A key challenge in mobilising growing numbers of digitised biological specimens for scientific research is finding high-throughput methods to extract phenotypic measurements on these datasets. In this paper, we test a pose estimation approach based on Deep Learning capable of accurately placing point labels to identify key locations on specimen images. We then apply the approach to two distinct challenges that each requires identification of key features in a 2D image: (i) identifying body region-specific plumage colouration on avian specimens and (ii) measuring morphometric shape variation in Littorina snail shells. For the avian dataset, 95% of images are correctly labelled and colour measurements derived from these predicted points are highly correlated with human-based measurements. For the Littorina dataset, more than 95% of landmarks were accurately placed relative to expert-labelled landmarks and predicted landmarks reliably captured shape variation between two distinct shell ecotypes ('crab' vs 'wave'). Overall, our study shows that pose estimation based on Deep Learning can generate high-quality and high-throughput point-based measurements for digitised image-based biodiversity datasets and could mark a step change in the mobilisation of such data. We also provide general guidelines for using pose estimation methods on large-scale biological datasets.

Abstract Summary: EchoLOCATION is a database that provides a comprehensive analysis of the subcellular locations of Escherichia coli K-12 proteins. Locations are predicted by integrating data from a range of publicly available algorithms combined with extensive curation of experimental literature. The data can be searched in a variety of ways and can generate lists of subcellular proteomes for analysis. Experimental evidence supports the locations of over 500 envelope proteins (periplasm, inner and outer membrane). From analysis of disagreements between in silico predictions and experimental data, we provide an analysis of protein types where subcellular prediction algorithms are currently not accurate. Availability: http://www.ecoli-york.org/ Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

SUMMARY: BuchneraBASE is a bioinformatic research tool for the genome of the symbiotic bacterium Buchnera sp. APS that includes an improved genome annotation, comparative information about related insect symbiont genomes and a complete mapping of metabolic reactions to an Escherichia coli in silico model. The database is designed to accommodate genome-wide post-genomic datasets that are becoming available for this organism. AVAILABILITY: BuchneraBASE is available at http://www.buchnera.org/.

A database collating research on E. coli genes whose products have been characterised subsequent to in silico predictions from the completed genome sequence.