Category Archives: Commentary

Commentary: The sad tale of MutaDATABASE

The problem of bioinformatics web resources dying or moving is well known. It has been quantified in two interesting papers by Jonathan Wren entitled “404 not found: the stability and persistence of URLs published in MEDLINE” and “URL decay in MEDLINE — a 4-year follow-up study”. There is also a discussion on the topic at Biostar.

The resources discussed in these papers at least existed in an operational form at the time of publication, even if they have since perished. The same cannot be said about MutaDATABASE, which in 2011 was published in Nature Biotechnology as a correspondence entitled “MutaDATABASE: a centralized and standardized DNA variation database”. Fellow blogger Neil Saunders was quick to pick up on the fact that this database was an empty shell, but generously gave the authors the benefit of the doubt in his closing statement:

Who knows, MutaDatabase may turn out to be terrific. Right now though, it’s rather hard to tell. The database and web server issues of Nucleic Acids Research require that the tools described be functional for review and publication. Apparently, Nature Biotechnology does not.

Now, almost five years after the original publication, I think it is fair to follow up. Unfortunately, MutaDATABASE did not turn out to be terrific. Instead, it turned out just not to be. In March 2014, about three years after the publication, www.mutadatabase.org looked like this:
MutaDATABASE in 2014

By the end of 2015, the website had mutated into this:
MutaDATABASE in 2015

To quote Joel Spolsky: “Shipping is a feature. A really important feature. Your product must have it.” This also applies to biological databases and other bioinformatics resources, which is why journals would be wise never to publish any resource without this crucial feature.

Commentary: Does it even matter whether you use Microsoft Word or LaTeX?

Shortly before Christmas, PLOS ONE published a paper comparing the efficiency of using Microsoft Word and LaTeX for document preparation:

An Efficiency Comparison of Document Preparation Systems Used in Academic Research and Development

The choice of an efficient document preparation system is an important decision for any academic researcher. To assist the research community, we report a software usability study in which 40 researchers across different disciplines prepared scholarly texts with either Microsoft Word or LaTeX. The probe texts included simple continuous text, text with tables and subheadings, and complex text with several mathematical equations. We show that LaTeX users were slower than Word users, wrote less text in the same amount of time, and produced more typesetting, orthographical, grammatical, and formatting errors. On most measures, expert LaTeX users performed even worse than novice Word users. LaTeX users, however, more often report enjoying using their respective software. We conclude that even experienced LaTeX users may suffer a loss in productivity when LaTeX is used, relative to other document preparation systems. Individuals, institutions, and journals should carefully consider the ramifications of this finding when choosing document preparation strategies, or requiring them of authors.

This study has been criticized for being rigged in various ways to favor Word over LaTeX, which may or may not be the case. However, in my opinion, the much bigger question is this: does the efficiency of the document preparation system used by a researcher even matter?

Most readers of this blog are probably familiar with performance optimization of software. The crucial first step is to profile the program to identify the parts of the code in which most time is being spent. The reason for doing profiling is, that optimization of other parts of the program will make hardly any difference to the overall runtime.

If we want to optimize the efficiency with which we publish research articles, I think it would be fruitful to adopt the same strategy. The first thing we need to do is thus to identify which parts of the process take the most time. In my experience, what takes by far the most time is the actual writing process, which includes reading related work that should be cited. The time spent on document preparation is insignificant compared to the time spent on authoring the text, and the efficiency of the software you use for this task is thus of little importance.

What, then, can you do to become more efficient at writing? My best advice is to start writing the manuscript as soon as you start on a project. Whenever you perform an analysis, document what you did in the Methods section. Whenever you read a paper that may be of relevance to the project, write a one- or two-sentence summary of it in the Introduction section and cite it. The text will look nothing like the final manuscript, but it will be an infinitely better starting point than that scary blank page.

Commentary: The 99% of scientific publishing

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.

Commentary: GPU vs. CPU comparison done right

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.

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

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?

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.