For use in presentations on literature mining, I did a back-of-the-envelope calculation of how much time I would be able to spend on each new biomedical paper that is published. Assuming that all papers were indexed in PubMed (which they are not) and that I could read papers 24 hours per day all year around (which I cannot), the result is that I could allocate approximately 50 seconds per paper. This nicely illustrates the point that no one can keep up with the complete biomedical literature.

When I discovered Wordle, which can turn any text into a beautiful word cloud, I thus wondered if this visualization method would be useful for summarizing a complete paper as a single figure. To test this, I extracted the complete text of three papers that I coauthored in the NAR database issue 2008. Submitting these to Wordle resulted in the three figures below (click for larger versions):

All in all, I think that Wordle does a pretty good job at capturing the essence of each paper: the first cloud shows that STITCH is a database of interactions between proteins and chemicals, the second cloud shows that NetworKIN is a database predictions related to the kinases and phosphorylation, and the third cloud shows that Cyclebase.org is a database of experiments on gene expression during the cell cycle. However, a paper describing a database might be easier to summarize that a typical research paper.

As a final test, I therefore submitted the complete text from my paper “Evolution of Cell Cycle Control - Same molecular machines, different regulation”, which describes the somewhat complex concept of just-in-time assembly to Wordle (click for larger version):

The result is rather less impressive than for the papers from the NAR database issue. Although the word cloud does contain a good selection of words, it fails to convey the main message. I think a large part of the problem is the splitting of multiwords; for example, “cell cycle” becomes two separate terms “cell” and “cycle”. Another problem is that words from different sections of the paper are mixed, which blurs the messages. These two issues could be solved by 1) detecting multiwords and considering them as single tokens, and 2) sorting the terms according to where in the paper they are mainly used.

I yesterday blogged about how the protein half-life data from the O’Shea lab fit well with my earlier analyses of transcriptional regulation during the budding yeast cell cycle and with the just-in-time assembly hypothesis. However, I have now realized that the same data set can be used to test the validity of the sequence-based predictions of protein degradation signals that I relied on for the cell-cycle study.

To this end, I divided the budding yeast proteome into six groups: proteins with a D-box, proteins without a D-box, proteins with a KEN-box, proteins without a KEN-box, proteins with a PEST region, and proteins without a PEST region. For each of these six groups of proteins, I simply plotted the distribution of protein half-lives as a histogram:

The figure shows that for all three degradation signals, proteins with the sequence motif tend to have shorter half-lives than proteins without the motif. These differences are all statistically significant according to the Mann-Whitney U test (D-box, P < 10^-6; KEN-box, P < 0.02; PEST region, P < 10^-15). It is noteworthy that the KEN-box motif gives a far weaker correlation with protein half-live than the two other degradation signals, as it was also the only degradation signal that did not correlate with transcriptional cell-cycle regulation in budding yeast (see supplementary information of Jensen et al., 2006).

In summary, proteins that contain putative degradation signals have significantly shorter half-lives than proteins that do not contain such signals. The only caveat is that long sequences are more likely to match the sequence motifs, and that O’Shea and colleagues found a negative correlation between sequence length and protein half-life. The correlations described here could thus be a secondary effect; however, it is also possible that the presence of degradation signals in long sequences is the missing explanation for their short half-lives.

In relation to an entirely different analysis than the one I will describe here, I downloaded the protein half-life data for budding yeast that was published in PNAS by the O’Shea lab about two years ago:

Quantification of protein half-lives in the budding yeast proteome

A complete description of protein metabolism requires knowledge of the rates of protein production and destruction within cells. Using an epitope-tagged strain collection, we measured the half-life of >3,750 proteins in the yeast proteome after inhibition of translation. By integrating our data with previous measurements of protein and mRNA abundance and translation rate, we provide evidence that many proteins partition into one of two regimes for protein metabolism: one optimized for efficient production or a second optimized for regulatory efficiency. Incorporation of protein half-life information into a simple quantitative model for protein production improves our ability to predict steady-state protein abundance values. Analysis of a simple dynamic protein production model reveals a remarkable correlation between transcriptional regulation and protein half-life within some groups of coregulated genes, suggesting that cells coordinate these two processes to achieve uniform effects on protein abundances. Our experimental data and theoretical analysis underscore the importance of an integrative approach to the complex interplay between protein degradation, transcriptional regulation, and other determinants of protein metabolism.

The idea that transcriptional regulation goes hand-in-hand with protein degradation is fully consistent with the just-in-time assembly hypothesis. I thus examined the distributions of protein half-lives for dynamic (i.e. periodically expressed) and static (i.e. not periodically expressed) proteins:

The histogram suggests that dynamic proteins are shifted towards shorter half-lives relative to static proteins. The difference is indeed statistically significant according to the Mann-Whitney U test (P < 10^-4). This result supports the sequence-based observation that dynamic proteins contain more D-box, KEN-box, and PEST degradation signals than static proteins.

I next tested if the half-life of the dynamic proteins varies during the cell cycle by make scatter plot of the protein half-life as function of the time of peak expression for the corresponding mRNA:

There appears to be no correlation. Together, these analyses indicate that dynamic proteins have shorter half-lives than static proteins, irrespective of when in the cell cycle they are expressed.

Over the years several microarray time-course experiments have been performed to identify the genes that are transcriptionally regulated during the mitotic cell cycle, i.e the periodically expressed genes. Moreover, bioinformaticians have developed many different computational methods for identifying the periodically expressed genes from microarray time-course data.

Below is a list of the experimental and computational analyses of the budding yeast cell cycle that I am aware of (please notify me if you know of other microarray experiments or computational methods):

1. Cho et al., Mol. Cell, 1998
2. Spellman et al., Mol. Biol. Cell, 1998
3. Zhao et al., Proc. Natl. Acad. Sci. USA, 2001
4. Langmead et al., Proc. IEEE Comput. Soc. Bioinformatics Conf., 2002
5. Langmead et al.,RECOMB, 2002
6. Langmead et al., J. Comput. Biol., 2003
7. de Lichtenberg et al., J. Mol. Biol., 2003
8. Johansson et al., Bioinformatics, 2003
9. Wichert et al., Bioinformatics, 2004
10. Lu et al., Nucleic Acids Res., 2004
11. Luan and Li, Bioinformatics, 2004
12. de Lichtenberg et al., Bioinformatics, 2005
13. de Lichtenberg et al., Yeast, 2005
14. Willbrand et al., Bioinformatics, 2005
15. Ahdesmäki et al., BMC Bioinformatics, 2005
16. Chen, BMC Bioinformatics, 2005
17. Qiu et al., Conf. Proc. IEEE Eng. Med. Biol. Soc., 2005
18. Qiu et al., Bioinformatics, 2006
19. Andersson et al., BMC Bioinformatics, 2006
20. Gan et al., Int. Conf. Pattern Recog., 2006
21. Glynn et al., Bioinformatics, 2006
22. Ahnert et al., Bioinformatics, 2006
23. Lu et al., Bioinformatics, 2006
24. Xu et al., LSS Comput. Syst. Bioinformatics Conf., 2006
25. Pramilla et al., Genes Dev., 2006
26. Liew et al, BMC Bioinformatics, 2007
27. Lu et al., Genome Biol., 2007
28. Morton et al., Stat. Appl. Genet. Mol. Biol., 2007
29. Rowicka et al., Proc. Natl. Acad. Sci. USA, 2007
30. Gauthier et al., Nucleic Acids Res., 2008
31. Orlando et al., Nature, 2008

These studies have reported a mixture of ranked and unranked lists of periodically expressed genes. By that I mean that some studies provided a list of genes sorted according to how periodic the expression profiles appear, whereas others simply provide a list of the genes deemed periodic. For the ranked lists, I first checked the publications to see if the authors suggested a cutoff for the number of periodically expressed genes, in which case I followed their recommendations. If the authors suggested multiple lists of varying confidence, I used the highest-confidence list. If no cutoff was proposed, I selected the top-300 genes if the list was based on a single time course and the top-500 genes if the list was based on three or more time courses. It should be noted that both of these cutoffs are on the conservative side since most studies propose 800 or more periodically expressed genes when combining multiple expression time courses.

This meta-analysis resulted in a list of more than 4200 budding yeast genes that are periodically expressed according to at least one of the methods listed above; that is more than two-thirds of all genes encoded by the budding yeast genome!

To investigate further how such a large number of genes can have been proposed to be periodically expressed, I plotted how many of these genes are on how many of the lists of periodically expressed genes:

The histogram reveals that the majority of the over 4200 genes have been proposed by only one or two analyses. It seems reasonable to assume that the genes that have been proposed as periodically expressed by only one or a few methods are less likely to be correct than the ones that many methods agree on. Also, one could expect that taking the consensus of many methods would yield a more reliable answer than using just a single method.

To test these two hypotheses, I compared two different ways of identifying the periodically expressed genes:

1. Ranking the genes based on a single scoring scheme that combines all the available experimental data (Gauthier et al., Nucleic Acids Res., 2008)
2. Ranking the genes based on vote among 30 different methods (not 31; the analysis by Orlando and coworkers was left out of the voting as this dataset is not included in Cyclebase.org)

To benchmark the two methods, I compared the ranked lists to a set of target genes for cell-cycle transcrition factors identified in genome-wide ChIP-on-chip experiments and plotted the fraction of these that were identified as function of the number of genes proposed to be periodically expressed:

The plot confirms that genes proposed to be periodically by multiple methods are more likely to be targets of cell-cycle transcription factors, and are hence more likely to truly be subject to transcriptional cell-cycle regulation. However, it also shows that the list obtained by voting among 30 methods is a bit worse than what is obtained by using the single best method.

This result may come as a surprise to many since meta-servers that combine multiple prediction methods have in the past proven very successful for many other bioinformatics tasks. I suspect that the approach fails in this case for two reasons: first, many of the analyses included perform considerably worse than the best one, and second, most of the methods make use of only half of the available experimental data. It may thus be possible to obtain better results by selecting only a subset of the methods and rerunning each of them on all the available data. So far, however, dictatorship seems to work better than democracy for identification of periodically expressed genes.

I recently took a look at colonization of titles and found that the fraction of papers with colons in their titles is increasing steadily. Intuitively, one would thus expect that the average length of the titles has also increased. The plot below shows that this is indeed the case (not that the y-axis does not begin at zero):

The average title length has increased from 8.5 words in 1950 to 12.5 words in 2008. Strangely, the increase is almost perfectly linear except for a fluctuation in the early 60s - I have no idea why this is the case.

But is the title length of a paper important? I personally expected that papers with short, catchy titles would be cited more than papers with longer, more complex titles. Lacking citation information for individual publications, I thus calculated average title length for publications from each journal and correlated it with the ISI impact factor of the corresponding journal:

No correlation is observed between the impact factor of a journal and the average title length of the papers published therein. So we can conclude that - at least for titles of scientific papers - size does not matter.

You have probably noticed that a high fraction of scientific papers have colons in their titles. Several people have written humorous commentaries on this. Although these authors clearly see the use of colons as a growing trend, they did not present hard evidence for the increase in the usage of colons in the titles of scientific publications.

Out of curiosity, I thus wrote a small script to count the fraction of papers in Medline that have colons in their titles for each of the past 25 years. The result is shown in the plot below (note that the y-axis does not start at zero):

The conclusion is very clear: the fraction of titles with colons has increased linearly from 15% to 24% over the past 20 years. One could object that this effect may be explained by the increase in apologies (which often have a title “Retraction: …”) or by the NAR special issues on databases and web servers (which contain hundreds papers with titles such as “YADB: yet another database”). However, these add up to less than 2% of the papers with colonized titles and are thus insufficient to explain the observed 9% increase.

I have long used a data integration approach to obtain a global picture of eukaryotic cell-cycle regulation. The cell cycle is a popular research topic in part because of its importance for cancer research. I thus recently compared microarray expression data on the human cell cycle to genes with mutations that have been causally implicated in various forms of cancer.

From the Cancer Genome Project website, I downloaded a list of 353 human genes that are implicated in cancer. Using the identifier mapping file from STRING, I was able to automatically map 338 of these genes to the set of human genes from Ensembl that I used in earlier cell-cycle studies. 295 of the 338 genes were present on the microarrays used in the cell-cycle expression study by Whitfield et al. (2002). However, only 23 of these are among the 600 periodically expressed genes identified in the reanalysis by Jensen et al. (2006). The many numbers are illustrated in the diagram below:

By random chance, 295*600/12097 = 15 of the 295 genes would be expected to be periodically expressed, and the enrichment is thus only a bit over 1.5 fold. Although this enrichment is statistically significantly (P < 3%, Fisher’s exact test), the correlation is clearly not strong enough to allow prediction of novel cancer genes.

My step was to look at the evolutionary conservation of the 23 periodically expressed cancer genes. Only 12 of them belong to an orthologous group. Half of them do thus not appear to have orthologs in budding yeast, fission yeast, or Arabidopsis thaliana. Only three periodically expressed cancer genes have orthologs in all of these organisms. One of these genes is periodically expressed onlt in human, one in human and fission yeast, and one in all four organisms (a histone subunit).

In summary, it seems that one cannot say much about cancer based on cell-cycle mRNA expression data. This is perhaps not surprising considering that the transcriptional regulation does not seem to vary much between cancer cells and normal cells.

The groups of Ziv Bar-Joseph and Itamar Simon recently published a paper in PNAS on a new microarray study of the cell cycle of primary human fibroblasts:

Genome-wide transcriptional analysis of the human cell cycle identifies genes differentially regulated in normal and cancer cells

Characterization of the transcriptional regulatory network of the normal cell cycle is essential for understanding the perturbations that lead to cancer. However, the complete set of cycling genes in primary cells has not yet been identified. Here, we report the results of genome-wide expression profiling experiments on synchronized primary human foreskin fibroblasts across the cell cycle. Using a combined experimental and computational approach to deconvolve measured expression values into ‘‘single-cell’’ expression profiles, we were able to overcome the limitations inherent in synchronizing nontransformed mammalian cells. This allowed us to identify 480 periodically expressed genes in primary human foreskin fibroblasts. Analysis of the reconstructed primary cell profiles and comparison with published expression datasets from synchronized transformed cells reveals a large number of genes that cycle exclusively in primary cells. This conclusion was supported by both bioinformatic analysis and experiments performed on other cell types. We suggest that this approach will help pinpoint genetic elements contributing to normal cell growth and cellular transformation.

In contrast to the earlier study by Whitfield et al. (2002), which was performed on HeLa cells, Ziv Bar-Joseph et al. worked on non-transformed fibroblasts. The dataset thus offers a first global view of the differences between the cell cycle of normal human cells and that of cancer cells.

To compare their list of cell-cycle-regulated human genes to the one the I have used so far, I mapped their 480 genes to Ensembl using the mapping file from the STRING database. This resulted in a list of 410 genes, that is 70 genes could not be mapped by the automatic procedure. Whereas this is far from a perfect mapping, it is sufficient to judge the quality of the list.

The plots below show the fraction of a benchmark set that is identified as function of the number of genes that is proposed to be periodically expressed during the cell cycle. In each plot, I compare the results for the list of 410 obtained from the new study by Bar-Joseph et al., the analysis by Whitfield et al., and the reanalysis of the latter dataset by Jensen et al. (2006) (available from Cyclebase.org). To make the comparison as fair as possible, I only considered the subset of genes that were present in both microarray designs. The first plot uses as benchmark a set of 63 genes that have been identified as periodically expressed in targeted small-scale studies:

I also benchmarked the three gene lists against a second benchmark set, which consists of predicted target genes of E2F cell-cycle transcription factors:

Both benchmarks suggest that the three lists are of very comparable quality, but that the list by Whitfield and coworkers is much more inclusive than the one from Bar-Joseph and coworkers. In other words, the former list has better sensitivity whereas the latter has better specificity. This is consistent with the results presented by Bar-Joseph et al., who conclude that their list is more reliable than the previously published list. However, this is probably not due to better quality of the raw expression data, since reanalysis of the data by Whitfield et al. yielded a list with almost identical sensitivity and specificity (that is the red curve is very close to the blue cross in both plots).

Although the two lists of periodically expressed are of comparable quality, they may still contain very different sets of genes. I therefore decided to compare the list of genes that are periodically expressed in the time course on primary fibroblasts and in each of the four time courses on HeLa cells. To make this comparison as easy as possible, I selected the top-364 cycling genes from each of the four HeLa time courses based on the reanalysis by Jensen et al. (2006). The ten Venn diagrams below show all pairwise comparisons of the five lists of 364 genes each:

The average overlap between the list by Bar-Joseph et al. and an experiment from Whitfield et al. is 114 genes. By comparison, the average overlap between the top-364 lists from two individual experiments from Whitfield et al. is 123 genes. Although the overlap may seem low, I thus believe that it is due to the poor reproducibility between microarray time courses rather than due to genuine differences between primary fibroblasts and HeLa cells as suggested by Bar-Joseph and colleagues.

Although cancer cells have to circumvent the regulatory mechanisms that would normally prevent cell proliferation, the cell cycle itself appears to function the same way as in normal cells. In other words, the difference does not lie in the “engine” but in the “brakes”, which have been sabotaged in cancer cells.

Atsushi Miyawaki’s lab from RIKEN has recently published a Cell paper that describes a novel approach for how to monitor cell-cycle progression of individual cells:

Visualizing spatiotemporal dynamics of multicellular cell-cycle progression

The cell-cycle transition from G1 to S phase has been difficult to visualize. We have harnessed antiphase oscillating proteins that mark cell-cycle transitions in order to develop genetically encoded fluorescent probes for this purpose. These probes effectively label individual G1 phase nuclei red and those in S/G2/M phases green. We were able to generate cultured cells and transgenic mice constitutively expressing the cell-cycle probes, in which every cell nucleus exhibits either red or green fluorescence. We performed time-lapse imaging to explore the spatiotemporal patterns of cell-cycle dynamics during the epithelial-mesenchymal transition of cultured cells, the migration and differentiation of neural progenitors in brain slices, and the development of tumors across blood vessels in live mice. These mice and cell lines will serve as model systems permitting unprecedented spatial and temporal resolution to help us better understand how the cell cycle is coordinated with various biological events.

The clever idea was to fuse a red- and a green-emitting fluorescent protein to Cdt1 and Geminin, respectively. Cdt1 is ubiquitinated by SCF^Skp2 at the onset of S phase, which causes it to be rapidly degraded by the proteasome, whereas Geminin is targeted for proteolytic degradation by APC^Cdh1 in late M phase. By fluorescent labeling of two proteins, Miyawaki and colleagues managed to make mouse cells that become increasingly red during G1 phase, yellow around the G1/S transition, and increasingly green through S, G2, and M phase. It is thus possible to monitor the cell-cycle states of individual cells with a microscope.

The movie below follows a few HeLa cells for 3-4 cell cycles:

The authors also show how their construct can be used for imaging the cell-cycle state of the cells in a slice of a mouse brain or a mouse embryo. I expect that this will become an indispensable tool for unraveling the links between cell-cycle control and developmental processes.

For more details, I strongly recommend that you read Jake Young’s post at Pure Pedantry.

There seems to be a new trend in computational biology: worrying about sequence alignments. Over the past couple of months, two high-profile papers have appeared that flaws related to sequence alignment methods.

The first paper appeared in Science Magazine in January this year. Wong and coworkers describe how uncertainties in multiple alignments can lead to errors in different phylogenetic trees:

Alignment Uncertainty and Genomic Analysis

The statistical methods applied to the analysis of genomic data do not account for uncertainty in the sequence alignment. Indeed, the alignment is treated as an observation, and all of the subsequent inferences depend on the alignment being correct. This may not have been too problematic for many phylogenetic studies, in which the gene is carefully chosen for, among other things, ease of alignment. However, in a comparative genomics study, the same statistical methods are applied repeatedly on thousands of genes, many of which will be difficult to align. Using genomic data from seven yeast species, we show that uncertainty in the alignment can lead to several problems, including different alignment methods resulting in different conclusions.

The second paper appeared in Nature Biotechnology. Styczynski and coworkers discovered that the most commonly used substitution matrix, BLOSUM62, was calculated wrongly:

BLOSUM62 miscalculations improve search performance

The BLOSUM family of substitution matrices, and particularly BLOSUM62, is the de facto standard in protein database searches and sequence alignments. In the course of analyzing the evolution of the Blocks database, we noticed errors in the software source code used to create the initial BLOSUM family of matrices (available online). The result of these errors is that the BLOSUM matrices — BLOSUM62, BLOSUM50, etc. — are quite different from the matrices that should have been calculated using the algorithm described by Henikoff and Henikoff. Obviously, minor errors in research, and particularly in software source code, are quite common. This case is noteworthy for three reasons: first, the BLOSUM matrices are ubiquitous in computational biology; second, these errors have gone unnoticed for 15 years; and third, the ‘incorrect’ matrices perform better than the ‘intended’ matrices.

Upon casual reading of these publications, one could get the idea that over a decade of work based on alignments, sequence similarity searches, and molecular evolution is wrong. Fortunately, this does not appear to be the case.

Starting with the second paper, I applaud the authors for discovering a mistake in such an established method, and I agree with them that it is remarkable that it has not been noticed before. However, I do not think that it is surprising that the ‘incorrect’ matrices work very well. Although they were not calculated as intended, the BLOSUM matrices have become the de facto standard precisely because they work as well as they do.

Regarding the first paper, I think it is fair to say that anyone working on multiple alignments and phylogeny are well aware that uncertain alignments can lead to wrong phylogenetic trees. This is why almost everyone uses programs like Gblocks to remove the ambiguous parts of their alignments before moving on to constructing phylogenetic trees. Unfortunately, Wong et al. instead constructed two sets of trees for each of the six multiple alignment methods: one based on the complete alignments, and one in which they excluded all gapped sites from the phylogenetic analysis. The latter is not equivalent to using a blocked alignment, since not all ambiguously aligned sites contain gaps, and since not all sites with gaps are ambiguously aligned.

Wong and coworkers subsequently compared the trees that they obtained using the six different alignment programs and found disagreements for almost half of all yeast proteins. This number may sound shockingly high, but I find it to be misleading in several ways. First, “disagreement” was defined as at least one of the six trees disagreeing with the others – much of the disagreement could thus be due to a single poorly performing alignment program. This definition also implies that the results can only get worse by adding more alignment methods to the comparison. Second, the comparison was not limited to the trees that are supported by bootstrap analysis – much of the disagreement is thus due to trees that we already know should not be trusted.

In my view, it would be more fair to make the comparison along the following lines:

• Align the sequences as done by Wong et al.
• Remove ambiguously aligned sites with Gblocks
• Construct phylogenetic trees based on the blocked alignments
• Calculate the bootstrap support for each tree
• Discard trees with poor bootstrap support
• Calculate the agreement on tree topology for each pair of alignment methods

This procedure will ensure that trees are not distorted by the unreliable parts of the alignments, that comparisons are not based on trees we know are unreliable, that the results are not skewed by a single poorly performing alignment method, and that the numbers remain comparable if more alignment methods are added. I have already downloaded all the alignments and run then through Gblocks; please let me know if you would like to continue the analysis from that step, and I will arrange a way to transfer the files.

Time might prove me wrong, but I expect that such an analysis will show that alignment uncertainty is not a major factor that needs to be taken into account when constructing phylogenetic trees.

I am now back from two weeks in Italy where I experimented with live blogging. You have probably noticed that some presentations from the meeting at CoSBi in Trento were covered on Buried Treasure within a matter of minutes of them ending. Also, quite a number of pictures were posted in the associated Picasa web album while the presentations were still ongoing. Here is a brief explanation of how I planned to pull this off and how it worked in practice.

My original plan was to use WordPress through the web browser on my smartphone together with a foldable bluetooth keyboard. This was how I first imagined that my live-blogging platform would look:

It seemed a good idea at the time, but there were a couple of “minor” problems:

• The mobile web interface for WordPress does not enable you to upload pictures.
• The full web interface for WordPress works neither in Microsoft Internet Explorer Mobile nor in Opera Mobile.

I thus started looking around for alternative clients for WordPress and eventually found ShoZu, which allows you to upload pictures from your phone to a variety of services including WordPress blogs and Picasa web albums. However, it is not a true blogging tool and only enables you to write a short description for each picture. I thus accepted to the real blog posts would be written on my laptop, whereas the following platform would be used for live blogging in the form of images with short descriptions:

This seemed like an even better idea at the time, but again I ran into a few technical problems:

• Due to strange combinations of firewalls, HTTP proxies, and complex login web pages, I never managed to get my smartphone reliably connected to the internet.
• The camera in the smartphone was unable to take even half decent picture under the poor light conditions.

In reality, I thus ended up using my old Apple PowerBook G4 and my Lumix TZ3 camera. They got the job done in terms of covering the presentations, but live blogging from poster sessions was practically impossible.

I have now put on my thinking cap to come up with a live-blogging platform that would work for poster sessions. You generally have too little time for too many posters, so it has to be very fast to snap a photo and post it. The light is often poor and people tend to use too small fonts on their posters, so you need a good camera to get a readable result. Finally, the lack of space and tables prevents you from using a laptop. The Eye-Fi Card  might be a solution as it would enable me to upload images directly from my camera to, for example, a Picasa web album or Flickr. Please let me know if you have any ideas, experiences, or thoughts on this.

The group of Donald F. Hunt at University of Virginia has recently published a paper in PNAS that describes a new phosphoproteomics study of budding yeast:

Analysis of phosphorylation sites on proteins from Saccharomyces cerevisiae by electron transfer dissociation (ETD) mass spectrometry

We present a strategy for the analysis of the yeast phosphoproteome that uses endo-Lys C as the proteolytic enzyme, immobilized metal affinity chromatography for phosphopeptide enrichment, a 90-min nanoflow-HPLC/electrospray-ionization MS/MS experiment for phosphopeptide fractionation and detection, gas phase ion/ion chemistry, electron transfer dissociation for peptide fragmentation, and the Open Mass Spectrometry Search Algorithm for phosphoprotein identification and assignment of phosphorylation sites. From a 30-microg (approximately 600 pmol) sample of total yeast protein, we identify 1,252 phosphorylation sites on 629 proteins. Identified phosphoproteins have expression levels that range from <50 to 1,200,000 copies per cell and are encoded by genes involved in a wide variety of cellular processes. We identify a consensus site that likely represents a motif for one or more uncharacterized kinases and show that yeast kinases, themselves, contain a disproportionately large number of phosphorylation sites. Detection of a pHis containing peptide from the yeast protein, Cdc10, suggests an unexpected role for histidine phosphorylation in septin biology. From diverse functional genomics data, we show that phosphoproteins have a higher number of interactions than an average protein and interact with each other more than with a random protein. They are also likely to be conserved across large evolutionary distances.

As is so often the case with experimental papers, no comparison is provided to earlier studies. I thus decided to compare the set of phosphoproteins identified by Hunt and coworkers to the set of Cdc28p substrates identified in two studies by the Morgan lab as well as to the proteome-wide, sequence-based predictions made by NetPhosK:

The Venn diagram obviously shows that each of the three sets contains a considerable number of phosphoproteins that are not present in any of the other sets. This was to be expected since the three methods are fundamentally very different. The dataset from the Hunt lab includes proteins that are phosphorylated by other kinases than Cdc28p; however, it is limited in the sense that low-abundance phosphopeptides are typically missed by MS studies. Conversely, the set from the Morgan lab consists only of Cdc28p substrates, but is likely to have much better coverage of low-abundance phosphoproteins. Finally, the set of Cdc28 substrates from NetPhosK is likely to contain a considerable number of false positives as they are predicted from the protein sequence alone.

As a matter of fact, I find the overlap between the three sets to be surprisingly good. Even if we assume that the dataset from the Morgan lab contains no false positives, the overlap suggests that the new dataset from Hunt and coworkers captures one third of all phosphoproteins in budding yeast; assuming errors in both datasets increases this estimate. It is also noteworthy that NetPhosK misses only 22% of the Cdc28p that were identified by the Morgan lab and supported by the new data from the Hunt lab, although this high coverage is probably obtained at the price of many false positive predictions.

You may have heard about the controversial movie “Expelled: No intelligence Allowed” by Ben Stein in which people behind the intelligent design movement whine about being suppressed the scientific community. The truth is obviously that intelligent design is not a falsifiable theory and hence simply does not qualify as science.

However, the movie is also controversial in other respects. To start with the producers conned both Richard Dawkins and fellow blogger PZ Myers into participating in the movie by interviewing them under false pretense.

Richard Dawkins and PZ Myers thus both registered for participating in a public screening of the movie. But while queuing up for the movie, PZ Myers was identified by security officers and told to leave the premises - immediately! Oh the irony, oh the double standard. They make a movie about suppression of views, they call it “Expelled”, and then they expel a person whom you conned into participating in the movie because you disagree with his views.

But it gets even better. The very same security officers were apparently oblivious to the fact that Richard Dawkins was standing right next to PZ Myers and thus let him enter to watch the movie. PZ Myers immediately wrote a blog post about it, while the movie was still being shown to the audience - including Richard Dawkins.

After the movie, Richard Dawkins (of course) stood up and asked why PZ Myers was not allowed to see the movie. The answer? Because he did not have a ticket and was thus a gate crasher! Very interesting explanation since it was not a ticket event - you simply had to register a seat, which PZ Myers had done.

The two gentlemen have now posted an interesting little discussion on YouTube in which they humorously describe the incident as well as just how bad the movie really is:

Richard Dawkins also reveals that Expelled includes one of the beautiful movies produced by the multimedia team at Harvard. You really have to wonder if they actually got permission for that, if they conned the people at Harvard as well, or if they just resorted to plain old plagiarism. In any case, this has to be one of the biggest PR disasters ever made by the intelligent design movement.

Expelled from Expelled: no intelligence involved.

I have now arrived in Bertinoro where I will be lecturing on the 8th Course in Bioinformatics for Molecular Biologists. And after a fight with network configuration and power outages, I also eventually managed to get online.

All the speakers are housed at the castle, which has a fantastic view over the surrounding area - also by night:

The scientific part of the meeting was kicked off by H. Werner Mewes:

I am sure there will be many interesting lectures to follow - and I hope that the audience will think that mine is one of them.

Orkun Soyer has just finished his excellent presentation at CoSBi on the use of toy models for understanding the principles that govern biological pathways, in particular signaling pathways. One can obviously imagine several scenarios for how pathways came about:

The key point, however, is that we might be able to understand something about pathways through computational studies of simple toy models. The toy model discussed throughout the talk was bacterial chemotaxis:

The idea is that evolution can to some extend be approximated as an optimization process, in which the objective function corresponds to fitness. In case of the “tumble or swim” problem, computational simulations allowed simple regulatory network to evolve that mimic the food-finding behavior of bacteria.

He also presented an interesting view on how biological complexity has evolved. The idea is to show how complex systems can evolve even if assuming a (weak) selection against complexity:

I think that his results provide a lot of insight into how real signaling may have evolved, although all the simulations are based on simplistic toy models. I recommend that you download Orkun Soyer’s slides if you want to know more.

This talk ends the Computational and Systems Biology course at CoSBi.

As most readers of this blog are probably aware, Mohammad Warda and Jin Han published a paper in Proteomics that contained several pages of text copied from unreferenced sources. Exactly one month ago it was thus retracted “due to a substantial overlap of the content of this article with previously published articles in other journals”.

Plagiarism is, however, not the main issue. The paper by Warda and Han also claimed to disprove the endosymbiotic origin of mitochondria, mentioned fingerprints of a mighty creator, and proposed mitochondria to be the missing link between the body and the preserved wisdom of the soul!

It remains a mystery how a manuscript with such unsubstantiated claims was accepted for publication in a respectable, peer-reviewed journal. The retraction notice by Proteomics made no attempt to explain this, and their approved draft press release merely states that it was due to “human error”. I would have been really worried if it could happen without human error being involved. Although this draft was approved a month ago, the final version is nowhere to be found on the internet, also not on the Proteomics website. I thus wonder if an official press release was ever published.

Attila Csordas, PZ Myers, Steven Salzberg, and I have decided to mark the one month anniversary of the retraction by pointing out the important questions that still remain to be answered by the Editor in Chief of Proteomics, Prof. Michael J. Dunn.

The manuscript contains four parts with unsupported claims that should have been caught by any peer reviewer or editor:

1. Title - “Mitochondria, the missing link between body and soul”.
2. Abstract - “These data are presented with novel proteomics evidence to disprove the endosymbiotic hypothesis of mitochondrial evolution that is replaced in this work by a more realistic alternative”.
3. Section 3.4 - “More logically, the points that show proteomics overlapping between different forms of life are more likely to be interpreted as a reflection of a single common fingerprint initiated by a mighty creator than relying on a single cell that is, in a doubtful way, surprisingly originating all other kinds of life”.
4. Conclusions - “We realize so far that the mitochondria could be the link between the body and this preserved wisdom of the soul devoted to guaranteeing life”.

My questions to Michael J. Dunn are when in the publication process these parts first appeared:

1. Were they already in the initial version that was submitted to Proteomics and sent out for peer review?
2. Did they appear in a revised version that was sent to the peer reviewers?
3. Were they introduced in a revised version that was accepted without sending it to the reviewers?
4. Or were they added at the copy editing stage, that is after the manuscript had formally been accepted?

I want to make explicit that the aim with these questions is not to place the blame but to elucidate what went wrong in the publication process. To prevent similar incidents inthe future, it is important to know whether the editor and the peer reviewers overlooked glaring flaws of the manuscript or if the flawed parts were introduced after peer review. It is not important who the editor and the peer reviewers are. I sincerely hope that Prof. Dunn will help improve the procedures for peer reviewed publication by answering the questions in this post and in the related posts on PIMM, Pharyngula, and Genomics, Evolution, and Pseudoscience.

Gabor Balazsi has just finished a very interesting presentation on the interplay between molecular networks, gene expression noise, and evolutionary selection - here is the opening slide:

In the first part of his talk he gave a nice introduction to global network topology and network motifs - this should be nothing new to people familiar with the work of the Barabasi and Alon labs. He also explained the “Commander, Intermediate, Executor” model for hierarchical regulatory networks, which I had personally not heard about before, and the concept of “origons”, which seems quite use for understanding the response of large signaling networks to environmental cues.

The second part of his talk was about stochastic noise in gene expression. Genetically identical cells in a culture may express the same protein at different levels; this is a result of random noise influencing transcription, mRNA degradation, translation, and protein degradation. This is simply a consequence of low copy numbers giving rise to stochastic, as opposed to deterministic, behavior.

Finally, he talked about how noise at the level of gene expression can influence the survival of species in a changing environment. This part of his talk was kicked off with the funniest slide of his presentation:

I guess it should be seen as a lesson on how not to do. He made some very good points about how noise plays hardly any role in multicellular organisms that reproduce sexually. By contrast, stochastic variation within clonal bacterial cultures provides much higher chance of survival when faced with sudden stress such treatment with anti-bacterial drugs. I would have liked to hear more about this, but unfortunately there was not much time left for this part of the presentation due to technical problems with the projectors. It looks like Guy Shinar picked the safe strategy for his presentation.

All in all, I found it to be a really inspiring talk. I have uploaded his slides in case if you want to take a look at it.

Fifteen minutes ago, Attila Csikasz-Nagy opened the Computational and Systems Biology Course at CoSBi in Trento, Italy:

Over the coming week, I will be covering the most interesting presentations and posters here on the blog and in the Picasa web album.

David Botstein’s group at Princeton recently published a paper in Molecular Biology of the Cell with the title “Coordination of Growth Rate, Cell Cycle, Stress Response, and Metabolic Activity in Yeast”. As described in their abstract, they found interesting several correlations between the transcriptional responses to changes in growth rate and the regulation in response to stress and during the metabolic cycle:

We studied the relationship between growth rate and genome-wide gene expression, cell cycle progression, and glucose metabolism in 36 steady-state continuous cultures limited by one of six different nutrients (glucose, ammonium, sulfate, phosphate, uracil, or leucine). The expression of more than one quarter of all yeast genes is linearly correlated with growth rate, independent of the limiting nutrient. The subset of negatively growth-correlated genes is most enriched for peroxisomal functions, whereas positively correlated genes mainly encode ribosomal functions. Many (not all) genes associated with stress response are strongly correlated with growth rate, as are genes that are periodically expressed under conditions of metabolic cycling. We confirmed a linear relationship between growth rate and the fraction of the cell population in the G0/G1 cell cycle phase, independent of limiting nutrient. Cultures limited by auxotrophic requirements wasted excess glucose, whereas those limited on phosphate, sulfate, or ammonia did not; this phenomenon (reminiscent of the “Warburg effect” in cancer cells) was confirmed in batch cultures. Using an aggregate of gene expression values, we predict (in both continuous and batch cultures) an “instantaneous growth rate”. This concept is useful in interpreting the system-level connections among growth rate, metabolism, stress, and the cell cycle.

Because of my interest in cell cycle, their results regarding growth rate and cell-cycle regulation caught my attention. In Figure 6 of their paper, Brauer et al. show the slope distribution for the genes belonging to each of the phase-specific clusters defined by Spellman et al. (1998). The only trend they observe is that genes expressed at the G1/M transition.

I decided to redo the cell-cycle part of their analysis in a slightly different manner, hoping that I would be able to get a stronger signal than they did. Rather than using the 800 periodically expressed genes proposed by Spellman et al. (1998), I thus made use of the list of 600 periodically expressed genes from de Lichtenberg et al. (2005). Like Brauer et al., I found no difference in growth-rate response between cell-cycle-regulated genes and other genes. To analyze the phase-specific expression, I chose to plot the peak time distributions for genes that are up- and down-regulated in response to increasing growth rate:

In agreement with Brauer et al., genes that are down-regulated at high growth rates appear to have a striking preference for being expressed at the G1/M transition. However, manual inspection of these genes revealed that more than half of them belong to the Y’ family of DNA helicases, which are encoded by the sub-telomeric regions (striped blue bars). The trend observed by Brauer et al. is thus presumably not due to slower growing cells spending more time in M-G1 phase as suggested by the authors, Instead, it is likely an artifact of the many Y’ helicase genes found in the sub-telomeric regions of budding yeast, which are so highly homologous that they can cross hybridize on microarrays and hence all appear to be periodically expressed with identical peak times.

After correcting for this the down-regulated genes show a weak preference for being expressed during M phase whereas the up-regulated genes tend to be expressed in late G1 and S phase. However, the peak-time distributions of up- and down-regulated do not differ significantly from that of all cell-cycle-regulated genes (Kolmogorov-Smirnov test).

In summary, my reanalysis suggests that there is no correlation between the transcriptional response to changes in growth rate and transcriptional cell-cycle regulation. It also reiterates the importance of manually inspecting the results from statistical analyses - they may be highly significant for all the wrong reasons.

When I launched this blog just a month ago, I wrote that it was an experiment and that I would later evaluate if it was worthwhile to continue. I am very happy to already now be able to declare the experiment a success. With over 2000 views during the first month and around 50 subscriptions to the RSS feed, Buried Treasure is certainly worthwhile writing - I hope you also find it worthwhile reading.

In the near future I plan to experiment with live blogging. The idea is to provide coverage of the conferences and meetings that I attend. To avoid polluting my blog with dozens of short posts consisting of just one or two sentences and a photo, I have setup an associated Picasa web album. This is where the main action will happen - I plan to post only highlights and summaries here on the blog. You can find the RSS feeds for the album and the blog in the sidebar.

“Oh, you work on systems biology? So do I!”

New buzzwords to describe scientific disciplines and technologies seem to pop up every year. For the fun of it, I have developed a small web resource, BuzzClouds, that provides a visual overview of the latest buzzwords in biomedicine.

Without destroying your weekend with mathematical formulas, here is how the BuzzCloud selection and visualization method works:

• A list of potential buzzwords is constructed by extracting all one- and two-word phrases ending on -ics, -ology, -omy, -phy, -chemistry, -medicine, or -sciences. These endings were select to get buzzwords that correspond to scientific disciplines and technologies.
• The potential buzzwords are ranked according to a score th