A milestone for Pentatomoidea: Grazia et al. 2008—What do we know and where can we go?

Pentatomoidea is the third largest superfamily in Heteroptera. The internal systematics and classification of this superfamily have an intricate history. The paper by Grazia et al. (2008) is a milestone to the phylogenetic hypothesis of Pentatomoidea. Subsequent papers explored the limited conclusions and unanswered questions left by Grazia et al. (2008). We proposed to look at the body of knowledge produced since Grazia et al. (2008) and to compile the molecular data for Pentatomoidea deposited in Genbank to achieve three aims: (i) to evaluate the advances on the phylogenetic relationships of the Pentatomoidea; (ii) to produce a phylogenetic hypothesis based on molecular data deposited in Genbank; and (iii) to highlight the shortcomings and strengths of the available data. We retrieved sequences of four molecular markers (COI, 16S, 18S, and 28S) for 167 terminal taxa, including 149 pentatomoids. A concatenated matrix was analyzed under maximum likelihood (ML) and parsimony (MP). Both methods supported the monophyly of Pentatomoidea, and poorly resolved internal relationships among the families. Acanthosomatidae, Dinidordae, Pentatomidae, Scutelleridae, Thaumastellidae, and Urostylididae were monophyletic (under ML and MP), and also Plataspidae and Thyreocoridae (under ML). Tessaratomidae and Cydnidae were non-monophyletic under both methods. Our results were compared to the phylogenetic hypotheses proposed for Pentatomoidea. The analysis of the data available on the GenBank allowed us to affirm that many problems mentioned previously remain unsolved, even though the sampling of terminals has increased. In summary, the efforts in the last two decades to better understand the relationships within the Pentatomoidea have been insufficient to propose robust advances in phylogenetic hypothesis for the group. We discuss topics we understand are paramount to upcoming developments:1) better taxon sample; 2) collection management; 3) increased markers; and 4) morphology and anatomical ontology.

Curation and annotation of planarian gene expression patterns with segmented reference morphologies

Motivation Morphological and genetic spatial data from functional experiments based on genetic, surgical and pharmacological perturbations are being produced at an extraordinary pace in developmental and regenerative biology. However, our ability to extract knowledge from these large datasets are hindered due to the lack of formalization methods and tools able to unambiguously describe, centralize and interpret them. Formalizing spatial phenotypes and gene expression patterns is especially challenging in organisms with highly variable morphologies such as planarian worms, which due to their extraordinary regenerative capability can experimentally result in phenotypes with almost any combination of body regions or parts. Results Here, we present a computational methodology and mathematical formalism to encode and curate the morphological outcomes and gene expression patterns in planaria. Worm morphologies are encoded with mathematical graphs based on anatomical ontology terms to automatically generate reference morphologies. Gene expression patterns are registered to these standard reference morphologies, which can then be annotated automatically with anatomical ontology terms by analyzing the spatial expression patterns and their textual descriptions. This methodology enables the curation and annotation of complex experimental morphologies together with their gene expression patterns in a centralized standardized dataset, paving the way for the extraction of knowledge and reverse-engineering of the much sought-after mechanistic models in planaria and other regenerative organisms. Availability and implementation We implemented this methodology in a user-friendly graphical software tool, PlanGexQ, freely available together with the data in the manuscript at https://lobolab.umbc.edu/plangexq. Supplementary information Supplementary data are available at Bioinformatics online.

A Semi-Automated Approach for Anatomical Ontology Mapping

Summary This paper presents a study in the domain of semi-automated and fully-automated ontology mapping. A process for inferring additional cross-ontology links within the domain of anatomical ontologies is presented and evaluated on pairs from three model organisms. The results of experiments performed with various external knowledge sources and scoring schemes are discussed.