Wednesday, 19 September 2012

Available: Research position Biological Data Visualization and Visual Analytics


We could still use more applicants for this position, so bumping the open position...

Available: Research position Biological Data Visualization and Visual Analytics


Keywords: biological data visualization; visual analytics; data integration; genomics; postdoc

Are you well-versed in the language of Tufte? Do you believe that visualization plays a key role in understanding data? Do you like to work in close collaboration with domain experts using short iterations? And do you want to use your visualization skills to help us understand what makes a cancer a cancer, and what distinguishes a healthy embryo from one that is not?

We're looking for a motivated data visualization specialist to help biological researchers understand variation within the human genome. Methodologies exist for analyzing this type of data, but are still immature and return very different results depending on what assumptions are made. The type of data can also be used for a huge amount of different research questions, which necessitates developing very exploratory tools to support hypothesis generation.

Profile
The ideal candidate is well-motivated, holds a PhD (or at least MSc) degree in computer science or bioinformatics, and has experience in data visualization (e.g. using tools like D3 [http://d3js.org] or Processing [http://processing.org]). Prior experience working with DNA sequencing data and genome-wide detection of genetic variation would be an advantage but is not crucial. Good communication skills are important for this role.

You will collaborate closely with biologists and contribute to the reporting of the project. You will be able to work semi-independently under the supervision of a senior investigator, mentor PhD students, and contribute to the acquisition of new funding. A three-year commitment is expected. Start date is as soon as possible.

Relevant publications

  • Medvedev P, Stanciu M & Brudno M. Computational methods for discovering structural variation with next-generation sequencing. Nat Methods 6(11):S13-S20 (2009)
  • Nielsen CB, Cantor M, Dubchak I, Gordon D & Ting W. Visualizing genomes: techniques and challenges. Nat Methods 7:S5-S15 (2010)
  • Bartlett C, Cheong S, Hou L, Paquette J, Lum P, Jager G, Battke F, Vehlow C, Heinrich J, Nieselt K, Sakai R, Aerts J & Ray W. An eQTL biological data visualization challenge and approaches from the visualization community. BMC Bioinformatics 13(8):S8 (2012)

Application
For more information and to apply, please contact Jan Aerts (jan.aerts@esat.kuleuven.be, @jandot, +Jan Aerts). If possible, also send screenshots and/or screencasts of previous work.
 
URL: http://www.kuleuven.be/bioinformatics/

Tuesday, 4 September 2012

Postdoc position available: visualization and genomic structural variation discovery

http://www.ftmsglobal.edu.kh/wp-content/uploads/2012/04/Your-Career.jpg

SymBioSys is a consortium of computational scientists and molecular biologists at the University of Leuven, Belgium focusing on how individual genomic variation leads to disease through cascading effects across biological networks (in specific types of constitutional disorders and cancers). We develop innovative computational strategies for next-generation sequencing and biological network analysis, with demonstrated impact on actual biological breakthroughs.

The candidate will be a key player in the SymBioSys workpackage that focuses on genomic variation detection based on next-generation sequencing data (454, Illumina, PacBio) using a visual analytics approach (i.e. combining machine learning with interactive data visualization). This includes applying and improving existing algorithms and tools for the detection of structural genomic variation (insertions, deletions, inversions and translocations), as well as developing interactive data visualizations in order to investigate parameter space of these algorithms. These methods will be applied to specific genetic disorders in day-to-day collaboration with the human geneticists within the consortium.

We offer a competitive package and a fun, dynamic environment with a top-notch consortium of young leading scientists in bioinformatics, human genetics and cancer. Our consortium offers a rare level of interdisciplinarity, from machine learning algorithms and data visualization to fundamental advances in molecular biology, to direct access to the clinic. The University of Leuven is one of Europe’s leading research universities, with English as the working language for research. Leuven lies just east of Brussels, at the heart of Europe.

Profile
The ideal candidate holds a PhD degree in bioinformatics/genomics and has good analytical, algorithmic and mathematical skills. Programming and data analysis experience is essential. Prior experience working with sequencing data, i.c. alignment of next-generation data, as well as genome-wide detection of genetic variation would be a distinct advantage. Experience in data visualization - e.g. using tools like D3 (http://d3js.org) or Processing (http://processing.org) - would also be considered a big plus. Good communication skills are important for this role.

The candidate will collaborate closely with researchers across the consortium and contribute to the reporting of the project. Qualified candidates will be offered the opportunity to work semi-independently under the supervision of a senior investigator, mentor PhD students, and contribute to the acquisition of new funding. A three-year commitment is expected from the candidate. Preferred start date is November/December 2012, so please let us know asap.


Relevant publications
  • Conrad D, Pinto D, Redon R, Feuk L, Gokumen O, Zhang Y, Aerts J, Andrews D, Barnes C, Campbell P et al. Origins and functional impact of copy number variation in the human genome. Nature 464:704-712 (2010)
  • Medvedev P, Stanciu M & Brudno M. Computational methods for discovering structural variation with next-generation sequencing. Nat Methods 6(11):S13-S20 (2009)
  • Nielsen CB, Cantor M, Dubchak I, Gordon D & Ting W. Visualizing genomes: techniques and challenges. Nat Methods 7:S5-S15 (2010)


Application
Please send in PDF: (1) a CV including education (with Grade Point Average, class rank, honors, etc.), research experience, and bibliography, (2) a one-page research statement, and (3) two references (with phone and email) to Dr Jan Aerts (jan.aerts@esat.kuleuven.be), cc Dr Yves Moreau (yves.moreau@esat.kuleuven.be) and Ms Ida Tassens (ida.tassens@esat.kuleuven.be).
 
URL: http://www.kuleuven.be/bioinformatics/http://www.kuleuven.be/bioinformatics/

To apply : http://phd.kuleuven.be/set/postdoc/voorstellen_departement?departement=50000516http://phd.kuleuven.be/set/postdoc/voorstellen_departement?departement=50000516

Tuesday, 28 August 2012

Quantified Health, and my frustration with genetics

Since the publication of the human genome sequence about a decade ago, the popular press has reported on many occasion about genes allegedly found for things ranging from breast sizeintelligencepopularity and homosexuality to fidgeting. The general population is constantly told that the revolution is just around the corner. But the last year or so, articles start to pop up in the popular press that genomics and genetics will not be able to deliver what it promised (or what people thought it promised) a couple of years ago. The technology of (next-generation) sequencing is clearly following the Gartner Hype Cycle, and we're probably nearing the top of the "peak of inflated expectations".


Gartner Hype Cycle (taken from TechHui.com)


As a researcher myself, I also am not insensitive to this sensation. Even though (or because) I have contributed to some large genome sequencing projects (in chicken and cow) and have worked closely with the 1000Genomes team while at the Wellcome Trust Sanger Institute, I feel a growing frustration with genetics and genomics. It's all very interesting to build genomes, find associations of genes with disease, etc, but what can I as an individual do with this information? Yes, our research helps us understand the core of biology, and we do help (parents of) people with rare diseases diagnose those diseases or find the gene that causes the particular congenital condition. But how can this information help the vast majority of the population in their day-to-day life?

My frustration with what genetics can tell me

Under the umbrella of GenomesUnzipped, I had about half a million SNPs genotyped by 23andme (for data: see here). Based on that data and scientific literature, for example, they state that I have a higher chance of getting venous thromboembolism (VTE). Almost 18% of European descent with my genotype will develop VTE before they're 80, compared to 12.3% of the general European male population. The heritability of VTE, by the way, is about 55%. So what does this tell me? I should eat healthy, not smoke, and do more exercise. Still: my genotype can not tell me actually when I will get this, not even just before the event.

The issue is that our genomes are just the blueprints for who we are; they're not us. For that, we need to look at other omes: our transcriptomes, but most of all our proteomes and metabolomes, and our environment. Whatever our genotype is, it has no effect whatsoever unless through how it affects the constitution of the enzymes and other molecules in our cells: proteins could for example not work, or be present in non-optimal amounts.

Meanwhile...

When you go to a doctor in the hope to get rid of frequent headaches, what does the doctor base his diagnosis on? Those symptoms? More often than not: your memory. "I think I had those headaches last Friday and Monday, and if I recall correctly the one on Monday was a bit worse than the other one". Doctor: "is it always after eating something specific?". You: "I can't remember". Wouldn't it be great if in such cases your GP could diagnose your disease based on actual data?
But then comes the next step: taking drugs. The amount of a particular drug (and actually the drug to be taken itself) is based on population-wide averages of efficiency and occurrence of side effects of that drug. It's not based on how you react to that particular drug.

Quantified Health

Enter personalized medicine, and more specifically: P4Medicine (predictive, preventive, personalized and participatory). I'd like to look at this from a little broader perspective, as quantified health: data-driven health.

Since quite a while I've been following the Quantified Self movement (see e.g. quantifiedself.com). The aim of this movement is to improve self knowledge through self tracking: collecting data about yourself to identify trends (e.g. weight loss) and correlations (e.g. linking migraine episodes to triggers). This knowledge can then be used to change someone's behaviour, to predict the onset of disease or episodes thereof, or to prevent it altogether. What if you're smartphone could give you a message on Friday afternoon to get into the dark and drink more fluids otherwise you'd get a migraine episode on that particular Saturday? The big thing here is that any decision would be personalized, and appropriate for that individual, rather than for the majority of the population that that individual belongs to (e.g. male caucasians).

http://jaeselle.com/wp-content/uploads/2012/05/the-quantified-self.jpeg


Conceptually, the type of things that we can track can be tracked either externally (i.e. using a fitbit, tracking apps on a smartphone, continuous ECG monitoring) or internally (i.e. using biosensors to follow molecular markers). Working in the omics field, I'm obviously very interested in the latter: what molecules can we easily track in the body that can predict disease? Even as a boy, I fantasized that we could track anything happening in our bodies and use that to stay healthy. D-dimers are a nice example here. These are protein fragments that are produced when a blood clot degrades. Detecting d-dimers has a high sensitivity and negative predictive value for thrombosis (remember that I have a higher genetic predisposition for VTE), but unfortunately a low specificity. This means that if you have a blood clot forming (that could get dislodged) you definitely will show d-dimers; but having d-dimers in your blood does not necessarily mean that you're forming clots. Current practice, where a blood sample is taken when the doctor orders one because there is suspicion of blood clotting, however, has only little value. With age, one starts seeing this molecule in the individual's blood. But if we would monitor this molecule longitudinally (hypothetically: every day), this background/noise would become irrelevant. Hence: quantifying self at the molecular level.

Nice examples

If you're interesed, you should definitely check out the research on the Integrated Personal Omics Profile (iPOP, or "Snyderome") by Chen et al (doi: 10.1016/j.cell.2012.02.009), where Michael Snyder's transcriptome, proteome and metabolome were sampled 20 times during a 14-month period. Another astonishing story is that of Eric Alm's year-long gut microbiome tracking (check the video!). Some interesting conclusions based on his (and one of his student's) data! For his student, traveling only changed his gut microbiome transiently. Getting salmonella, however, resulted in a permanent change in Alm's microbiome constitution. Finally, check out these articles on Larry Smarr: "Is health tracking the next big thing?", and "The Measured Man".

I can also definitely recommend the book Experimental Man by David Ewing Duncan. It shows you what's possible, and most of all what's not (yet) possible.

I've also created a "Quantified Health" paper.li a while ago, which helps me pick up interesting news.

Challenges

To fully implement quantified health, there are still several challenges. As these are basically n-is-one studies, the statistics and data mining will be different. What's even more important is how do we return results and trends to the end user? It will be very important to display the data/results within context rather than just reporting p-values and "above thresholds". On the molecular side, we also need some good use cases. These should

  1. have clear "events" that we can track (something happened or didn't happen),
  2. have useful (combinations of) biomarkers (so that we don't need to do a full-blown discovery phase),
  3. be in a population that is happy to provide the samples (e.g. daily blood sample),
  4. have assays to follow those biomarkers
From what I'm finding at the moment, it's the first two conditions that are the hardest to meet. Assays can often be developed using antibodies and/or aptamers (really cool technology, BTW), and for a proof-of-principle it should be possible to start with diabetes patients who have to sample their blood periodically anyway. The use of saliva or urine samples would be nice, but unfortunately most biomarkers will be in the blood...

We're at the brink of truly personalizing medicine and health.

Wednesday, 23 May 2012

Clojure, visualization, and scripts

Bit of a technical post for my own reference, about visualization and scripting in clojure.

Clojure and visualization

Being interested in clojure, a tweet by Francesco Strozzi (@fstrozzi) caught my attention last week: "A D3 like #dataviz project for #clojure. Codename C2 and looks promising. http://keminglabs.com/c2/. They need contribs so spread the word!" I tried a while ago to do some stuff in D3, but the javascript got in the way so I gave up after a while. But I was still pulled towards something html5+css rather than java applets as created by processing.org.

Although still in a very early stage, C2 is already very powerful. Rapid development of visualizations is aided by the visual repl: the webpage localhost:8987 automatically loads the last modified file in the samples directory.


(ns rectangles-hover
  (:use [c2.core :only [unify]]))

(def css "
body { background-color:white;}
rect { -webkit-transition: all 1s ease-in-out;
       fill:rgb(255,0,0);}
rect:hover { -webkit-transform: translate(3em,0);
fill:rgb(0,255,0);}
circle { opacity: 1;
         -webkit-transition: opacity 1s linear;
         fill:rgb(0,0,255);}
circle:hover { opacity: 0; }
")

[:svg
 [:style {:type "text/css"} (str "<[CDATA[" css "]]>")]
 (unify {"A" 50 "B" 120} (fn [[label val]]
  [:rect {:x val :y val :height 50 :width 60}]))
 (unify {"C" 180 "D" 240} (fn [[label val]]
  [:circle {:cx val :cy val :r 15}]))]


This bit of code draws 2 red rectangles and 2 blue circles. Hovering the mouse over any of the rectangles will move it to the right and change its colour to green; hovering over a circle will make that circle transparent. Some more scripts that I've used to build up simple things and learn C2 are on github.

Although interactions are not covered in C2 itself, simple transitions can be handled in the CSS part (see the example above). Brushing, linking and other types of interaction would be interesting to have available as well, though. But the developer Kevin Lynagh is very responsive.

I haven't looked yet into how to run C2 without the visual repl; still on my to-do list. (UPDATE: see end of post)

Clojure and scripting

And today, I saw this. Leiningen 2 will allow you to easily execute little clojure scripts without the whole setup of a project. Makes it amenable for pipelining just like you would do with little perl/ruby/python scripts. The completely-useless-but-good-enough-as-proof-of-principle little example below attaches some dashes to the front and stars at the back of anything you throw at it from STDIN.

#!/bin/bash lein exec
(doseq [line (line-seq (java.io.BufferedReader. *in*))]
 (println (str "----" line "****")))

Pipe anything into this:
ls ~ | ./proof-of-principle.clj

Dependencies are now stored in ~/.mv2 rather than in the project directory, you can load libraries such as clojure like this:

#!/bin/bash lein exec
(use '[leiningen.exec :only (deps)])
(deps '[[incanter "1.3.0"]])

(use '(incanter core charts stats datasets))
(save (histogram (sample-normal 1000)) "plot.png")

This also works in the interactive repl ("lein repl").

Bringing the two together

It's really easy to combine these two (after a pointer from C2 Kevin (Thanks!)). You need an additional dependency on hiccup to convert to html, but that's it.

Here's a script that, when executed with "lein exec this-script.clj" will generate a html file with the interactive picture shown above.

#!/bin/bash lein exec
(use '[leiningen.exec :only (deps)])
(deps '[[com.keminglabs/c2 "0.1.1"] [hiccup "1.0.0"]])

(use '[c2.core :only (unify)])
(use 'hiccup.core)

(def css "
body { background-color:white;}
rect { -webkit-transition: all 1s ease-in-out;
       fill:rgb(255,0,0);}
rect:hover { -webkit-transform: translate(3em,0);
             fill:rgb(0,255,0);}
circle { opacity: 1;
         -webkit-transition: opacity 1s linear;
         fill:rgb(0,0,255);}
circle:hover { opacity: 0; }
")

(def svg [:svg
 [:style {:type "text/css"} (str "")]
 (unify {"A" 50 "B" 120} (fn [[label val]]
  [:rect {:x val :y val :height 50 :width 60}]))
 (unify {"C" 180 "D" 240} (fn [[label val]]
  [:circle {:cx val :cy val :r 15}]))])

(spit "test.html" (html svg))

Thursday, 10 November 2011

Biovis/Visweek recap

Finally time to write something about the biovis/visweek conference I attended about a week ago in Providence (RI)... And I must say: they'll see me again next year. (Hopefully @infosthetics will be able to join me then). Meanwhile, several blog posts are popping up discussing it (see here and here, for example).

This was the first time that biovis (aka the IEEE Symposium on Biological Data Visualization) was organized. It's similar to the 2-year old vizbi, but has an agenda that is more focused on research in visualization rather than application of visualization in the biological sciences. Really interesting talks, posters and people.

The biovis contest
This first installment of biovis included a data visualization contest, focusing on "specific biological problem domains, and based on realistic domain data and domain questions". The topic this year was on eQTL (expression quantitative trait loci) data, and I'm really happy that Ryo Sakai -  now a PhD student in my lab - won the "biologists' favourite" award!! The biologist jury was impressed with the ease in which his visualizations of the eQTL data highlighted and confirmed prior knowledge, and how it suggested directions for further experiments. It was interesting to see that there was a huge variation in the submissions, going from just showing the raw data in an interesting way (which Sakai-san did) to advanced statistical and algorithmic munging of the input data and visualizing the end result (which the winner of the "dataviz professionals' favourite" award did). See how this relates to my previous post on humanizing bioinformatics?


Interesting talks - amazing (good & bad) talks
As this was the first time that I attended visweek, I was really looking forward to the high quality presentations/papers and posters. Overall, I got what I wanted. But there were some examples of papers and posters that I have major doubts about (taking into account that I have to be humble here in talking about people working in the field for far longer than I do).
One example that seemed pretty counterintuitive was a presentation by Basak Alper from Microsoft about a new set visualization technique that they baptized LineSets. The main issue that they want to solve is the visualization of intersections of >3 sets (up to 3 you'd just use Venn diagrams). Their approach is to connect the different elements from a set by a line; hence: linesets. However, I (and many others with me) felt that this approach has some very serious drawbacks. Most of all, it suggests that there is an implicit ordering of the elements, which there isn't. In the image below, for example, line sets were used to connect Italian restaurants (in orange) and Mexican restaurants (in purple). That's the only thing this visualization wants to do: tell me which of the restaurants are Italian and which are Mexican. But give this picture to 10 people, and every single one of them will think that the lines are actually paths or routes between these restaurants. Which they're not... The example below shows data that has specific positions on a map, but they demonstrate this approach on social networks as well.
LineSets
Another example comes from the biovis conference: TIALA or Time Series Alignment Analysis. Suppose you have the time-dependent gene expression values for a single gene, which you'd plot using a line plot. Now what would you do if you have that type of data for 100 genes? Would you put those plots into 3D? I know I wouldn't... And better still: would you then connect these plots so you end up with some sort of 3D-landscape? That's like connecting the tops of a barchart displaying categorical data with a line...

TIALA - Time Series Alignment Analysis


But of course there were plenty really good talks as well. Some of the talks I really enjoyed are those about HiTSEE (by Bertini et al) on the analysis of high-throughput screening experiments, EVEVis (Miller et al) on multi-scale visualization for dense evolutionary data, arc length-based aspect ratio selection (Talbot et al) which is an alternative to banking to 45 degrees, drawing road networks with focus regions (Haunert et al), and especially DICON which showed an amazing application of visual analysis of multidimensional clusters using healthcare data.

HiTSEE

EVEVis
Road networks with focus regions
DICON - interactive visual analysis of multidimensional clusters

Meeting interesting people
But of course this was very much about meeting interesting people as well. It was really nice to exchange ideas again with the biovis crowd (Nils Gehlenborg, Cydney, Tamara, Will Ray, ...), and I finally had the chance to have a chat with @filwd Enrico. All those discussions with Thorri from Icelandic DataMarket were both useful and fun (as was our day hanging out in town, chatting to the Occupy Providence woman (forgot her name, I'm afraid) and trying to find a good hat).
At the airport on my way back, as I was trying to find out how to get to Brussels (as our flights were cancelled due to the weather), a chap comes to me and introduces himself as someone from Belgium. From Leuven. From our very own faculty. So together with @infosthetics Andrew that now makes three of us :-)

Anyway: I'll definitely be back next year (have to play some more official role anyway) and already looking forward to it.