Hey folks,
I've been working with relatively simple models of positive selection (the branch-site model of Zhang et. al 2005 and its non-coding equivalent) to contrast coding and non-coding selection as a follow-up to some earlier work by Ralph Haygood and Olivier Fedrigo. As pointed out elsewhere, I probably should phase out these models in favor of more recent ones, but they work well enough for detecting genome-wide patterns, esp. when many alignments are short.
Now, the questions:
1) Using HyPhy, it is straight-forward to get p-values from the model contrasts as well as site-frequency-weighted Omega values for a specific branch as a measure of the strength of selection on a branch. What is not clear to me is how to derive something more simple like dN. As a measure of selection, dN seems to me a problematic one, but many older papers to which I'd like to compare my results use it rather than a formal test for selection. For coding regions, I could count, but there is no countable dN for non-coding regions.
With output that looks like the following (for coding, alternate model), is there anything you might suggest?
Quote:REPL 4
log_L:-6160.4643423430889015
global omega0=0.7734953444635891;
omega0:<1;
global f_aux=4.523416980580921e-06;
f_aux:<1;
global f0=0;
f0:<1;
global omega2=3.6887710819289;
omega2:>1;
global kappa=0.2613381403107172;
hyphy_tree.dyak.syn_rate=0.413490713825213;
hyphy_tree.dsec.syn_rate=0.2240437703175933;
hyphy_tree.dmel.syn_rate=0.1560377184859193;
hyphy_tree.dsec.t=0.1195718292552295;
hyphy_tree.dyak.t=0.2779202180703129;
hyphy_tree.dmel.t=0.0801202493786467;
global omega_bgrnd:=((codon_class==0)+(codon_class==2))*omega0+(codon_class==1)+(codon_
class==3);
global omega_fgrnd:=(codon_class==0)*omega0+(codon_class==1)+((codon_class==2)+(codon_c
lass==3))*omega2;
hyphy_tree.dmel.nonsyn_rate:=omega_fgrnd*hyphy_tree.dmel.syn_rate;
hyphy_tree.dsec.nonsyn_rate:=omega_bgrnd*hyphy_tree.dsec.syn_rate;
hyphy_tree.dyak.nonsyn_rate:=omega_bgrnd*hyphy_tree.dyak.syn_rate;
Tree hyphy_tree=(dyak:0.0412697,dsec:0.0096207,dmel:0.0128978);
2) Is there a standard (widely accepted) HyPhy-approved approach for quantifying negative selection? It seems as though a non-branch-specific codeml-style estimate should work fine, but this is not my main field, and I'd appreciate suggestions (or .bf files!) pointing the right direction.
Thanks so much!
David