be a great venue. It's convenient for most people in the UK to get to, with a smart building right by the train station, providing a large poster-exhibiting hall, a modern lecture theatre and a cafeteria, all next to each other.

Every year the Lovelace is an inspiring event. I've now been (and blogged) in 2012, 2013, 2014 and 2015 and it gets bigger and better each year. I'm particularly impressed by the first year undergraduates who are up there presenting posters alongside everyone else, talking to employers and thinking about their future careers.

I didn't get to attend many of the talks this year because I spent more time on the desk and doing organisational jobs (and fixing my posters-numbering error, oops!). But there were some really strong posters covering a wide range of computer science topics, including several with live Arduino demos. The end of the day panel session featured questions and advice on where the field is going in the future, the pros and cos of a career in industry vs academia, the challenges of running your own business and questions about recruitment.

This year I was also impressed to chat to many interesting people during the evening social, for example Claire and Emily from Relish Learning. After graduating from uni they worked for others until deciding one day that they could do it themselves. They set up their own business in Sheffield, and now provide digital e-learning, for a wide range of topics. They described how they've been recently training people in the Army on how to change the wheel on a tank (imagine animations of the components required, and the order in which to remove parts, etc). They are now keen to help others to succeed, to encourage them to believe that they can and to talk to others about how they did it.

If you are not recommending this event to your women undergrads in computer science, then they are missing out. Poster presenters get expenses refunded and may come away with a prize, thanks to all the sponsors. Employers are keen to meet them, so they will also come away with contacts to help them apply for a job or placement. The photos of the event give a great impression of what it's really like if anyone needs any further reassurance.

Other summaries of the day:

• Sue Beckingham's Storify,
• Hannah Pretswel from Scott Logic's blog

Goldilocks: census your genomes

Goldilocks is a new tool written by Sam Nicholls for counting interesting properties of genomes. It's very easy to install ("pip install goldilocks") and has a detailed user manual.

So, let's have a look at GC count across each of the chromosomes of Sorghum. Sorghum is a plant that is a reasonably close relative to Miscanthus, which is extensively studied here in Aberystwyth. I downloaded the chromosome assembly of sorghum from RefSeq. Here's the plot, showing amount of GC on the y-axis and position along the chromosome on the x-axis. The 10 Sorghum chromosomes are all shown stacked up in one plot panel.

The Python code for this using Goldilocks to do this plot is as simple as:

sequence_data = { 
    "sorghum" : {"file": "./sorghum.fna.fai"},
}
g = Goldilocks(GCRatioStrategy(), sequence_data, length="500K",
               stride = "1000K", is_faidx = True)
g.plot("sorghum", title="GC content of sorghum chromosomes")

The dip in GC for the centromere of each chromosome is obvious, except for chromosomes 2 and 6.

A similar but inverted pattern can be seen if we look at the number of Ns along the genome:

So, what's different about the centromeres in chromosomes 2 and 6? Why are they not so visible? Another way to spot them would be to look for a motif known to be in the centromeres. Centromeres have many repeats, and a repeat region known to be found in sorghum centromeres is CEN38. Let's choose a short motif from the sequence for CEN38, say "CCTAATG", and census that.

There's clearly plenty of this motif found in chromosomes 2 and 6, and found where we might expect a centromere to be (also lots of this motif in the centromeres of chr 3 and chr 5 too). But it's not found in all chromosomes. Could it be that CEN38 varies its sequence in the other chromosomes, and so doesn't have precisely that motif? Or that too many Ns in the other chromosomes stop CEN38 being characterised?

This is just a simple demonstration of how Golidlocks can be used to explore questions. And questions lead to more questions, and then many a happy hour can be spend browsing your genomes. Goldilocks can also be used to export details about which regions are the most interesting (hence the name: it finds regions that are "just right", for whatever your "just right" criterion might be).

Enjoy browsing your genomes! Goldilocks paper, Goldilocks docs, Goldilocks source code.

Playful coding: computing activities for schools

In many schools, computing is a topic that needs more encouragement. The Playful Coding project wants to make practical activities that can be run in schools to explore ideas in computer science. I've just been along to one of their meetings and seen it in action. It was extremely inspiring to be in a room full of people who didn't see running computing engagement activities as a chore, but as fun. They had all put a lot of thought into making fun activities and all wanted to run their activities with the groups of children.
 

It's an EU project involving teachers and university researchers from Spain, Romania, Italy, France and us in Aberystwyth, Wales. Each project partner had developed several activities and the purpose of the meeting was to tested out many of these activities on children and their teachers, and to start to develop a guide for teachers to explain how to use them. Until that guide is produced, you can still browse the activities and have a go with them. Try out for example:

• Collaborative story telling with Scratch, where each team of children makes a 30 second story with a character who moves from the left of the screen to the right. When all laptops are lined up, a character can move through every screen.
• Artificial Intelligence, where children get to think about whether a computer can ever be intelligence, and how would we know if it was?
• Poetry animation with Scratch, where children make characters that act out a poem.

There are lots more activities to choose from. Some just take an hour, some take a day, and some span a term. Some use robots, some use no particular equipment. They can be embedded into other lessons (languages, maths, science, art), or just standalone. And they are adaptable for different age ranges.

To follow the project see the Playful Coding website, follow #playfulcoding on Twitter or find Playful Coding on Facebook.

Seamless gene deletion

2015 is the year that genome editing really became big news. A new technique, "CRISPR/CAS", was named as Science magazine's breakthrough of the year as voted by the public from a shortlist chosen by staff.

However, people have been manipulating DNA through many useful methods long before CRISPR/CAS made headlines. Gene deletion is an important tool when trying to understand the function of genes. Take out a gene and see what effect it causes. Genes can be disrupted (by removing a portion of the DNA or inserting some extra DNA) or can be interfered with, for example via their RNA production, or they can be entirely deleted. It's common practice when removing a gene to insert a marker, so that we can easily select for the cells where this procedure has been successful. For example, to insert an antibiotic resistance gene as a marker, so that we can now grow the cells on a plate with an antibiotic. Then only those that have lost our gene of interest and gained antibiotic resistance will now grow. The trouble with this is that many gene deletions have no visible effect by themselves. If we also want to delete a second gene and a third, then we need more markers, or we need to be able to remove and reuse the marker we inserted. We also don't want the process to leave any scars behind that could destabilise the genome. We've just published a paper to help solve this problem.

This process of 'swap a gene of interest for a marker gene' can be achieved in many organisms by homologous recombination. This is a process used by many cells to repair broken strands of DNA. If we provide a piece of DNA that has a good region of similarity to the region just downstream of our gene of interest, and also a good region of similarity to the region just upstream of the gene of interest, but instead of the gene of interest, has the marker gene between these regions, then the normal cellular processes of homologous recombination will exchange the two. Some organisms perform homologous recombination very readily (S. cerevisiae for example). Others may need a little more encouragement, such as creating a double stranded break.

Our new paper A tool for Multiple Targeted Genome Deletions that Is Precise, Scar-Free and Suitable for Automation with Wayne Aubrey as first author uses a 3-stage PCR process to synthesise a stretch of DNA (a 'cassette') that will do everything. It will have good regions of similarity to the regions upstream and downstream of the gene of interest. It will contain a marker gene. And (here's the good bit), it will contain a specially designed region ('R') before the marker gene that is identical to the region that occurs just after the gene of interest. In this way, after homologous recombination has done its thing and inserted the DNA cassette instead of the gene of interest, there will be two identical R regions, one before the marker gene, and one after the marker gene. Sometimes the DNA will loop round on itself, the two R regions will match up and homologous recombination will snip out the loop, including the marker gene.

We can encourage this to happen and select for the cells that have had this happen if our marker is also 'counter-selectable'. That is, we'd like a marker for which we can add something to the growth medium so that now only cells without the marker will now grow. That is, we'd like to use a marker or marker combination for which we can first select for its presence and then counter-select for its absence. When we have this we can select for cells that have had the marker replace the gene, and then counter-select for cells that have now lost the marker too. So we have a clean gene deletion.

Of course we're always standing on the shoulders of giants when we do science. Our method is an improvement on a method by Akada 2006, so that no extra bases are lost or gained and the method requires no gel purification steps. Just throw in your primers and products and away you go. It's not fussy about quantity. No purification steps means that it could be automated on lab robots. And it could be used to delete any genetic component, not just genes. Give it a try!

Data science and a scoping workshop for the Turing Institute

In November I went to a workshop to discuss the remit of the Alan Turing Institute, the UK national institute for data science, with regard to the theme of "Data Science Challenges in High Throughput Biology and Precision Medicine". This workshop was held in Edinburgh, in the Informatics Forum, and hosted by Guido Sanguinetti.

The Alan Turing Institute is a new national institute, funded partly by the government, and partly by five universities (Edinburgh, UCL, Oxford, Cambridge, Warwick). The amount of funding is relatively small compared with that of other institutes (e.g. the Crick) and seems to be enough to fund a new building next door to the Crick in London, together with a cohort of research fellows and PhD students to be based in the new building. What should be the scope of the research areas that it addresses and how should it work as an institute? There are currently various scoping workshops taking place to discuss these questions.

Data science is clearly important to society, whether it's used in the analysis of genomes, intelligence gathering for the security services, data analytics for a supermarket chain, or financial predictions for the city. Statistics, machine learning, mathematical modelling, databases, compression, data ethics, data sharing and standards and novel algorithms are all part of data science. The ATI is already partnered with Lloyds, GCHQ and Intel. Anecdotal reports from the workshop attendees suggest that data science PhD students are snapped up by industry, ranging from Tesco to JP Morgan, and that some companies would like to recruit hundreds of data scientists if only they were available.

The feeling at the workshop seemed to be a concern that the ATI will aim to highlight the UK's research in the theory of machine learning and computational statistics, but risks missing out on the applications. The researchers who work on new and cutting edge machine learning and computational statistics don't tend to be the same people as the bioinformaticians. The people who go to NIPS don't go to ISMB/ECCB. And KDD/ICML/ECML/PKDD is another set of people again. These groups used to be closer, and used to overlap more, but now they rarely attend each others' conferences. Our workshop discussed the division between the theoreticians who create the new methods but prefer their data to be abstracted from the problem at hand, and the applied bioinformaticians, who have to deal with complex and noisy data, and often apply tried and tested data science instead of the latest theoretical ideas. To publish work in bioinformatics generally requires us to release code and data, and to have shown results on a real biological problem. To publish in theoretical machine learning or computational statistics, there is no particular requirement for an implementation of the idea, or to demonstrate its effectiveness on a real problem. There is also a contrast between the average size of research groups in the two areas. Larger groups are needed to produce the data (people in the lab to run the experiments, bioinformaticians to manage and analyse the data, and these groups are often part of larger consortia) whereas the theoreticians are often cottage-industry style research with just a PI and a PhD student. How should these styles of working come together?

Health informatics people worry about access to data: how to share it, get it, and ensure trust and privacy. Pharmaceuticals worry about dealing with data complexity, such as how to analyse phenotype from cell images in high throughput screening, having interpretable models rather than non-linear neural networks, and how to keep up with all the new sources of information, such as function annotations via ENCODE. GSK now has a Chief Data Officer. Everyone is concerned about how to accumulate data from new bio-technologies (microarrays then RNA-seq, fluorescence then imaging, new techniques for measuring biomarkers of a population under longitudinal study). Trying to keep up with the changes can lead to bad experiment design, and bad choices for data management.

There was much discussion about needing to make more biomedical data open-access (with consent), including genomic, phenotypic and medical data. There seemed to be some puzzlement about why people are happy to entrust banks with their financial data, and supermarkets with purchase data, but not researchers with biomedical data. (I don't share their puzzlement: your genetic data is not your choice, it's what you're born with, and it belongs to your family as much as it belongs to you, so the implications of sharing it are much wider).

All these issues surrounding the advancement of Data Science are far more complex and varied than the creation of novel and better algorithms. How much will the ATI be able to tackle in the next five years? It's certainly a challenge.

An executable language for change in biological sequences

A discussion on Twitter about whether there was a language for representing sequence edits prompted me to post my draft proposal for such a language. http://figshare.com/articles/Draft_proposal/1559009

Comments, criticism, collaboration and competition welcome. Hopefully I'll submit it shortly.