The Phylogenetic Likelihood Library

[Abstract] We introduce the Phylogenetic Likelihood Library (PLL), a highly optimized application programming interface for developing likelihood-based phylogenetic inference and postanalysis software. The PLL implements appropriate data structures and functions that allow users to quickly implement common, error-prone, and labor-intensive tasks, such as likelihood calculations, model parameter as well as branch length optimization, and tree space exploration. The highly optimized and parallelized implementation of the phylogenetic likelihood function and a thorough documentation provide a framework for rapid development of scalable parallel phylogenetic software. By example of two likelihood-based phylogenetic codes we show that the PLL improves the sequential performance of current software by a factor of 2–10 while requiring only 1 month of programming time for integration. We show that, when numerical scaling for preventing floating point underflow is enabled, the double precision likelihood calculations in the PLL are up to 1.9 times faster than those in BEAGLE. On an empirical DNA dataset with 2000 taxa the AVX version of PLL is 4 times faster than BEAGLE (scaling enabled and required).DFG, German Research Foundation; STA/860-4. F.I.-C.DFG, German Research Foundation; STA/860-3DFG, German Research Foundation; STA/860-2. L.-T.N.University of Vienna; I059-NAustrian Science Fund; I760-B1

Want to track pandemic variants faster? Fix the bioinformatics bottleneck.

The prospect of reduced vaccine potency from fast-spreading SARS-CoV-2 variants has spurred a global rush to increase genomic surveillance for the coronavirus. This is crucial for quickly identifying and tracking emergent strains. It can also pin down how transmission occurs between individuals more definitively than typical contact tracing can. As this article went to press, laboratories around the world had sequenced more than 610,000 SARS-CoV-2 samples; that number could well exceed one million by the end of the pandemic. In theory, these genomes could help us to understand the spread of the virus through communities and across the globe, allowing us to stall infections. In practice, such analyses reveal much less than they might do