Ask me anything at PLOS Science Wednesday on May 18

PLOS Science Wednesday is a weekly science communication series featuring live, direct chats with PLOS authors on redditscience (/r/science), the popular online gathering place for researchers, students and others interested in science which has over 8 million registered members. The series provides a forum for PLOS authors to communicate their work and interact directly with fellow researchers and the public.

You can find the complete schedule here.

And on May 18th it’s my turn to answer anything together with my colleague James Brenton.

And when I say ‘anything’ I mean ‘anything about cancer evolution’.



UAI 2016 Workshop on Machine Learning for Health

Machine Learning for Health: Learning to understand human disease

Machine learning is revolutionizing our understanding of many human health problems from obesity to cancer. With ever increasing amount of data coming from this domain, computational biology and medicine are also transforming the machine learning community by not only providing new applications but also inspiring new modeling frameworks and learning paradigms.

The goal of this workshop is to bring together machine learning scientists and computational biologists. We would like to showcase recent advances in this field and discuss challenges in computational methodology and biomedical application.

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Career, Science

Open positions – cancer evolution and networks

I have three open positions in my lab:

  1. A PhD student position for “Single-cell analysis of cancer evolution”
  2. A postdoc position for “Evolutionary biology in cancer”. This position is ideal for somebody trained in evolutionary biology in model systems to make the transition to biomedical applications in cancer.
  3. And finally a postdoc position broadly advertised as “Computational cancer genomics” but actually having a strong network focus.

More info here

Any questions, just contact me directly.



Inferring tumor evolution from single-cell genomes

Series on Tumor Evolution

Everything is better if you do it with a Nested Effects Model – even inferring tumor evolution.

Let me introduce to you Oncogenetic Nested Effects Models, or for short OncoNEMs, which we just published in the new Single Cell collection of Genome Biology (see here). They exploit the fact that tumors accumulate mutations while they evolve, which leads to (noisy) subset relations between clones – exactly the type of pattern NEMs were made for.

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“Look at me, I was a terrible supervisor”

“I was a terrible PhD supervisor. Don’t make the same mistakes I did,” writes Sian Townson in the Guardian.

Lots of points I agree with:

Research points to high levels of depression among PhD students.

I am not surprised. This is one of the reasons Cambridge has such an active counseling service and, as far as I can see, there is little stigma attached to using it.

I also share her observation about the lack of training for supervisors:

[Academic practice courses] taught me some technical rules and requirements but nothing about the practical processes involved in teaching, mentoring and career-building a fellow human.

I have lamented this fact before in my post “Why science needs continuous leadership support”.

She is also right when saying

I failed to see that even as mature, independent people, my students still needed clear achievable milestones and objectives and celebrations when they reached them.

Especially the celebrations can be hard to do. There is always a Next Goal, a Next Paper.

Like my supervisors before me, I was technically successful – all my students passed on time and within budget – but in practice they struggled, feeling lost, unsupported and sometimes depressed.

She is making an important point here: there can be a big difference between how successful you and your students look on paper, and how you feel about it.

But nothing she writes here sounds really worrying to me. First of all, if all your students graduate in time – that’s great! Well done!

PhD research is hard. You are pushing the boundaries of current knowledge. If you don’t struggle and don’t feel lost for a while, you are not pushing hard enough.

I am not sure what she means by ‘sometimes depressed’ – it’s too unspecific. Everyone has their ups and downs. As a supervisor I am not trained to and shouldn’t attempt to diagnose people’s mental health. This is a task for specialists.

However, her saying her student felt ‘unsupported’ is an issue – this is definitely something a supervisor can and should change.

Look at me, I failed

For my taste, there is to much “Look at me, I failed” in this article.

I was an utterly appalling supervisor and I didn’t even realise it.

What is this? Fishing for compliments? Does she want an answer like “Oh, no, you were not. You did the best you could.”

Or is this an indirect way of telling me I am blind? Maybe I only think that I am an Ok supervisor because I haven’t realised yet how appalling I actually am.


Her last sentence is

Perhaps you can learn from my example.

No, I can’t.

Because this is all about perceived shortcomings and weaknesses of supervisors. To learn, I’d need some positive examples about how these shortcomings and weaknesses were overcome in some concrete situations.

Without any positive advise, this is just Failure Porn.





‘Five selfish reasons’ is one of Genome Biology’s Most Influential Articles of 2015

Genome Biology just sent an email around with 2015’s Most Influential Articles, according to

And, guess what, one of mine made the Top 10: Five selfish reasons to work reproducibly  from last December — really a late-comer to the competition.

And so, my fellow scientists: ask not what you can do for reproducibility; ask what reproducibility can do for you! Here, I present five reasons why working reproducibly pays off in the long run and is in the self-interest of every ambitious, career-oriented scientist.

Now I just need one of my research papers to have the same impact as my opinions, and I’d be sorted …




First parasites, now online harrassment – how has transparency harmed you lately?

An interesting post at Political Science Replication:

Getting the idea of transparency all wrong

Following an article in the New England Journal of Medicine, which portrayed scientists who re-use data as parasites, we now hear more on this from Nature. Apparently, data transparency is a menace to the public. The Nature comment “Don’t let transparency damage science” claims that the research community must protect authors from harassment by replicators. The piece further infects the discussion about openness with more absurd ideas that don’t reflect reality, and it leads the discussion backwards, not forward. 


Duty Calls, Science

I am a research parasite. Got a problem with that?

In case you wondered what’s wrong with biomedical research, just read this editorial on data sharing by Longo and Drazen in the New England Journal of Medicine, a leading journal in the field. What you will find is a desperate attempt to take data hostage and to enforce co-authorships for people who didn’t make any intellectual contributions.

But let’s take it one step at a time. What did Longo and Drazen actually say? They think there are major problems with sharing data fully, timely and openly.

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Science Stories – Reproducibility

If you think I am serious about reproducibility, you should see my wife.

In this movie by the Royal Society she is explaining the issue to David Spiegelhalter. That is Sir David Spiegelhalter, FRS etc etc.

Published on 22 Dec 2015. We need mathematical help to tell the difference between a real discovery and the illusion of one. Fellow of the Royal Society and future President of the Royal Statistical Society, Sir David Spiegelhalter visits Dr Nicole Janz to discuss reproducibility in scientific publications.

 Way to go!



“Five selfish reasons to work reproducibly” published


Wohoo! Genome Biology just published my piece on “Five selfish reasons to work reproducibly” (which I have talked about before).

And so, my fellow scientists: ask not what you can do for reproducibility; ask what reproducibility can do for you! Here, I present five reasons why working reproducibly pays off in the long run and is in the self-interest of every ambitious, career-oriented scientist.

Go check it out at

I am a bit sad, though, that they cut this über-geeky joke I used to illustrate how tightly the tools of reproducibility have to be linked with routine practice: