Questions about BETS and temporal signal assessment in BEAST v2.6.7

[Sou Noguchi]

Dear BEAST users,

I am currently evaluating whether my RNA virus dataset contains sufficient temporal signal for divergence time estimation in BEAST v2.6.7.

As an initial screening step, I examined the dataset using TempEst. I am working under an uncorrelated lognormal relaxed clock (UCLD) model. To further assess temporal signal, I am considering a Bayesian evaluation of temporal signal (BETS) using path sampling. In addition, I performed a cluster randomization test using TipDatingBeast, following Duchêne et al. (2015), but did not detect significant temporal signal in that analysis.

I would be very grateful for your advice on the following two questions, both of which are important for interpreting my dataset:

1. When performing BETS using path sampling under a UCLD model, is it acceptable to run the analysis with a fixed or informed value for ucld.mean? Or, when assessing temporal signal itself, would it be more appropriate to leave ucld.mean less constrained so that the result is not overly influenced by the prior setting?
2. In my dataset, the cluster randomization test in TipDatingBeast did not support significant temporal signal. However, if BETS were to support the presence of temporal signal, how should this situation be interpreted? In such a case, would it be reasonable to conclude that the dataset still contains temporal signal, or should the negative result from the cluster randomization test be regarded as stronger evidence against it?

I would greatly appreciate any guidance on how to think about these two issues, as well as any relevant references or practical recommendations.

Best regards,
So Noguchi

[Lambodhar Damodaran]

Hi Sou,

I think it's important to know what your TempEst analysis showed. Are you willing to share what your root-to-tip regression looked like and a bit more descriptive stats about the dataset that you are looking at? I imagine TempEst is sufficient to determine this if your sampling is good, BETS is useful if have very sparse sampling.

Best

Lambo