Comparing newly obtained and previously known nucleotide and amino-acid sequences underpins modern biological research. BLAST is a well-established tool for such comparisons but is challenging to use on new data sets. We combined a user-centric design philosophy with sustainable software development approaches to create Sequenceserver, a tool for running BLAST and visually inspecting BLAST results for biological interpretation. Sequenceserver uses simple algorithms to prevent potential analysis errors and provides flexible text-based and visual outputs to support researcher productivity. Our software can be rapidly installed for use by individuals or on shared servers.

Neuropeptides are evolutionarily ancient mediators of neuronal signalling in nervous systems. With recent advances in genomics/transcriptomics, an increasingly wide range of species has become accessible for molecular analysis. The deuterostomian invertebrates are of particular interest in this regard because they occupy an ‘intermediate' position in animal phylogeny, bridging the gap between the well-studied model protostomian invertebrates (e.g.Drosophila melanogaster,Caenorhabditis elegans) and the vertebrates. Here we have identified 40 neuropeptide precursors in the starfishAsterias rubens, a deuterostomian invertebrate from the phylum Echinodermata.Importantly, these include kisspeptin-type and melanin-concentrating hormone-type precursors, which are the first to be discovered in a non-chordate species. Starfish tachykinin-type, somatostatin-type, pigment-dispersing factor-type and corticotropin-releasing hormone-type precursors are the first to be discovered in the echinoderm/ambulacrarian clade of the animal kingdom. Other precursors identified include vasopressin/oxytocin-type, gonadotropin-releasing hormone-type, thyrotropin-releasing hormone-type, calcitonin-type, cholecystokinin/gastrin-type, orexin-type, luqin-type, pedal peptide/orcokinin-type, glycoprotein hormone-type, bursicon-type, relaxin-type and insulin-like growth factor-type precursors. This is the most comprehensive identification of neuropeptide precursor proteins in an echinoderm to date, yielding new insights into the evolution of neuropeptide signalling systems. Furthermore, these data provide a basis for experimental analysis of neuropeptide function in the unique context of the decentralized, pentaradial echinoderm bauplan.

Neuropeptides are a diverse class of intercellular signalling molecules that mediate neuronal regulation of many physiological and behavioural processes. Recent advances in genome/transcriptome sequencing are enabling identification of neuropeptide precursor proteins in species from a growing variety of animal taxa, providing new insights into the evolution of neuropeptide signalling. Here, detailed analysis of transcriptome sequence data from three brittle star species, Ophionotus victoriae , Amphiura filiformis and Ophiopsila aranea , has enabled the first comprehensive identification of neuropeptide precursors in the class Ophiuroidea of the phylum Echinodermata. Representatives of over 30 bilaterian neuropeptide precursor families were identified, some of which occur as paralogues. Furthermore, homologues of endothelin/CCHamide, eclosion hormone, neuropeptide-F/Y and nucleobinin/nesfatin were discovered here in a deuterostome/echinoderm for the first time. The majority of ophiuroid neuropeptide precursors contain a single copy of a neuropeptide, but several precursors comprise multiple copies of identical or non-identical, but structurally related, neuropeptides. Here, we performed an unprecedented investigation of the evolution of neuropeptide copy number over a period of approximately 270 Myr by analysing sequence data from over 50 ophiuroid species, with reference to a robust phylogeny. Our analysis indicates that the composition of neuropeptide ‘cocktails’ is functionally important, but with plasticity over long evolutionary time scales.

Summary: Phenopolis is an open-source web server providing an intuitive interface to genetic and phenotypic databases. It integrates analysis tools such as variant filtering and gene prioritization based on phenotype. The Phenopolis platform will accelerate clinical diagnosis, gene discovery and encourage wider adoption of the Human Phenotype Ontology in the study of rare genetic diseases. Availability and Implementation: A demo of the website is available at https://phenopolis.github.io. If you wish to install a local copy, source code and installation instruction are available at https://github.com/phenopolis. The software is implemented using Python, MongoDB, HTML/Javascript and various bash shell scripts. Contact:n.pontikos@ucl.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Background: Keratoconus is a disorder characterized by progressive thinning and distortion of the cornea. If detected at an early stage, corneal collagen cross-linking can prevent disease progression and further visual loss. Although advanced forms are easily detected, reliable identification of subclinical disease can be problematic. Several different machine learning algorithms have been used to improve the detection of subclinical keratoconus based on the analysis of multiple types of clinical measures, such as corneal imaging, aberrometry, or biomechanical measurements. Objective: The aim of this study is to survey and critically evaluate the literature on the algorithmic detection of subclinical keratoconus and equivalent definitions. Methods: For this systematic review, we performed a structured search of the following databases: MEDLINE, Embase, and Web of Science and Cochrane Library from January 1, 2010, to October 31, 2020. We included all full-text studies that have used algorithms for the detection of subclinical keratoconus and excluded studies that did not perform validation. This systematic review followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) recommendations. Results: We compared the measured parameters and the design of the machine learning algorithms reported in 26 papers that met the inclusion criteria. All salient information required for detailed comparison, including diagnostic criteria, demographic data, sample size, acquisition system, validation details, parameter inputs, machine learning algorithm, and key results are reported in this study. Conclusions: Machine learning has the potential to improve the detection of subclinical keratoconus or early keratoconus in routine ophthalmic practice. Currently, there is no consensus regarding the corneal parameters that should be included for assessment and the optimal design for the machine learning algorithm. We have identified avenues for further research to improve early detection and stratification of patients for early treatment to prevent disease progression.

Rare eye diseases such as inherited retinal diseases (IRDs) are challenging to diagnose genetically. IRDs are typically monogenic disorders and represent a leading cause of blindness in children and working-age adults worldwide. A growing number are now being targeted in clinical trials, with approved treatments increasingly available. However, access requires a genetic diagnosis to be established sufficiently early. Critically, the timely identification of a genetic cause remains challenging. We demonstrate that a deep learning algorithm, Eye2Gene, trained on a large multimodal imaging dataset of individuals with IRDs (n = 2,451) and externally validated on data provided by five different clinical centres, provides better-than-expert-level top-five accuracy of 83.9% for supporting genetic diagnosis for the 63 most common genetic causes. We demonstrate that Eye2Gene’s next-generation phenotyping can increase diagnostic yield by improving screening for IRDs, phenotype-driven variant prioritization and automatic similarity matching in phenotypic space to identify new genes. Eye2Gene is accessible online (app.eye2gene.com) for research purposes.