20 researchers, hundreds of opinions, no powerpoint – yes, it’s been that time of the year again: Systems Genetics of Cancer rocked London.
When I started my PI career, my first cover letter to a glamour journal emphatically pointed out that my cutting-edge, ground-breaking work was the first and firstest to do X.
Feedback from senior colleagues was: “Drop that blech! Better say what your insight into X actually is, and in what way it is profound.” — Good advice. Because novelty is overrated, insight rules.
How should novelty be valued in science? Not exclusively.
So I wasn’t too surprised how Barak Cohen answered the question “How should novelty be valued in science?” in the last issue of eLife. I would never put a question mark into a title, if the answer is so clear:
Laborjournal.de just published a German translation of my opinion piece “All biology is computational biology” in PLoS Biology earlier this year.
Have a look at it here: http://www.laborjournal.de/rubric/essays/essays2017/e17_10.lasso
Luckily I didn’t have to translate it myself. My Deutsch has been getting pretty schlecht lately.
And this is reading quite well, don’t you think?
Superheroes like Mr Fantastic are used to being watched, and bioinformaticians better get used to it, too. Like superheroes, bioinformaticians are adored by the public for their powers as well as their dress sense. And while superheroes have their own Superbeing watching from the moon (Uatu the Watcher), bioinformaticians have their own tribe of sociologists stalking them, as a recent insider report has revealed.
When I opinionated on and on about All Biology being Computational Biology, I was aware that these were not really novel ideas. After all Hallam Stevens had written a whole book about it and my friends inside my intellectual bubble kept on asking why I had spent so much time on writing up something so glaringly obvious.
But what I had missed is that some of my points had already been made very clearly in an excellent piece by Pavel Pevzner and Ron Shamir in Science in 2009 titled “Computing Has Changed Biology—Biology Education Must Catch Up“.
The Human Cell Atlas preprint came out some days ago on bioRxiv. It describes a project to collect all the cell types in the human body in one big reference map.
Our mission: To create comprehensive reference maps of all human cells—the fundamental units of life—as a basis for both understanding human health and diagnosing, monitoring, and treating disease. [from humancellatlas.org]
The contributors to the project are a Who-is-who of the leaders in single cell genomics and this will be a fantastic data set when it comes out. Because in-depth analysis of resources like this provides the foundation of all biology, as you know.
I enjoyed reading the preprint. It puts the project into a historical perspective and discusses promises as well as limitations. It even references Borges’ `On Rigor in Science’. (I love well-read scientists!) And even if all that means nothing to you, it is still worth reading as a comprehensive summary of the current state-of-the-single-cell-art.
But I kept wondering, with a project like this, how do you know whether it is a success or not? How do you know that your reference map is really comprehensive and covers all (most?) of what it is supposed to find?
Have a look at this excellent editorial in Nature: Integrity starts with the health of research groups – Funders should force universities to support laboratories’ research health.
I really like the term ‘research health’, which encompasses both technical aspects of doing research right as well as the well-being of researchers.
Almost a month has passed since I published an opinion piece called “All biology is computational biology” in PLoS Biology.
In my paper, I envisioned a biology that explicitly and clearly acknowledges how much it has changed over the last 20 years, how much its questions have changed, and how much the practice of doing biology has changed. I envisioned a biology that gives credit broadly and fairly to everybody who contributed to key insights – regardless of what tools they used.
As intended, my paper provoked many responses from the community, and in the following you find my thoughts on some particularly interesting comments.
Check out Amber Dance’s comparison of single cell tumor phylogeny methods at The Scientist.
Almost makes it look like Niko and I know what we are doing in this field.
In this series, we ask leading scientists in their respective fields to explain clearly and engagingly for a lay audience why the research carried out in their laboratories – and those of their collaborators and their colleagues – matters.
It wasn’t immediately clear to me, what I should write about. I tend to label myself a cancer researcher nowadays, but cancer research does not need any explanation why it matters – unfortunate as that is.
At the same time, I am a computational biologist – and here I thought was a much bigger need to explain why it matters. The question is not so much why computational biology and bioinformatics are useful (nobody seems to question that it’s handy to have the geeks around) but why is it biological research, rather than just a support and service activity.
Except for me, everyone else at the workshop came from the social sciences and I found it very interesting to engage with a different community this time.
One of our papers just came out in Genome Biology
BaalChIP: Bayesian analysis of allele-specific transcription factor binding in cancer genomes
Ines de Santiago, Wei Liu et al. Genome Biology 2017 18:39
Here is what it’s about:
It’s good to get feedback now and then. Better still if it is positive.
High-throughput sequencing of tumors should be informative about the stages of cancer progression. This paper is one of several that exploit the interesting observation that cancer progression is essentially a phylogenetic reconstruction problem. Of course, that should not be surprising since cancer is an evolutionary disease.
This paper looks at copy number variation (CNV) in particular, reducing CNV to an integer vector by considering SNPs in a series of windows along the genome. It addresses both allele phasing and phylogeny.
Most interestingly, from a methodological viewpoint, it does so using techniques from language and automata theory (specifically, context-free grammars and finite-state transducers). These are both tools that have found application in phylogenetics, in fact, as rather advanced tools for modelling the evolution of things like indels and RNA structure.
So, this paper represents an example of the state-of-the-art in one field (phylogenetics) being applied to advance another (computational cancer biology).
Thank you. Very appreciated.
- Schwarz RF, Trinh A, Sipos B, Brenton JD, Goldman N, Markowetz F.
Phylogenetic quantification of intra-tumour heterogeneity.
PLoS Comput Biol. 2014 Apr 17;10(4):e1003535.
doi: 10.1371/journal.pcbi.1003535. PMID: 24743184;
Tired of viruses and fruit flies? Want to work on something really important for a change? Come and help us to figure out cancer evolution!
We are looking for outstanding candidates to work on inferring patterns of tumor evolution from genomics data. We work with a close group of clinical collaborators, both locally and internationally, who will provide multi-sample bulk sequencing and single-cell data sets. We plan to adapt methods from population genetics and phylogenetics to the cancer setting. Key questions will be to compare mutation rates and selection hotspots between the genomes of cancer clones.
This position is ideal for somebody trained in evolutionary biology in model systems to make the transition to biomedical applications in cancer.
The successful applicant will have a PhD in a quantitative field like mathematics, statistics, physics, engineering, bioinformatics, or computer science. A background in evolutionary biology, molecular evolution or population genetics is highly desired. The applicant should have a good biological background and excellent computing skills. The atmosphere at CI is very collaborative and interactive; good communication skills are key.
To apply, please visit http://www.jobs.cam.ac.uk/job/12614/
- Beerenwinkel et al (2014) Cancer evolution: mathematical models and computational inference, Systematic Biology.
- Ross and Markowetz (2016), OncoNEM: Inferring tumour evolution from single-cell sequencing data, Genome Biology, 17:69
- Schwarz et al (2015), Spatial and temporal heterogeneity in high-grade serous ovarian cancer: a phylogenetic reconstruction, PLoS Med, 12(2)
- Yuan et al (2015), BitPhylogeny: A probabilistic framework for reconstructing intra-tumor phylogenies, Genome Biology, 16:36
More publications, more grants, more awesome! Here is my #scidata16 talk on youtube:
And here is Jonathan Page reporting on my talk in Naturejobs:
The problem lies in the fact that working reproducibly often requires some time investment, something which many scientists working in competitive fields claim they can’t afford. Florian Markowetz from the University of Cambridge counters these claims by saying “not to ask what you can do for reproducibility, but to ask what can reproducibility do for you!”
Indeed I do.