nail: software for high-speed, high-sensitivity protein sequence annotation.

" Fast is fine, but accuracy is final. " -- Wyatt Earp. Background: The extreme diversity of newly sequenced organisms and considerable scale of modern sequence databases lead to a tension between competing needs for sensitivity and speed in sequence annotation, with multiple tools displacing the venerable BLAST software suite on one axis or another. Alignment based on profile hidden Markov models (pHMMs) has demonstrated state of art sensitivity, while recent algorithmic advances have resulted in hyper-fast annotation tools with sensitivity close to that of BLAST. Results: Here, we introduce a new tool that bridges the gap between advances in these two directions, reaching speeds comparable to fast annotation methods such as MMseqs2 while retaining most of the sensitivity offered by pHMMs. The tool, called nail, implements a heuristic approximation of the pHMM Forward/Backward (FB) algorithm by identifying a sparse subset of the cells in the FB dynamic programming matrix that contains most of the probability mass. The method produces an accurate approximation of pHMM scores and E-values with high speed and small memory requirements. On a protein benchmark, nail recovers the majority of recall difference between MMseqs2 and HMMER, with run time ~26x faster than HMMER3 (only ~2.4x slower than MMseqs2's sensitive variant). nail is released under the open BSD-3-clause license and is available for download at https://github.com/TravisWheelerLab/nail.

A topology-marginal composite likelihood via a generalized phylogenetic pruning algorithm.

Bayesian phylogenetics is a computationally challenging inferential problem. Classical methods are based on random-walk Markov chain Monte Carlo (MCMC), where random proposals are made on the tree parameter and the continuous parameters simultaneously. Variational phylogenetics is a promising alternative to MCMC, in which one fits an approximating distribution to the unnormalized phylogenetic posterior. Previous work fit this variational approximation using stochastic gradient descent, which is the canonical way of fitting general variational approximations. However, phylogenetic trees are special structures, giving opportunities for efficient computation. In this paper we describe a new algorithm that directly generalizes the Felsenstein pruning algorithm (a.k.a. sum-product algorithm) to compute a composite-like likelihood by marginalizing out ancestral states and subtrees simultaneously. We show the utility of this algorithm by rapidly making point estimates for branch lengths of a multi-tree phylogenetic model. These estimates accord with a long MCMC run and with estimates obtained using a variational method, but are much faster to obtain. Thus, although generalized pruning does not lead to a variational algorithm as such, we believe that it will form a useful starting point for variational inference.