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View Code? Open in Web Editor NEWA repository of Greg's Ensembl perl stuff.
A repository of Greg's Ensembl perl stuff.
For a given ProteinTree alignment, use the Compara API to collect an equivalent alignment derived from the EPO pipeline. Things to do might include:
Basically, automate the creation of figures like the tree in the Paabo Foxp2 paper: http://www.nature.com/nature/journal/v418/n6900/fig_tab/nature01025_F2.html
People might be interested in primate-specific acceleration within certain families.
Possible tools:
I should move all code currently in the Bio::EnsEMBL::Compara::* folders into Bio::Greg::* instead. This will eventually help with future possible integration into EnsEMBL's codebase.
This can be calculated in two ways for each gene:
(Note: this can also be done by collating on domains, etc...)
This needs to be done in Java / Processing, but it would be cool.
Given a (hidden) true alignment and a (shown) inferred alignment. Plot the entire inferred alignment, and when the user hovers over a given residue, highlight (a) the current column of inferred homologous residues and (b) the (potentially scattered) set of truly homologous residues.
These values per gene tree could be useful for isochore / codon bias analysis...
See paper for more info: http://www.citeulike.org/user/gjuggler/article/6593838
In order to achieve genome-wide bootstrap simulations, we need to go one level deeper than random simulations with indel processes. Domain-loop structure seems to be the next logical level to get at.
On the Slrsim side, we should have a parameter which accepts a Perl arrayref that defines a series of "domains", where each domain has a separate set of simulation parameters. These are then sent to Indelible as different blocks for simulation in consecutive order.
In the SLRsim project, it would be nice to directly compare the inferred alignments with the true alignment. We could do this by either:
You know what to do... it could be part of the CollectEslrStats.pm module or as an R script on the exported site-wise data.
Data: http://www.ebi.ac.uk/intact/main.xhtml
Use IntAct data to get pairs of interacting proteins. See if there's a significant correlation between conserved evolutionary rates / pos-sel in interacting pairs.
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