New hypotheses of cell type diversity and novelty from orthology-driven comparative single cell and nuclei transcriptomics in echinoderms.

Cell types are the building blocks of metazoan biodiversity and offer a powerful perspective for inferring evolutionary phenomena. With the development of single-cell transcriptomic techniques, new definitions of cell types are emerging. This allows a conceptual reassessment of traditional definitions of novel cell types and their evolution. Research in echinoderms, particularly sea star and sea urchin embryos has contributed significantly to understanding the evolution of novel cell types, through the examination of skeletogenic mesenchyme and pigment cells, which are found in sea urchin larvae, but not sea star larvae. This paper outlines the development of a gene expression atlas for the bat sea star, Patiria miniata, using single nuclear RNA sequencing (snRNA-seq) of embryonic stages. The atlas revealed 23 cell clusters covering all expected cell types from the endoderm, mesoderm, and ectoderm germ layers. In particular, four distinct neural clusters, an immune-like cluster, and distinct right and left coelom clusters were revealed as distinct cell states. A comparison with Strongylocentrotus purpuratus embryo single-cell transcriptomes was performed using 1:1 orthologs to anchor and then compare gene expression patterns. The equivalent of S. purpuratus piwil3+ Cells were not detected in P. miniata, while the Left Coelom of P. miniata has no equivalent cell cluster in S. purpuratus. These differences may reflect changes in developmental timing between these species. While considered novel morphologically, the Pigment Cells of S. purpuratus map to clusters containing Immune-like Mesenchyme and Neural cells of P. miniata, while the Skeletogenic Mesenchyme of S. purpuratus are revealed as orthologous to the Right Coelom cluster of P. miniata. These results suggest a new interpretation of the evolution of these well-studied cell types and a reflection on the definition of novel cell types.

Echinobase: leveraging an extant model organism database to build a knowledgebase supporting research on the genomics and biology of echinoderms.

Echinobase (www.echinobase.org) is a third generation web resource supporting genomic research on echinoderms. The new version was built by cloning the mature Xenopus model organism knowledgebase, Xenbase, refactoring data ingestion pipelines and modifying the user interface to adapt to multispecies echinoderm content. This approach leveraged over 15 years of previous database and web application development to generate a new fully featured informatics resource in a single year. In addition to the software stack, Echinobase uses the private cloud and physical hosts that support Xenbase. Echinobase currently supports six echinoderm species, focused on those used for genomics, developmental biology and gene regulatory network analyses. Over 38 000 gene pages, 18 000 publications, new improved genome assemblies, JBrowse genome browser and BLAST + services are available and supported by the development of a new echinoderm anatomical ontology, uniformly applied formal gene nomenclature, and consistent orthology predictions. A novel feature of Echinobase is integrating support for multiple, disparate species. New genomes from the diverse echinoderm phylum will be added and supported as data becomes available. The common code development design of the integrated knowledgebases ensures parallel improvements as each resource evolves. This approach is widely applicable for developing new model organism informatics resources.

Classifying domain-specific text documents containing ambiguous keywords.

A keyword-based search of comprehensive databases such as PubMed may return irrelevant papers, especially if the keywords are used in multiple fields of study. In such cases, domain experts (curators) need to verify the results and remove the irrelevant articles. Automating this filtering process will save time, but it has to be done well enough to ensure few relevant papers are rejected and few irrelevant papers are accepted. A good solution would be fast, work with the limited amount of data freely available (full paper body may be missing), handle ambiguous keywords and be as domain-neutral as possible. In this paper, we evaluate a number of classification algorithms for identifying a domain-specific set of papers about echinoderm species and show that the resulting tool satisfies most of the abovementioned requirements. Echinoderms consist of a number of very different organisms, including brittle stars, sea stars (starfish), sea urchins and sea cucumbers. While their taxonomic identifiers are specific, the common names are used in many other contexts, creating ambiguity and making a keyword search prone to error. We try classifiers using Linear, Naïve Bayes, Nearest Neighbor, Tree, SVM, Bagging, AdaBoost and Neural Network learning models and compare their performance. We show how effective the resulting classifiers are in filtering irrelevant articles returned from PubMed. The methodology used is more dependent on the good selection of training data and is a practical solution that can be applied to other fields of study facing similar challenges. Database URL: The code and date reported in this paper are freely available at http://xenbaseturbofrog.org/pub/Text-Topic-Classifier/.

Integration of 1:1 orthology maps and updated datasets into Echinobase.

Echinobase (https://echinobase.org) is a central online platform that generates, manages and hosts genomic data relevant to echinoderm research. While the resource primarily serves the echinoderm research community, the recent release of an excellent quality genome for the frequently studied purple sea urchin (Strongylocentrotus purpuratus genome, v5.0) has provided an opportunity to adapt to the needs of a broader research community across other model systems. To this end, establishing pipelines to identify orthologous genes between echinoderms and other species has become a priority in many contexts including nomenclature, linking to data in other model organisms, and in internal functionality where data gathered in one hosted species can be associated with genes in other hosted echinoderms. This paper describes the orthology pipelines currently employed by Echinobase and how orthology data are processed to yield 1:1 ortholog mappings between a variety of echinoderms and other model taxa. We also describe functions of interest that have recently been included on the resource, including an updated developmental time course for S.purpuratus, and additional tracks for genome browsing. These data enhancements will increase the accessibility of the resource to non-echinoderm researchers and simultaneously expand the data quality and quantity available to core Echinobase users. Database URL: https://echinobase.org.