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Category Archives: bioinformatics

Structure prediction without structure – visual inspection of BLAST results

portschemaMy recent post on visual analytics in bioinformatics lacked a specific example, but I’m happy to finally provide one (happiness comes also from the fact that respective publication is finally in press). The image above shows a multiple pairwise alignment from BLAST of a putative inner membrane protein from Porphyromonas gingivalis. Image is small but it does not really matter – colour patches seem to be visible anyway.

Regions marked with ovals are clearly less conserved, than other part of the protein. There are five hydrophobic (green patches, underlined with blue lines) regions in this alignment (I ignore N-terminus, as this is likely the signal peptide), however the three inner ones appear to be of similar length, while the outer ones seem to be of the half as long as the inner ones. If we assume that the single unit is the short one, we can summarize the protein as follows: 8 beta structures, four long loops, for short loops. It looks like an eight-stranded outer membrane beta-barrel. Almost structure prediction, but without a structure.

I could end the story here, but the model didn’t fit previously published data. Its localization in the inner membrane was confirmed by an experiment, however pores in the inner membrane are considered very harmfull 😉 . Fortunately, one of my colleagues explained to me that particular localization technique is not 100% reliable, so I gathered more evidence, created detailed description of topology and the other group has designed experiments which confirmed my visual analysis.

Lessons learned? Maybe without this feedback on quality of that experimental technique, I would still claim that this is OM beta-barrel. Or maybe not. But I’ve learned that to safely ignore experimental results, one needs a more than a intuition. Also, it shows that sometimes looking at the results, is all one needs to make a reasonable prediction (I still have no idea what were E-values of these BLAST hits, but does it matter?).

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Posted by on February 3, 2009 in bioinformatics, Research, Visualization

 

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Database query and ranked results

The Autophagy network extracted from the recen...
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Already some time ago I’ve  read a piece by Marcelo Calbucci: Is it a database or a search engine?. While it deals with search information within a real estate database, I think his comments are applicable in the many areas of life sciences.

In short, Marcelo points out that people miss a lot of interesting entries while looking for a house, because of inflexibility of the query; number of bedrooms, price, distance from some point – these are all set. However, users are flexible and in such case need rather a search engine that gives them close enough answer or allows to specify weight to each filter.

In life sciences we do search for similarities and analogies all the time. Sometimes it’s direct comparison of sequences, on other occasion is high-level meta-comparison between two systems. And while we have various (statistical) metrics of similarities and they sometimes become a part of a database designs, interfaces of biological databases don’t allow to rank query results according to these metrics. For example I can easily find all human proteins related to disease X or disease Y or disease Z, although I cannot specify that I want proteins related to Z AND Y first on the list. Other example would be searching PubMed – I can look for articles related to “synthetic biology”, but I have no way to specify, that I want papers by James Collins from HHMI AND articles related to these papers to be first on the list. I guess it is possible to obtain such results without going through the whole list, but I doubt the method will be very simple. Filtering still seems to be neglected aspect of database design in life sciences.

My dream biological search engine would have a series of sliders (or ideally, I would like to have a device with series of mechanical knobs attached to the computer) and would allow me to dynamically change weights of various aspects of the query and see immediately how it affects the results. It would be something resembling interactivity of Gapminder World, but on dynamically generated data. Technology and proof of concept seems to be there, but I guess we need to wait quite a few years before this approach will be adopted within life sciences.

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Posted by on January 22, 2009 in bioinformatics, Data mining, Software

 

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Science and art. New theme for the new year.

Bose–Einstein condensate In the July 14, 1995 ...
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In 2007 this blog was mainly scientific. Last year I’ve explored possibilities of being a freelance scientist. As I’ve announced earlier on Twitter, theme for this year will be science and art. And I should already explain: I’m not going to write about such extraordinary artistic endeavours like creating music from DNA/protein sequence, try to convince you that science is beautiful or state that my pictures of molecules are the true art. I’m more interested to see if there’s anything I can learn from The Art, its history and its approach. While I’m not yet sure what I will end up writing about, here are two topics I may start with to see in which direction this theme unfolds.

Holistic approach to science

This is something I was thinking about for a while. I didn’t come up with anything interesting, but I think it’s worth exploring further. Some first ideas were coming from reading Wikipedia entry about lateralization of brain functions or Steve Brenner’s comments about “middle-out approach” (as opposed to top-bottom or bottom-up). I’ve also found peculiar Mihaly Csikszentmihalyi‘s answer to Edge 2009 question, where he wrote about “The end of analytic science”. Very recently I’ve also found interesting interview with Daniel Tammet, autistic savant, who explains his theory of exceptional creativity coming from “hyper-connectivity” of distinct brain regions. I have no yet idea whether there’s anything practical to find in such theories, but their exploration will be appealing enough.

Dashboard design for scientific data

This is something more practical, although again I expect to get no points for that topic. Information dashboard is a very cool concept rarely used in life sciences. One of the best known examples in bioinformatics may be InterPro domain page (here’s example entry on pore-forming lobe of aerolysins) – almost everything is on the single page, it has some nice graphical overviews of particular features (like species distribution), etc. It’s not the prettiest dashboard around, but at least you don’t need to click anywhere to have an overview of stored information (compare it to PFAM approach to similar domain). I hope to learn what makes a great dashboard, experiment a little and see if the result is worth the effort.

Other topics

I still will be blogging about bioinformatics, visualizations and open science – that stays in place. Especially the last topic is something I expect to write about quite a lot – my feeling is that this year will bring couple of interesting events in this area (and I hope to initiate some of them). So if you don’t like the “science and art” theme, I think I will give you some other reasons to visit this blog once in a while.

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Posted by on January 11, 2009 in bioinformatics

 

Bioinformatics is a visual analytics (sometimes)

Short description of my research interest is “I do proteins” (I took this phrase from my friend Ana). I try to figure out what particular protein, protein family, or set of proteins does in the wider context. Usually I start where automated methods have ended – I have all kinds of annotation so I try to put data together and form some hypothesis. I recently realized that the process is basically visualizing different kind of data – or rather looking at the same issue from many different perspectives.

It starts with alignments. Lots of alignments. And they all end up in different forms of visual representation. Sometimes it’s a conservation with secondary structure prediction (with AlignmentViewer or Jalview):

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Sometimes I look for transmembrane beta-barrels (with ProfTMB):

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Sometimes I try to find a pattern in hydrophobicity and side-chain size values across the alignment (Aln2Plot):

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Afterwards I seek for patterns and interesting correlations in domain organization (PFAM, Smart):

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Sometimes I map all these findings onto a structure or a model that I make somewhere in the meantime based on found data (Pymol, VMD, Chimera):

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I also try to make sense out of genomic context (works for eukaryotic organisms as well – The SEED):

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I investigate how the proteins cluster together according to their similarity (CLANS):

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And figure out how the protein or the system I’m studying fits into interaction or metabolic networks (Cytoscape, Medusa, STRING, STITCH):

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If there’s some additional numerical information I dump it into analysis software (R, for simpler things DiVisa):

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And I make note along the process in the form of a mindmap (Freemind, recently switched to Xmind, because it allows to store attachments and images in the mindmap file, not just link to them like Freemind does):blog-0010

So it turns out that I mainly do visual analytics. I spend considerable amount of time on preparing various representations of biological data and then the rest of the time I look at the pictures. While that’s not something every bioinformatician does, many of my colleagues have their own workflows that also rely heavily on pictures. For some areas it’s more prominent, for others it’s not, but the fact is that pictures are everywhere.

There are two reasons I use manual workflow with lots looking at intermediate results: I work with weak signals (for example, sometimes I need to run BLAST at E-value of 1000) or I need to deeply understand the system I study. Making connections between two seemingly unrelated biological entities requires wrapping one’s brain around the problem and… lots of looking at it.

And here comes the frustration. I counted that I use more than twenty (!) different programs for visualization. And even if I’m enjoying monitor setup 4500 pixels wide which is almost enough to put all that data onto screen, the main issue is that the software isn’t connected. AlignmentViewer cannot adjust its display automatically based on the domain I’m looking at or a network node I’m investigating – I need to do it by myself. Of course I can couple alignments and structure in Jalview, Chimera or VMD but I don’t find such solution to be usable on the long run. To have the best of all worlds, I need to juggle all these applications.

I’ve been longing for some time already for a generic visualization platform that is able to show 2D and 3D data within the single environment, so I follow development of SecondLife visualization environment and Croquet/Cobalt initiatives. While these don’t look very exciting right now, I hope they will provide a common platform for different visualization methods (and of course visual collaboration environment).

But to be realistic, visual analytics in biology is not going to become a mainstream. It’s far more efficient to improve algorithms for multidimensional data analysis than to spend more time looking at pictures. I had already few such situations when I could see some weak signal and in a year or two it became obvious. But I’m still going to enjoy scientific visualization. I came to science for aesthetic reasons after all. 🙂

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Synthetic biology is not engineering, it’s a programming

Vierpunktlager, geteilter Innenring, zerlegbar...

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Topic of this post has been sitting in my head for the very long time, but I couldn’t come up with a good enough opening. I’ve found it recently in the comments thread under the post on systems biology by Derek Lowe over at In the Pipeline. Citing Cellbio:

A trick of the human mind has us believe that if we rename something, we have changed the fundamental nature of the beast, but we have not.

I have taken it out of the context, but it applies very well to current situation in synthetic biology. My enormous frustration with this field comes from the fact that most of so-called synthetic biology is nothing else than genetic engineering with more systematic approach. The whole engineering meme has stuck in people’s head and many of them seem to care more about characterization of the system than about understanding how it works.

If we take a bearing from a car and from a bike, both will differ in shape and very likely one couldn’t be replaced by the other. However, their role and mechanism of work is the same, no matter in which machine we put it (this is BTW what I tried to say in my previous post on BioBricks, but judging from the comments I failed). Mainstream synthetic biology doesn’t seem to be interested in understanding how car and bike works – it’s interested in taking both of them apart as fast as possible, puting labels on the parts and pretend that now we understand how they work. And while this approach can be succesful to a certain extent in engineering, biology, especially synthetic biology, is not engineering, it’s rather a programming.

If we look at the particular component of conserved signalling pathway in two different organisms, its sequence most likely will differ. And for some pairs of organisms sequences of this component stop to be freely exchangable: they need to be mutated to fit particular chassis. Repository of information what works where is a great starting point, but it’s about the time to move further. It’s about the time to express biological systems as sets of functional roles and to build a compiler that transforms an abstract description of biological system into sequence understandable by the particular architecture (organism). This is what I think synthetic biology is all about. It’s designing by understanding.

Formalized language of biological processes sounds like a domain of systems biology, but a compiler certainly doesn’t, so such programming framework could use the best of both worlds. Can you imagine “Hello world” equivalent of a living cell? Or how would you debug program in such language? Sounds like lots of fun.

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Data from Bioinformatics Career Survey posted

Data analysis of Bioinformatics Career Survey

Data analysis of Bioinformatics Career Survey

Michael Barton did a great job of collecting and cleaning data for First Bioinformatics Career Survey. Raw results are available at Github and please read also details on the analysis and sharing results over at OWW page.

Michael encouraged to go wild with an analysis, so here’s my quick look at the data. On the image above you can see a scatter plot of salary vs years in the field (top), histogram of salaries (bottom left), histogram of planned years in the field and histogram of positions (bottom right). All plots are colored according to the positions.

There some obvious things in these graphs, such as correlations between position and salary or between years in the field and position (see also the video below). But what strikes me is the plot showing estimated number of years in the field. There are some local maxima at around 5, 20 and 30 years, but its very interesting to see that ca. half of the people see themselves in bioinformatics for another 25-30 years and longer, and there’s no clear correlation between positions of these people and these predictions (other than senior/PI-level staff doesn’t like an idea of working for another 30-40 years). The reason I find it interesting is that I have no idea how bioinformatics will look like in these 20-30 years (and that was the reason I’ve put conservative 5 years in this field). Do you know? Do you have an idea how bioinformatics will look like so much time ahead?

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Posted by on September 2, 2008 in bioinformatics, Career

 

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BadA head structure

Modularity is one of the most interesting features of the trimeric autotransporter adhesins, and probably one of the most frustrating. As I wrote before, domain annotation is quite difficult, especially that these proteins can have often few thousands residues in length.

BadA, the major adhesin of Bartonella henselae, is probably the best known large TAA out there. Its sequence served us as a unofficial benchmark for domain annotation tool. Its head consist of three domains, one resembling head of YadA and two others which we claimed are similar to Hia head domains. The claim at the moment of starting this project wasn’t supported very well – Evalues of HHpred alignments were around 1 (of course all less sensitive tools didn’t see anything), but we knew they must be similar (because that two,three conserved residues were at exactly where we expected). Crystal structure of these two domains from BadA couldn’t be solved directly, so we’ve attempted molecular replacement and that worked. On the picture above you can see three known head structures for TAAs, BadA (ours), Hia and YadA (full BadA head model in on the right) and arrangement of corresponding domains in all three proteins. The whole story and lots of pretty pictures (you must see EM figures) was published today yesterday in PLoS Pathogens (OA).

Today the story isn’t so exciting as it was at the beginning. Currently HHpred easily finds domains from Hia and BadA similar with high probability – it’s an advantage of bigger database size and more mediating sequences. But I’m still pretty happy about how it went – such projects build confidence in one’s analysis skills.

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Posted by on August 9, 2008 in bioinformatics

 

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