Searching for motifs

Well, I don't know why my first attempt at running a Gibbs motif search with the Gallibacterium genome returned errors. The errors it described were ones I hadn't made (as far as I could tell), so I resubmitted the runs and they were fine.

But the grid-computer system was slow to get around to my runs (probably those physicists and meteorologists hogging the system), so I poked around and rediscovered that the Gibbs motif searcher program now also runs on Macs. Luckily one of the post-docs had just come back from a two-week course that required intensive use of Unix, so she was able to dive in and sort out the permissions etc. for me. So now I can run the Gibbs searches on my newish MacBook Pro and on our other fast Mac. And I can also still run them on the grid system too.

Results: I can't find anything (i.e. Gibbs can't find anything) in the Gallibacterium genome that looks anything like a typical Pasteurellacean uptake sequence. I've done simple searches where I just asked it to look for an 8-mer motif, and searches wherre I gave it 'segmentation-mask' prior files telling it the spacing typical of a H. influenzae or A. pleuropneumoniae USS, but it still didn't find anything. Most of the time it can't find anything it even considers to be a motif at all. This may be because the USS motifs are too sparse for it to pick them up, or because Gallibacterium really doesn't have a USS at all.

But I'm trying one more thing - giving it a prior file that specifies not just the spacing but the actual position weight matrix to expect. If this doesn't find anything we may need to do some uptake-specificity experiments.

Can I remember how to run a Gibbs motif sampler analysis?

Our visiting grad student is working with Gallibacterium, a Pasteurellacean relative of Haemophilus. To help her optimize transformation we would like to find out about its uptake bias. As a first step, we'd like to find out whether it has repeats in its genome that resemble the known Pastuerellacean uptake signal sequences (USS) - fortunately a Gallibacterium genome sequence is available. I've done this analysis for all the other sequenced Pasteurellacean genomes, so I said I'd do this one too. Should be easy...

My first approach was to give the genome sequence to our Perl program that simulates USS, not because I want to do that, but because the program's first step is to count the numbers of full and partial USS matches in the starting sequence. The program was set up to do that for the H. influenzae USS (AAGTGCGGT), but when it didn't find many of these in the genome I modified it to find the other type of Pasteurellacean USS (ACAAGCGGT). It didn't find many of those either.

So, perhaps Gallibacterium has a previously unknown version of the USS. Or perhaps it has an unrelated USS. Or perhaps it doesn't have a USS at all, which would suggest that it has weak or no uptake bias. What was needed was analysis with the Gibbs motif sampler, which would look for any common repeat in the genome. OK, I did lots of those last summer, so I can do it again.

I remembered how to submit a sequence for analysis, but I didn't bother to carefully check what the different settings do bfore submitting the run. That was stupid, because 36 hours later I've received two emails fromt he system, telling me that my requested run failed. One says "ERROR:: Mismatched width ranges" and the other "ERROR:: Palandrome (sic) subscript overflow". Guess I'd better buckle down and sort it out.

CRP maniscript revisions submitted, on to Gallibacterium...

As usual, it took me about 3 tries to get the manuscript resubmitted with all the files in their correct forms. But it's done.

I'm going to try to get back to the bench next week, doing some competence-induction experiments with Gallibacterium, brought to the lab by our visiting grad student. Oh boy!

Manuscript almost ready to go back

Our manuscript about how the CRP proteins of E. coli and H. influenzae differ in their sequence specificity has been provisionally accepted, and the revised version is almost ready to send back to the journal. We weren't able to do the one experiment requested by the editor, but we make what we think is a pretty good argument about why it isn't needed.

The only remaining problem is that some of the figures look a bit weird, I think due to being shuffled between different formats. The dark grey shading in some of the bar-graph bars has turned into a dark grey check pattern. My former-postdoc-coauthor converted the figures into high-resolution PDFs so he could email them to me, but maybe he should instead post them to one of those file-sharing sites where I can download them. I know Google Groups works for this, but I think there are also sites dedicated to this.

The Response to Reviewers letter has been written and revised and polished, so once the figures are sorted out I think I can sit down and do the on-line submission.

Where are they now? Part 2

Two more lines of research that we're no longer working on:

3. When did eukaryote sexual reproduction begin? During the first 10 years that I was working on competence, I fully intended to switch to studying the origins of meiosis in eukaryotes. The plan, and the reasons I set it aside, are explained in this post from last summer. (Fortunately John Logsdon has taken up the torch.)

As the first steps in this project Joel Dacks, then a M.Sc. student in my lab, and I published two papers on the phylogeny of early-diverging eukaryotes. These results have been since confirmed by more detailed analyses, although the deep phylogeny of eukaryotes is still rather obscure.

4. Quorum sensing and/or diffusion sensing: Most bacteria secrete small more-or-less inert molecules into their micro-environments and monitor the external concentrations of these molecules.When this autoinducer-secretion was first discovered it was proposed to be a means of cell-cell communication, evolved to enable bacteria to monitor the cell density of the population they are living in and to respond with appropriate changes in gene expression. This "quorum sensing" explanation quickly became dogma, despite having serious theoretical/evolutionary problems. In retrospect, this acceptance was partly because there were no alternative explanations for the evolution of autoinducer secretion and sensing, and partly because the idea that bacteria are secretly talking to each other is very appealing.

In 2002 I published an opinion piece (in Trends in Microbiology) proposing a much simpler explanation, that the secreted molecules serve as inexpensive sensors of the diffusional properties of each cell's microenvironment, and thus allow cells to secrete expensive effector molecules (such as degradative enzymes) only when they and their products will not be lost by diffusion. This ‘diffusion sensing’ hypothesis was welcomed by evolutionary biologists but largely ignored by the many researchers actively investigating quorum sensing. My lab initially tried to develop experimental systems to demonstrate that isolated cells use secreted autoinducers for gene regulation, but gave up because of the technical problems of monitoring gene expression at the scale of single isolated cells.

However the paper now gets regular citations in reviews of quorum sensing, and several other research groups have produced evidence validating the importance of diffusion in autoinducer regulation. The latest is a study of Pseudomonas cells on leaves (Dulla and Lindow PNAS 2007), which found that diffusion and other physical factors in cells' microenvironments are major determinants of this regulation. They pointed out that my proposal "has received little attention despite the extensive study of QS in many species", and even quoted approvingly my sentences about what research is needed.

Where are they now?

In the course of updating my CV I've been checking what's become of hypotheses and projects we initiated but are no longer working on. The good news is that all of them are still active areas of research, and the ones I consider most important are getting increasing attention. Here's a quick overview of two of them.

1. Mutation rates in males vs females: In response to a paper reporting that point mutation rates are much higher in males than females (because sequences on X chromosomes evolve slower than sequences on Y chromosomes), I used a computer simulation model to show that the excess mutations in male lineages usually canceled out the benefits of sexual recombination for females (Redfield Nature 1994). This paper made a big media splash when it came out; Natalie Angier wrote it up for the New York Times, Jay Leno made a joke about it, and it even got a paragraph in Cosmopolitan! This was partly because the title was full of buzzwords 'sex', 'male', 'female', 'mutation', and partly because I wrote up a very clear useful press release.

It didn't make much of a scientific splash, and it hasn't had much impact on subsequent work on the evolution of sex, but the number of citations continues to increase. Many citations are from a European group of theoretical physicists who publish mainly in physics journals, but others are from evolutionary biologists. One 2007 review discusses the implications of my work, referring to it as 'a seminal study' (which I choose to interpret as not just a bad pun).

The hotspot paradox: Most meiotic crossing-over happens at chromosomal sites called recombination hotspots; the largest influence ont he activity of these sites is the DNA sequence at the site. While I was still a grad student I realized that, over evolutionary time, active hotspot sequences should disappear from genomes, being replaced first by leas-active and then by inactive sequences. This is because the mechanism by which hotspots cause recombination also causes more-active hotspot sequences to be physically replaced by less-active sequences. At that time the genetic evidence was strong but little was known about the molecular details. This creates a paradox, because hotspots have not disappeared (each chromosome has many of them).

About 10 years later I returned to this problem, using detailed computer simulations to model the evolution of hotspots. We first created a deterministic model of a single hotspot, and showed that none the forces opposing hotspot elimination (evolutionary benefits of recombination, benefits of correct chromosome segregation, direct fitness benefits of hotspots that also act as promoters, singly or in combination) were strong enough to maintain hotspots against their self-destructive activity. Several years later we created a better, stochastic, model that followed multiple hotspots on a chromosome - this confirmed and strengthened the previous conclusions.

The first paper (Boulton et al, PNAS 1997) was ignored by just about everyone, particularly the molecular biologists whose work might be expected to resolve the paradox. By the time the second paper was published (Pineda-Krch and Redfield 2005), evidence from human genetics had confirmed that the hotspot destruction originally studied in fungi also occurs in humans. Now, the increasing ability to examine individual crossover events at base-pair resolution has focused attention on the paradox, and most papers about hotspots in natural populations (including humans) mention it as a sign that the evolutionary history of recombination hotspots remains perplexing.

I'll write up a couple more of these projects tomorrow.

I decided to do a USS-model run with the values in the DNA-uptake matrix increased five-fold, thinking this meant that drive favouring uptake sequences would be much stronger.  This should have meant that, when beginning with a random sequence, there would initially be very few fragments that had sufficiently high scores to pass the probability filter and recombine with the genome.  So I was surprised when the frequency of recombination at the beginning of the run looked to be about the same as with the normal matrix.

But then I remembered that one nice feature of the latest version of the model is that it uses whatever values are in the DNA-uptake matrix to calculate an appropriate value for the recombination probability filter, always equal to the score earned by a single site that perfectly matches the matrix-specified preferences.  This is good under some circumstances, but in the present case it entirely defeats what I was trying to do.

So I guess it's time to tweak the model one more time, introducing a coefficient by which the probability filter can be multiplied.  Ideally we'd like to be able to sometimes have this coefficient change during the run, as this would allow us to simulate evolution of the uptake specificity itself.  I'm confident that I can introduce a constant coefficient (read in with the other run settings); I wonder if I can easily make it a variable...

Test post from iphone

The culture media problems we were having last fall are back. This
time the postdocs suspect the medium, so they've ordered a new stock
of the Bacto stuff.

I think I won't try to post any more from my phone until my iPhone
typing speed improves.

Sent from my iPhone (by email to Blogger, because the iPhone doesn't support MMS messaging).

How arbitrary is a position weight matrix?

Recently I've run some USS-evolution simulations that started with a 50 kb segment of the H. influenzae genome rather than with a random sequence of base pairs. I used the position weight matrix derived by Gibbs analysis of the whole genome, thinking that this would be a non-arbitrary measure of the over-representation of the uptake sequence pattern. I was hoping that this bias would be strong enough to maintain the uptake sequences already in the genome, but the genome score fell to about half (or less) of its original value after 50,000 or 100,000 cycles.

That started me wondering whether the position weight matrix should be treated as a fixed set of values, or just as telling us the relative value that should be applied to each base at each position. Said another way, could different settings of the Gibbs analysis have given a very different matrix? The answer is No, but only because the Gibbs analysis reports the weight matrix as the frequency of each base at each position, so the sum of the weights of the four bases at each position must add up to 1. So if we want to consider stronger or weaker matrices, there's no reason not to multiply all the values by any factor we want to test.

So I think the first goal is to see when strength of matrix is needed to maintain the uptake sequences in the genome at their natural frequencies. Then we can see whether the uptake sequences are still in the places they started at, or whether these have decayed and new ones arisin in new places.

sent from my new iPhone! Unfortunately I don't know how to get text entry in the big frame.

Some calculations and implications

In playing around with our Perl model of uptake sequence evolution, I've been surprised by how weak the effect of molecular drive seemed to be.  But I just did some back-of-the envelope approximations and realized that our settings may have been unreasonable.

We can use the relationship between the genomic mutation rate the model is assuming and known real mutation rates for a rough calibration.   Real mutation rates are on the order of 10^-9 per base pair per generation.  Using such a low rate per simulation cycle would make our simulations take forever, so we've been using much higher rates (usually 10^-4 or higher) and treating each simulation cycle as collapsing the effects of many generations.  But we didn't take the implications of this seriously.  

If we do, we have each simulation cycle representing 10^5 generations.  How much DNA uptake should have happened over 10^5 generations?  We could assume that real bacteria take up about one fragment of about 1 kb every generation.  The length is consistent with a recent estimate of the length of gene-conversion tracts in Neisseria, but the frequency is just a guess.  I don't know whether it's a conservative guess, but if bacteria are taking up DNA as food it's probably on the low side of reality.  

How much of this DNA recombines with the chromosome?  For now lets assume it all does.  This means that, in each cycle of our simulation, a simulated full-size genome would replace 10^5 kb of itself by recombination with incoming DNA.  Because a real genome is only about 2 x 10^3 kb, each part of it would be replaced about 100 times in each cycle.  We could cause this much recombination to happen in our model. but it wouldn't simulate reality because there wouldn't be any reproduction or DNA uptake between the multiple replacements of any one position.  

We can make things more realistic by (1) assuming that only about 10% of fragments taken up recombine with the genome, and (2) decreasing the genomic mutation rate by a factor of 10, so each cycle only represents 10^4 generations.  Now most of the genome gets replaced once in each cycle.

What about the fragment mutation rate?  We might assume that, on average, the fragments that a cell takes up come from cells that are separated from it by about 5 generations.  That is, the cell taking up the DNA and the cell the DNA came from had a common ancestor 5 generations back.  This means that 10 generations of mutation separate the genome from the DNA it is taking up, so the fragment-specific mutation rate should be 10 times higher than the genomic mutation rate.

So I have a simulation running that uses a genome mutation rate of 10^-5 and a fragment mutation rate of 10^-4.  The fragments are 1 kb long, and the cell considers 100 of these each cycle.  

One other modification of the program matters here.  We've now tweaked the program so that it can either start a run with a random-sequence 'genome' it's just created, or with a DNA sequence we give it, that can be taken from a real genome with real uptake sequences.  

So the run I'm trying now starts not with a random sequence but with a 50 kb segment of the H. influenzae genome.  This sequence already has lots of uptake sequences in it, so about half of the 1 kb fragments the model considers in each cycle pass the test and are recombined into the genome.  I'm hoping the new conditions will enable the genome to maintain these uptake sequences over the 50,000 cycle run I have going overnight.

The USS-Perl project becomes the USS-drive paper

I think I'm going to start using the label "USS-drive" for the manuscript that describes our "USS-Perl" computer simulation model.  That's because the focus of the manuscript will be on understanding the nature of the molecular drive process that we think is responsible for the accumulation of uptake sequences in genomes.

The plan is to combine the modeling analysis with our unpublished results on the bias of the uptake machinery and on the nature of the motif that has accumulated in the genome.

The broad outline will be as follows:

We want to understand how the bias of the uptake machinery can affect evolution of sequences in the genome, assuming that cells sometimes take up and recombine homologous sequences from closely related cells.  And we want to examine this in the context of what is seen in real cells - the real biases of the DNA uptake machineries and the real patterns of uptake-related sequences in genomes.

So we will begin by properly characterizing the genome sequences using the Gibbs Motif Sampler.  I've already done this analysis for all of the genomes that have uptake sequences.  And we've done Gibbs analysis on different subsets of the H. influenzae genome (coding, non-coding, potential terminators, different reading frames), and looked for evidence of covariation between different positions.

We will also collate the published uptake data for H. influenzae and N. meningitidis and N. gonorrhoeae, adding our unpublished H. influenzae data.  

And then we will present the model as a way to investigate the relationship between uptake bias and genome accumulation.  A key feature of the model is that it models uptake bias using a position-weight matrix that treats uptake sequences as motifs rather than as elements.  That is, it specifies the value of each base at each position of the motif.  This means that we can evaluate both uptake-bias data and the genome-frequency data as inputs into the model.  The uptake-bias data isn't really good enough for this, and I anticipate that the main focus will be using the genome frequency data to specify uptake bias in the model.

Because the model allows the matrix to be of any length, we can use it with the full-length H. influenzae motif (30 bp), not just the core.  And because the model lets us specify base composition, we can also use it for the Neisseria DUS. 

On to other manuscripts!

My bioinformatics colleague is headed back home, and our uptake sequence manuscript is waiting for the last bits of data, and for me to write a coherent and thoughtful Discussion. It's not nearly as far along as I'd hoped, but it's a much better piece of science than it was shaping up to be a week ago. One key step was switching the axes of a couple of the figures. Seems like a minor thing, but putting "% sequence identity" on the X-axis rather than the Y-axis transformed the data from meaningless to crystal clear.

The only big piece of analysis we still need is an examination of which of the H. influenzae genes that don't have BLAST hits in our three standard gamma-proteobacterial genomes also don't have hits in the more closely related A. pleuropneumoniae genome. Those that don't are stronger candidates for having entered the H. influenzae genome by horizontal gene transfer, and we predict they will also have relatively few uptake sequences.

So what's the problem with the Discussion? I don't seem to be able to see the big picture for the trees, and I'm hoping that setting the whole think aside for a couple of weeks will let the fog dissipate.

Lord knows I have lots of other stuff to work on. I wish I was going to do some benchwork, but I'm afraid for now it's two other manuscripts.

One of these is the educational research I've been referring to as "the homework project". It's been on the back burner while the graders we hired scored the quality of students' writing on various bits of coursework and the final exam. I think this is all finished now, though I don't even know where the data is. The teaching-research post-doc who was working closely with me on this project has started her new job back east, but she'll continue to be involved in the data analysis and writing. I'm also blessed with involvement of a new teaching-research post-doc, who'll be working with me here. (Both post-docs are part of our CWSEI program, not part of my own group.) The first step to getting the analysis and manuscript-writing under way is to email both of them and get the work organized. I'll do that this morning.

The other manuscript is the project I've been referring to as "USS-Perl". The basic computer simulation model now works well, our programming assistant has gone on to other things, and the post-doc involved with this project has done a lot of runs of the simulation to pin down some basic relationships between parameter settings and result. (I forget what these are, so I need to talk to her today about this.) I have a plan for the first manuscript, which I'll spell out in a separate post later today.

Divergence of genes in different COG functional categories?

The bioinformatics manuscript is coming along nicely (though of course a lot more slowly than I had hoped).

One of the things it does is show that the densities of uptake sequences in genes does not correlate well with the 18 'COG functional categories' that genes have been assigned to.  This is a significant result because a previous paper claimed a strong correlation between high uptake sequence density and assignment to a modified COG functional category containing 'genome maintenance genes'.  This result was considered to provide strong support for the hypothesis that uptake sequences exist to help bacteria get useful new genes, a hypothesis I think is dead wrong.

Our hypothesis is that the distribution of uptake sequences among different types of genes (with different functions) should only reflect how strongly these gene's sequences are constrained by their functions.  Our analysis would be quite a bit more impressive if we showed a positive result - that uptake sequence density in different COG functional categories correlates well with the degree of conservation of the genes in these groups.  My first idea was to ask my bioinformatics collaborator to do this analysis.  But I suspect it might be a lot of work, because she's only done any COG analysis with the A. pleuropneumoniae genome, but we would want analysis done with the H. influenzae and/or N. meningitidis COG functional categories genes, looking at the % identity of the three 'control' homologs we've used for our other analysis.

So I'm wondering whether someone might have already done a version of this analysis.  Not with H. influenzae or N. meningitidis, and not with the control homologs we've used, but any general estimate of levels of conservation of genes in the 18 COG functional categories.  I searched Google Scholar for papers about COG functional group divergence and found a good review of analyses one can do in the COG framework.  This got me hoping that maybe there was a web server that would let me do the analysis myself, but the paper didn't describe anything that would do the job.

But I looked deeper in Google Scholar's hits and found something that looks very promising.  It examines rates of sequence evolution across genes in the gamma-proteobacteria.  H. influenzae and the control genomes we used with it are all in the gamma-proteobacteria, and I think the paper has looked specifically at the relative rates of evolution of genes in different COG functional categories, so this might be exactly what I'm looking for.  The only problem is, the paper appeared in PLoS Genetics, and their site is down right now!  I'm trying to read the paper in Google's cached version, but the page is all greyed out and it can't show me the figures.  Guess I'll just have to be patient and hope the site is back up soon.

Base composition analysis supports gene transfer

Both our results and those recently published by another group suggest that uptake sequences are less common in genes that don't have homologs in related genomes.  I'm using 'related' loosely here, as our analysis searched for homologs in genes from other families of bacteria whereas the other group searched in members of the same genus.  But both results are best explained by the hypothesis that genes lacking uptake sequences are often those that a genome has recently acquired by lateral transfer from genomes that don't share that species' uptake sequence. 

To test this, we looked at gene properties that might give evidence of lateral transfer.  The simplest such property is base composition (%G+C), a property that differs widely between different bacterial groups and changes only slowly after a sequence has been transferred to a different genome.  My bioinformatics collaborator had already tabulated the %G+C of all the genes in the H. influenzae and N. meningitidis genomes, noting whether each gene had (1) uptake sequences and (2) homologs in the test genomes (see previous two posts for more explanation).  She'd then calculated the means and standard deviations of the base compositions of genes in each of the four classes.  

This analysis showed that, on average, the genes with no uptake sequences and no homologs also had anomalously low base compositions and larger standard deviations.  But I thought the effect might be clearer with a graphical presentation, so I plotted the actual %G+C for each gene as a function of which class it is in (presence/absence of uptake sequence and homologs).  I also noted sample sizes, as some classes have a lot more genes than others.  This gave a nice visualization of the effects, showing, not surprisingly, that genes with homologs in the control genomes have more homogeneous base compositions than those without homologs, and that the effect is quite a bit stronger for those genes that don't have uptake sequences.

Order of Results

We're still working out the order in which we present the different results in our draft manuscript.  The first parts of the Results are now the analysis of how uptake sequence accumulation has changed the frequencies of different tripeptides in the the proteome, and of why some reading frames are preferred.  

The next decision is how we present the results that use alignments of uptake-sequence-encoded proteins with homologs from three 'control'  genomes that do not have uptake sequences.  We had tentatively planned to first describe the finding that genes that don't have uptake sequences are less likely to have homologs in the control genomes (call this analysis A), then move to the genes that do have homologs in all three control genomes, first considering the overall degree of similarity (call this analysis B) and then considering the differences between the places where the uptake sequences are and the rest of the proteins (call this analysis C). 

But I was having a hard time seeing how to motivate the first analysis - it didn't easily connect with what came before. Now I think a better plan is to first describe analysis B, which shows that genes with more uptake sequences have lower similarity scores (actually identity scores - my collaborator and I disagree about the relative value of using similarity and identity scores). 

By describing analysis B first, we can more logically introduce the strategy of using alignments to homologs from a standard set of control genomes, before discussing the genes that lack homologs.  And the results of analysis B then motivate analysis A, looking at the genes that had to be omitted from analysis B because they didn't have all three homologs.

Organization of the Results

My bioinformatics collaborator and I are making good progress on both the writing of our manuscript and on improving the data that's going into it.  One of the latter came when I redid an analysis I first did about a year ago, this time being more meticulous about the input data and more thoughtful about how I used it.

The question the data address is why uptake sequences in coding regions are preferentially located in particular reading frames, specifically the effect of the codons they use on how efficiently the mRNA can be translated.  I had used the overall frequencies of different codons in the proteome (all the proteins the genome encodes) to estimate codon 'quality', by summing the frequencies of the three codons specified by the uptake sequence in a specific reading frame, and dividing this by the sum of the frequencies of the best (most-used) codons for the same amino acids.  This analysis showed no relationship between codon quality and the reading frame bias.  

In retrospect I should have multiplied the frequencies rather than adding them.  This both gives a more realistic approximation of how the codons' effects interact, and eliminates the differences caused by the differing frequencies of different amino acids in the proteome.  I should also have excluded those uptake sequence positions that allowed more than one codon. 

And I probably should have done the analysis for different species rather than only for H. influenzae (though, to be fair on myself, at the time I was working on a H. influenzae USS analysis). 

So now I've redone the analysis for the three species that exemplify the different uptake sequence types (H. influenzae, A. pleuropneumoniae and N. meningitidis), and find a substantial effect of codon quality.  That is, in each species, uptake sequences are mostly found in reading frames that allow use of the most common codons for those amino acids.  As codon frequency is thought to reflect the abundance of the corresponding tRNAs, this means that differences in the efficiency of translation explain some of the differences in reading frame use.

Collaborative writing

My bioinformatics collaborator is in town till next Friday, so we can work together to finish up our uptake sequence manuscript.  We have done (she has done) a lot of different analyses addressing various questions about how uptake sequence accumulation has affected protein evolution and vice versa, and I'm having a hard time keeping them all straight (partly because I haven't been thinking about this project for a while).  

The manuscript is mostly written already; but some parts of the Results are still up in the air because they were waiting for some final data.  It also needs polishing and some revising to incorporate results from a Neisseria uptake sequence paper that came out a few months ago.

To cope with all the data we've been occasionally creating 'Flow of Results' pages that summarize our results and conclusions, in the order we think the paper should present them. Yesterday we began our final push by going through the most recent version of the Flow of Results'.  I'm not sure we have everything in the perfect order, but we have an order that's good enough, with each result triggering a question that's addressed by the next result.  

Speedy simulations

Our programming assistant has finished his work for us.  At least, he's finished the period when he's employed by us, but now he's gotten the research bug and wants to stay involved to see how the program he's developed for us works out.

At present it works great - MUCH faster than before.  One of the reasons is that it no longer scores the genome in every cycle.  I'm doing a run now with a big genome (100 kb) and I can see it pause at every 100th cycle, as it takes the time to score the genome.  This is a sensible improvement, as the genome score isn't needed for the progress of the simulation - it just lets us the user monitor what's happening, and provides information used if the run needs to decide whether it has reached equilibrium.

I'm using this run to test whether the final accumulated USS motif precisely matches the biases specified in the recombination decisions (i.e. in the matrix used to score each fragment), by using a matrix where one position has substantially weaker bias than the others.  If the final motif matches the matrix bias, this position should show a weaker consensus in the accumulated uptake sequences.  

Of course to test this I may have to recall how to run the Gibbs Motif Sampler on the Westgrid server.  The alternative is to chop my 100 kb output genome into 10 pieces and run each separately on the Gibbs web server, which has a sequence limit of 10 kb.

Sex and Recombination in Minneaopolis?

The meeting on Sex and Recombination that was to be held in Iowa City next week has been canceled because of severe flooding! (And just when I was getting my talk pulled into shape too...)

I suspect I'm not the only one who planned to combine that meeting with the big Evolution meetings in Minneapolis, June 20-24. I'm flying into Minneapolis on June 15. If you'd like to get together with me or other sex-refugees, post a comment below and we'll see what we can organize.