My tests to find out why the Neisseria Gibbs searches found so many (poorlymatched) motif sites indicate that searches using a short expected motif (e.g. the Neisseria 12bp motif) give a lot more sites than searches using a long expected motif (e.g. the Haemphilus 22bp one). This means that my original strategy of specifying an expected number of 1.5 X the observed number of 'perfect' cores has given misleading results.
At first I feared that I would have to do all the searches over, and analyze their new output in some much-more-laborious way. But I think I've come up with a simple new method that's also good science.
Each site the Gibbs search finds is assigned a score, reflecting how well it matches the motif pattern the search has found. Until now I've just used all the sites, regardless of their scores. But now I'm going to take the output from each run and sort the sites by their scores. Then I'll keep enough of the high-scoring sites to give me 1.5 X the number of perfect cores, and discard all the lower scoring sites. Most of the runs I've done gave at least this many sites, so I shouldn't need to redo many searches.
This strategy will produce a much more comparable set of analyses, so I'll be able to fairly compare the results for different genomes.
Before I do the sorting, I'll combine the results of a forward and a reverse search, so the results will reflect both strands of the genome. But I'll need to first make sure I'm using a pair of searches that both settled on the same 'forward' orientation and exactly the same position of the motif. This is mainly an issue for the Pasteurellaceae searches, where some searches have the core starting at position 1 of the motif and some have it starting at position 2 or 3. If I can't find suitable pairs I'll need to do more searches.
Macrocycles, flexibility and biological activity: A tortuous pairing
1 day ago in The Curious Wavefunction