IRENE - Image, Reconstruct, Erase Noise, Etc.

This month’s Nature Audio file features a device, IRENE, designed to:

acquire digital maps of the surface of the media, without contact, and then apply image analysis methods to recover the audio data and reduce noise.

IRENE stands for Image, Reconstruct, Erase Noise, Etc. and was named after one of the first reconstructed audio recordings: “Goodnight Irene”, written by H. Ledbetter and J. Lomax, performed by the Weavers (1950). This earns IRENE the much prized pre-hoc classification.

Here more about IRENE here.

FUBAR - Fast Unconstrained Bayesian AppRoximation

From the authors that brought you BUSTED (Branch-site Unrestricted Statistical Test for Episodic Diversification), behold FUBAR: Fast Unconstrained Bayesian AppRoximation. (Doubly from the authors in this case, as the first author gave the tip-off.)

Despite it’s intranym status, FUBAR gets an extra geek hat-tip for being a homonym of “foo bar”. (Although in looking that up, I came across the original FUBAR acronym, which is hopefully not reflective of their method!)

Abstract

Model-based analyses of natural selection often categorize sites into a relatively small number of site classes. Forcing each site to belong to one of these classes places unrealistic constraints on the distribution of selection parameters, which can result in misleading inference due to model misspecification. We present an approximate hierarchical Bayesian method using a Markov chain Monte Carlo (MCMC) routine that ensures robustness against model misspecification by averaging over a large number of predefined site classes. This leaves the distribution of selection parameters essentially unconstrained, and also allows sites experiencing positive and purifying selection to be identified orders of magnitude faster than by existing methods. We demonstrate that popular random effects likelihood methods can produce misleading results when sites assigned to the same site class experience different levels of positive or purifying selection–an unavoidable scenario when using a small number of site classes. Our Fast Unconstrained Bayesian AppRoximation (FUBAR) is unaffected by this problem, while achieving higher power than existing unconstrained (fixed effects likelihood) methods. The speed advantage of FUBAR allows us to analyze larger data sets than other methods: We illustrate this on a large influenza hemagglutinin data set (3,142 sequences). FUBAR is available as a batch file within the latest HyPhy distribution (http://www.hyphy.org), as well as on the Datamonkey web server (http://www.datamonkey.org/).

• Murrell B, Moola S, Mabona A, Weighill T, Sheward D, Kosakovsky Pond SL & Scheffler K (2013). FUBAR: a fast, unconstrained bayesian approximation for inferring selection. Mol Biol Evol. 30(5):1196-205. PMID: 23420840

BUSTED - Branch-site Unrestricted Statistical Test for Episodic Diversification

This paper popped up in my PubCrawler feed today:

Murrell B et al. (2015). Gene-wide identification of episodic selection. Mol Biol Evol. 2015 Feb 19. pii: msv035.

We present BUSTED, a new approach to identifying gene-wide evidence of episodic positive selection, where the non-synonymous substitution rate is transiently greater than the synonymous rate. BUSTED can be used either on an entire phylogeny (without requiring an a priori hypothesis regarding which branches are under positive selection) or on a pre-specified subset of foreground lineages (if a suitable a priori hypothesis is available). Selection is modeled as varying stochastically over branches and sites, and we propose a computationally inexpensive evidence metric for identifying sites under episodic positive selection on any foreground branches. We compare BUSTED to existing models on simulated and empirical data. An implementation is available on www.datamonkey.org/busted, with a widget allowing the interactive specification of foreground branches.

From the Introduction, we find that BUSTED is indeed an orca-worthy contrived acronym: BUSTED - Branch-site Unrestricted Statistical Test for Episodic Diversification.