Job: Postdoc position in proteome bioinformatics

December 17, 2014

I currently have an opening for a two-year postdoc position in my group Cellular Network Biology at the Novo Nordisk Foundation Center for Protein Research.

The project primarily relates to on computational analysis of mass-spectrometry-based proteomics data. This includes developing new, improved methods for analyzing spectra, optimization of analysis protocols, and application of these to specific datasets. The focus will be on improving the identification of post-translationally modified peptides, which arise in cellular signaling processes and through chemical modification, in particular in ancient samples, which may also consist of mixtures of species. The work will involve close collaboration with the Proteomics Program.

The closing date for applications is December 31, 2014. For further details refer to the job advert.


Analysis: Does a publication matter?

August 11, 2014

This may seem a strange question to ask for someone working in academia – of course a publication matters, especially if it is cited a lot. However, when it comes to publications about web resources, publications and citations in my opinion mainly serve as somewhat odd proxies on my CV for what really matters: the web resources themselves and how much they are used.

Still, one could hope that a publication about a new web resource would make people aware of its existence and thus attract more users. To analyze this, I took a look at the user statistics of our recently developed resource COMPARTMENTS:

COMPARTMENTS user statistics

Before publishing a paper about it, the web resource had less than 5 unique users per day. Our paper about the resource was accepted on January 26 in the journal Database, which increased the usage to about 10 unique users on a typical weekday. The spike of 41 unique users in a single day was due to me teaching on a course.

So what happened end of June that gave a more than 10-fold increase in the number of users from one day to the next? A new version of GeneCards was released with links to COMPARTMENTS. It seems safe to conclude that the peer-reviewed literature is not where most researchers discover new tools.


Commentary: The 99% of scientific publishing

July 14, 2014

Last week, John P. A. Ioannidis from Stanford University and Kevin W. Boyack and Richard Klavans from SciTech Strategies, Inc published an interesting analysis of scientific authorships. In the PLOS ONE paper “Estimates of the Continuously Publishing Core in the Scientific Workforce” they describe a small influential core of <1% of researchers who publish each and every year. This analysis appears to have caught the attention of many, including Erik Stokstad from Science Magazine who wrote the short news story “The 1% of scientific publishing”.

You would be excused to think that I belong to the 1%. I published my first paper in 1998 and have published at least one paper every single year since then. However, it turns out that the 1% was defined as the researchers who had published at least one paper every year in the period 1996-2011. Since I published my first paper in 1998, I belong to the other 99% together with everyone else who started their publishing career after 1996 or stopped their career before 2011.

Although the number 1% is making the headlines, the authors seem to be aware of the issue. Of the 15,153,100 researchers with publications in the period 1996-2011, only 150,608 published all 16 years; however, the authors estimate an additional 16,877 scientists published every year in the period 1997-2012. A similar number of continuously publishing scientists will have started their careers all the other years from 1998-2011. Similarly, they an estimated 9,673 researchers stopped their long continuous publishing career in 2010, and presumably all other years in the period 1996-2009. In my opinion, a better estimate is thus that 150,608 + 15*16,877 + 15*9,673 = 548,858 of the 15,153,100 authors have had or will have a 16-year unbroken chain of publications. That amounts to something in the 3-4% range.

That number may still not sound impressive; however, this in no way implies that most researchers do not publish on a regular basis. To have a 16-year unbroken chain of publications, one almost has to stay in academia and become a principal investigator. Most people who publish at least one article and subsequently pursue a career in industry or teaching will count towards the 96-97%. And that is no matter how good a job they do, mind you.


Announcement: EMBO practical course on protein interaction analysis in South Africa

July 6, 2014

I very much look forward to once again be part of the team of teachers behind the EMBO practical course “Computational analysis of protein-protein interactions: From sequences to networks”. This time it will for the first time take place on the African continent, more specifically in Cape Town, South Africa. The course will take place from September 23 – October 3 and the application deadline is July 23.

Please check the course website or the poster below for details.

Course poster


Job: Ph.D. stipend in systems biology and bioinformatics

June 19, 2014

I currently have an open position for a Ph.D. student in my group Cellular Network Biology. My group is part part of the Novo Nordisk Foundation Center for Protein Research (CPR) and is financially supported by the Faculty of Health and Medical Sciences, University of Copenhagen.

The project will primarily focus on developing new, improved methodologies for analysis of large-scale datasets, e.g. from mass spectrometry, in the context of protein interaction networks, protein localization, and expression. In doing so, the aim is both to test scientific hypotheses and to improve existing resources developed within the group, such as STRING and COMPARTMENTS. Candidates are thus expected to have experience with programming and statistics.

The closing date for applications is June 30, 2014. For further details refer to the job advert.


Commentary: GPU vs. CPU comparison done right

May 1, 2014

I have in earlier posts complained about how some researchers, through unfair comparisons, make GPU computing look more attractive than it really is.

It is thus only appropriate to also commend those who do it right. As part of some ongoing research, I came across a paper published in Journal of Chemical Information and Modeling:

Anatomy of High-Performance 2D Similarity Calculations

Similarity measures based on the comparison of dense bit vectors of two-dimensional chemical features are a dominant method in chemical informatics. For large-scale problems, including compound selection and machine learning, computing the intersection between two dense bit vectors is the overwhelming bottleneck. We describe efficient implementations of this primitive as well as example applications using features of modern CPUs that allow 20–40× performance increases relative to typical code. Specifically, we describe fast methods for population count on modern x86 processors and cache-efficient matrix traversal and leader clustering algorithms that alleviate memory bandwidth bottlenecks in similarity matrix construction and clustering. The speed of our 2D comparison primitives is within a small factor of that obtained on GPUs and does not require specialized hardware.

Briefly, the authors compare the speed of with which fingerprint-based chemical similarity searches can be performed on CPUs and GPUs. In contrast to so many others, the authors went to great lengths to give a fair picture of the relative performance:

  • Instead of using multiple very expensive Nvidia Tesla boards, they used an Nvidia GTX 480. This card cost roughly $500 when released and was the fastest gaming card available at the time.
  • For comparison, they used an Intel i7-920. This CPU cost approximately $300 when released and was a high-end consumer product.
  • They compared the GPU implementation of the algorithm to a highly optimized CPU implementation. The CPU implementation makes use of SSE4.2 instructions available on modern Intel CPUs and is multi-threaded to utilize all CPU cores.

The end result was that the GPU implementation gives a respectable but non-exceptional 5x speed-up over a pure CPU implementation. If one further takes into account that the GPU is probably 40% of the cost of the whole computer, this reduces to a 3x improvement in price-performance ratio.

The authors conclude:

In summary: GPU coding requires one to think of the hardware, but high-speed CPU programming is the same; spending time optimizing CPU code at the same level of architectural complexity that would be used on the GPU often allows one to do quite well.

I can only agree wholeheartedly.


Resource: The COMPARTMENTS database on protein subcellular localization

March 24, 2014

Together with collaborators in the groups of Seán O’Donoghue and Reinhard Schneider, my group has recently launched a new web-accessible database named COMPARTMENTS.

COMPARTMENTS unifies subcellular localization evidence from many sources by mapping all proteins and compartments to their STRING identifiers and Gene Ontology terms, respectively. We import curated annotations from UniProtKB and model organism databases and assign confidence scores to them based on their evidence codes. For human proteins, we similarly import and score evidence from The Human Protein Atlas. COMPARTMENTS also uses text mining to derive subcellular localization evidence from co-occurrence of proteins and compartments in Medline abstracts. Finally, we precompute subcellular localization predictions with the sequence-based methods WoLF PSORT and YLoc. For further details, please refer to our recently published paper entitled “COMPARTMENTS: unification and visualization of protein subcellular localization evidence”.

To provide a simple overview of all this information, we visualize the combined localization evidence for each protein onto a schematic of an animal, fungal, or plant cell:

COMPARTMENTS NR3C1

COMPARTMENTS COX1

COMPARTMENTS PSAB

You can click any of the three images above to go to the COMPARTMENTS web resource. To facilitate use in large-scale analyses, the complete datasets for major eukaryotic model organisms are available for download.


Announcement: PTMs in Cell Signaling conference

March 11, 2014

Two years ago, I was one of the organizers of the 2nd Copenhagen Bioscience Conference entitled PTMs in Cell Signaling. I think it is fair to describe it as a highly successful meeting, and it is my great pleasure to announce that we will be organizing a second meeting on the topic September 14-18, 2014.

CBC6 poster

My co-chairs Jeremy Austin Daniel, Michael Lund Nielsen, and Amilcar Flores Morales have managed to put together the following excellent lineup of invited speakers:

Alfonso Valencia, Chris Sander, David Komander, Gary Nolan, Genevieve Almouzni, Guillermo Montoya, Hanno Steen, Henrik Daub, John Blenis, John Diffley, John Tainer, Karolin Luger, Marcus Bantscheff, Margaret Goodell, Matthias Mann, Michael Yaffe, Natalie Ahn, Pedro Beltrao, Stephen Elledge, Tanya Paull, Tony Hunter, Yang Shi, Yehudit Bergman, and Yosef Shiloh.

All conference expenses are covered, which means that there will be no registration fee and no expenses for accommodation or food. You will have to cover your own travel expenses, though.

Participants will be selected based on abstract submission, which is open until June 9, 2014. For more information please see the conference website.


Commentary: Are other women a woman’s worst enemies in science?

March 10, 2014

It is clear that in science, we have a gender bias among leaders. It is my impression that most people think this is due to a combination of men and women having different priorities in life and high-ranking male professors favoring their own gender. Conversely, I have never heard anyone dare to suggest that women may be their own worst enemies in this context.

Benenson and coworkers from Emmanuel College have just published an interesting study in Current Biology on collaborations between full professors and assistant professors entitled “Rank influences human sex differences in dyadic cooperation”.

By tabulating the joint publications, they found 76 same-sex publications from male full professors, which should be compared to a random expectation of 61 such publications. By contrast they found only 14 same-sex publications from female full professors with the random expectation being 29. In other words, whereas male full professors collaborated 25% more with male assistant professors than expected, female full professors collaborated more than 50% less with female assistant professors than expected. The authors conclude:

Our results are consistent with observations suggesting that social structure takes differing forms for human males and females. Males’ tendency to interact in same-gender groups makes them more prone to cooperation with asymmetrically ranked males. In contrast, females’ tendency to restrict their same-gender interactions to equally ranked individuals make them more reluctant to cooperate with asymmetrically ranked females.

There is, in other words, a bias towards high-ranking professors of both genders to preferentially collaborate with lower-ranking male professors as opposed to lower-ranking female professors. If anything, that bias appears to be stronger in case of high-ranking female professors than high-ranking male professors.


Commentary: Coffee, a prerequisite for research?

January 14, 2014

Yesterday, I stumbled upon two links that I found interesting. The first was the map-based data visualization blog post 40 Maps That Will Help You Make Sense of the World, in which maps 24 and 28 hint at a correlation (click for larger interactive versions):

Number of Researchers per million inhabitants by Country

Current Worldwide Annual Coffee Consumption per capita

The first map shows the number of researchers per million inhabitants in each country. The second map shows the number of kg coffee consumed per capita per year. As ChartsBin allows you to download the data behind each map, I did so and produced a scatter plot that confirms the strong correlation (click for larger version):

coffee_vs_researchers

This confirms my view that the coffee machine is the most important piece of hardware in a bioinformatics group. Bioinformaticians with coffee can do work even without a computer, but bioinformaticians without coffee are unable to work, no matter how good computers they have.

One should of course be careful to not jump to conclusions about causality based on correlation. This leads me to the second link: a new study published in Nature Neuroscience, which shows that Post-study caffeine administration enhances memory consolidation in humans.

I optimistically await a similar study confirming the correlation between Chocolate Consumption, Cognitive Function, and Nobel Laureates published last year in New England Journal of Medicine.


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