SUMMARY: Sensory receptor gene families have undergone extensive expansion and loss across vertebrate evolution, leading to significant variation in receptor counts between species. However, due to their species-specific nature, conventional reference-based annotation tools often underestimate the true number of sensory receptors in a given species. While there has been an exponential increase in the taxonomic diversity of publicly available genome assemblies in recent years, only ∼30% of vertebrate species on the NCBI database are currently annotated. To overcome these limitations, we developed 'Sensommatic', an automated and accessible sensory receptor annotation pipeline. Sensommatic implements BLAST and AUGUSTUS to mine and predict sensory receptor genes from whole genome assemblies, adopting a one-to-many gene mapping approach. While designed for vertebrates, Sensommatic can be extended to run on non-vertebrate species by generating customized reference files, making it a scalable and generalizable tool. AVAILABILITY AND IMPLEMENTATION: Source code and associated files are available at: https://github.com/GMHughes/Sensommatic.

SUMMARY: A number of limiting factors mean that traditional genome annotation tools either fail or perform sub-optimally when trying to detect coding sequences in poor quality genome assemblies/genome reports. This means that potentially useful data is accessible only to those with specific skills and expertise in assembly and annotation. We present an Assembled-Genome mIning pipeLinE (AGILE) written in Perl that combines bioinformatics tools with a number of steps to overcome the limitations imposed by such assemblies when applied to highly fragmented genomes. Our methodology uses user-specified query genes from a closely related species to mine and annotate coding sequences that would traditionally be missed by standard annotation packages. Despite a focus on mammalian genomes, the generalized implementation means that it may be applied to any genome assembly, providing a means for non-specialists to gather gene sequences for downstream analyses. AVAILABILITY AND IMPLEMENTATION: Source code and associated files are available at: https://github.com/batlabucd/GenomeMining and https://bitbucket.org/BatlabUCD/genomemining/src. Singularity and Virtual Box images available at https://figshare.com/s/a0004bf93dc43484b0c0. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Guide-trees are used as part of an essential heuristic to enable the calculation of multiple sequence alignments. They have been the focus of much method development but there has been little effort at determining systematically, which guide-trees, if any, give the best alignments. Some guide-tree construction schemes are based on pair-wise distances amongst unaligned sequences. Others try to emulate an underlying evolutionary tree and involve various iteration methods. RESULTS: We explore all possible guide-trees for a set of protein alignments of up to eight sequences. We find that pairwise distance based default guide-trees sometimes outperform evolutionary guide-trees, as measured by structure derived reference alignments. However, default guide-trees fall way short of the optimum attainable scores. On average chained guide-trees perform better than balanced ones but are not better than default guide-trees for small alignments. CONCLUSIONS: Alignment methods that use Consistency or hidden Markov models to make alignments are less susceptible to sub-optimal guide-trees than simpler methods, that basically use conventional sequence alignment between profiles. The latter appear to be affected positively by evolutionary based guide-trees for difficult alignments and negatively for easy alignments. One phylogeny aware alignment program can strongly discriminate between good and bad guide-trees. The results for randomly chained guide-trees improve with the number of sequences.