Search

Abstract Background HH-suite is a widely used open source software suite for sensitive sequence similarity searches and protein fold recognition. It is based on pairwise alignment of profile Hidden Markov models (HMMs), which represent multiple sequence alignments of homologous proteins. Results We developed a single-instruction multiple-data (SIMD) vectorized implementation of the Viterbi algorithm for profile HMM alignment and introduced various other speed-ups. These accelerated the search methods HHsearch by a factor 4 and HHblits by a factor 2 over the previous version 2.0.16. HHblits3 is ∼10× faster than PSI-BLAST and ∼20× faster than HMMER3. Jobs to perform HHsearch and HHblits searches with many query profile HMMs can be parallelized over cores and over cluster servers using OpenMP and message passing interface (MPI). The free, open-source, GPLv3-licensed software is available at https://github.com/soedinglab/hh-suite. Conclusion The added functionalities and increased speed of HHsearch and HHblits should facilitate their use in large-scale protein structure and function prediction, e.g. in metagenomics and genomics projects.

Currently available tools for the analysis of mutational signatures do not make use of all possible genomic properties aside from mutation patterns. Here the authors present TensorSignatures, an efficient framework that jointly infers mutational signatures and their genomic determinants.

Recent research has shown that mutational signatures reflective of the history of a cancer can be detected in a cancer genome. Here, using whole genome sequencing of DNA repair deficient and proficient nematodes exposed to genotoxins, the authors show that these mutational signatures reflect both the initial DNA damage that was inflicted and the repair processes that ensue.