Dear Andrew,
If you want general statistical books on bootstrap, then any by the inventor of the procedure (Bradley Efron) would do. In your case, the application is rather simple
0). Fit your data using some model M, with estimated parameter vector R (either direct model parameters, like branch lengths, or functions of model parameters and the data, such as the Bayes factor for dN/dS at site 10).
1). The objective of the bootstrap is to estimate sampling properties of elements of R (e.g. mean, variance etc) the the function of random data. Ideally, one would prefer to repeat the same analysis on a large number of independent data samples
which were generated using the same process as that which produced the observed data. Since such independent replicates are not available, we do the next best thing and assume that the model we estimated was what actually generated the data, and then simulate under it. Efron showed that this actually works really well asymptotically.
In your case, for each simulated data, record the actual dN/dS for each site.
2). Run the same inference procedure as the one you used in step 0.
3). Tabulate the proportions of (p_F) false positives (sites with dN<dS which were inferred to be under selection) and of true positives (p_T) (sites with dN>dS which were inferred to be under selection) as a function of the significance level of your test (e.g. the Bayes Factor = 50, 100, 200 etc). If you plot p_T vs p_F for a fixed value of the Bayes factor, you will get what is known as an ROC curve.
4). Decide what Bayes factor gives you decent performance (low p_F and high p_T, decreasing the BF will increase both, increasing the BF will decrease both)
5). Now use that Bayes factor cutoff to reparse your original results and claim that the sites you found under selection are reasonably robust based on bootstrapped sampling properties of the estimator.
Anisimova and Yang had two papers on this in MBE in 2001/2002, and our Not So Different ... paper in MBE 2005 uses the same procedure.
HTH,
Sergei
P.S. Take a look at Multimedia File Viewing and Clickable Links are available for Registered Members only!! You need to
for a canned set of scripts we used for the Not So Different paper.