Category Archives: Research

Visualization of internal repeats in proteins (or DNA)

There’s a number of protein families that have internal repeats (like TPR, Armadillo, ankyrin etc.). I’m very interested in many of them for reasons I will explain in other post. Assessing arrangement of these repeats is straightforward in majority of cases – most of them tend to occur next to each other, with little or no insertions between them (finding them at first is completely different story). However, there are proteins where internal repeats are separated by other domains or repeats, which can result in a real mess (or in scientific language: mosaic-like architecture). When couple of months ago I looked for some visualization method that would allow me to have a quick overview of internal structure of such proteins, I’ve stumbled across The Shape of Song – visualization method developed by Martin Wattenberg, researcher at IBM. This fitted my requirements so I’ve implemented it with some help of Processing (and which I’ve added later to a protein analysis server that has a chance to be published next month). Resulting visualization is below:

Internal repeats in a protein

Repeats are colored according to repeat type and are connected according to repeat family. If you think about it in terms of SCOP (Structural Classification of Proteins) hierarchy, colors represent class, while arcs connect superfamilies. The longer and more complicated analysed sequence is, the more useful this approach seems to be, so for short proteins typical domain bubbles would work better.

People that are into genomic sequences may notice similarity of this approach to Circos developed by Martin Krzywinski (whose work I really admire, especially on HDTR). Basically the idea behind both is pretty much the same, but I’ve never thought about straightening that circle until I saw The Shape of Song. My thinking is sometimes dramatically schematic…


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CLANS – java tool for cluster analysis of sequences

As frequent visitors of this blog have already noticed, I am a big fan of different tools for data visualization. Today I would like to point you to java software called CLANS (CLuster ANalysis of Sequences) developed by my former colleague Tancred Frickey. CLANS runs (PSI)BLAST on your sequences, all vs all, and clusters them in 2D or 3D according to their similarity. This method allows for rapid classification of huge datasets and has the advantage over, lets say, phylogenetic tree, that one can quickly assess results of the clustering in a visual way (I cannot imagine making any sense of looking at phylogenetic tree with 1500 branches, while the graphical output, as on the animation below, is pretty easy to read).

CLANS animation

Beauty of the idea behind CLANS is that you can apply this method almost to any dataset which can be translated into all-vs-all relations. CLANS page has examples from protein clustering, microarray analysis and (which I like the most) image showing how standard aminoacids cluster in space according to BLOSUM62.


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Tracking changes in a multiple sequence alignment

I had few free hours during this weekend so I’ve hacked together couple of scripts that in theory could help me visualize changes between subfamilies in the protein multiple sequence alignment. In essence, I took the alignment, chose a master sequence that correspond to a known structure, removed all columns with gaps in the master sequence, and visualized fragments of the alignment (sliding window with 15 sequences) with Weblogo – software for preparing sequence logos from alignments. On the video below you can see:

  • two boxes showing the same template structure (second is just rotated); size of C-alpha atoms correspond to overall conservation at that position; first few residues do not have corresponding positions in the alignment
  • sequence logo of actual alignment window
  • sequence logo of the whole alignment – as a reference

There are several of things I’m not yet happy with. First of all, visualization of changes on the structure is hardly readable, even with video of much higher quality (probably I should do it with Chimera’s “worm” representation). Second thing is that I have no information which species/proteins I’m looking right now at (another box with highlights on a species tree of the family?). Also, I should remove some redundancy from the alignment; sometimes sliding window contains copies of the same protein. But overall it looks promising enough to convince me to spend few more hours on this small project. However, I would probably do the final version with Processing.


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Genome Commons – knowledgebase of human genetic variation

The title says it all – have a look at Steve Brenner’s commentary in Nature (looks like its freely accessible) and the Genome Commons web page.

clipped from
The Genome Commons and Genome Commons Navigators are open resources I propose to assist with personal genome interpretation. A commentary describing these has been published in Nature, and additional versions of those musings and more details may be found on the about page of this site.

Thank you very much for your interest. Please explore the site and offer your thoughts. The background page offers some historical context for the Genome Commons idea. More valuable context is given by the resources page, which summarizes some existing resources for personal genome interpretation, with links to much larger lists of resources. The blog will have updates and discussions.

  blog it
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Posted by on October 17, 2007 in Clipped, Research


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Thoughts on CASP – Critical assessment of methods of protein structure prediction

I’ve just read an introduction to the supplemental issue of the journal PROTEINS, dedicated to the most recent round of the CASP experiment. It describes the progress of the protein structure prediction over the last few CASP editions.

The list of advancements include:

  • improvement of the homology modelling: one of the issues in template-based modelling of protein structures was that a final model wasn’t closer to the real structure than a template; now we have statistically significant (although very small) improvement thanks to the multi-template based modelling
  • fully automated methods are much closer to human predictors than ever: many groups use models from servers as their starting point and usually they don’t improve them that much

I believe that this was possible thanks to the progress that has been made in the area of sequence homology searches. Finding similarity between two sequences well beyond any reasonable identity thresholds is now doable thanks to profile-to-profile comparison, meta-servers (joining predictions from many different methods) or recent hmm-to-hmm algorithms (comparison of Hidden Markov Models). If you can find a suitable template for your protein, the rest is then much easier, isn’t it?

There are of course fields that still need some work. One of these often stirs a lot of discussion: automated assessing of model similarity to the real structure. The current methods have proven their suitability, I definitely agree. However I hope that at some point the protein structure comparison software will refuse to superimpose eight- and ten-stranded beta-barrels or left- and right-handed coiled-coil with a message: “It doesn’t make sense.”

CASP 7 logo

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Posted by on October 10, 2007 in Comments, Papers, Research, Structure prediction


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Manual sequence analysis – some common mistakes

This is a topic I probably will come back to on many occasions. Publication with very wrong sequence analysis like the one Stephen Spiro pointed out on his blog is not an exception. I may agree that large scale analysis can stand quick and dirty treatment of protein sequence (and some error propagation at the same time). In large scale analysis nobody cares if the domain assignment is 100% right (it isn’t), if there are false positives (there are) or even if the material to begin with (protein sequences for example) is free of errors (it is not) – as long as the overall quality of the work is acceptable. However, this optimistic approach cannot be applied to the manual protein sequence analysis. Simply errors introduced in such cases are a way more important. How to avoid some of these errors? A few common mistakes that come to my mind are:

  • lack, not accurate or quick and dirty domain annotation: this probably is a topic for separate post, but in short – relying on a single method or strict E-value, excluding overlaps, ignoring internal repeats, forgetting about structural elements like transmembrane helices etc. lead to mistakes in domain annotation
  • running PSI-BLAST search on unclustered databases: the profile for many query sequences will get biased and diverge in a random direction if the PSI-BLAST runs on the unclustered database (remember 500 copies of the same protein in the results?); after all these years I still don’t get why NCBI does not provide nr90 (non-redundant db clustered at 90% identity threshold) for the PSI-BLAST
  • running PSI-BLAST without looking at the results of each run: if you don’t assess what goes in, you risk allowing some garbage
  • masking low-complexity, coiled-coils and transmembrane regions in BLAST search on every single occasion: while most of the times this is a valid approach, there are cases where the answer is revealed after turning the masking off
  • skipping other tools for sequence analysis like predictors of signal sequences, motifs, functional sites
  • skipping analysis of a genomic context: while not applicable to all systems, analysis of the genomic context may influence dramatically function prediction

It’s so far all I could think of. Do you have any other suggestions? Let me know.

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Posted by on September 25, 2007 in Comments, Research, Research skills