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Formalin fixation of paraffin-embedded tissue samples is a well-established method for preserving tissue and is routinely used in clinical settings. Although formalin-fixed, paraffin-embedded (FFPE) tissues are deemed crucial for research and clinical applications, the fixation process results in molecular damage to nucleic acids, thus confounding their use in genome sequence analysis. Methods to improve genomic data quality from FFPE tissues have emerged, but there remains significant room for improvement. Here, we use whole-genome sequencing (WGS) data from matched Fresh Frozen (FF) and FFPE tissue samples to optimize a sensitive and precise FFPE single nucleotide variant (SNV) calling approach. We present methods to reduce the prevalence of false-positive SNVs by applying combinatorial techniques to five publicly available variant callers. We also introduce FFPolish, a novel variant classification method that efficiently classifies FFPE-specific false-positive variants. Our combinatorial and statistical techniques improve precision and F1 scores compared to the results of publicly available tools when tested individually.

An application ontology often reuses terms from other related, compatible ontologies. The extent of this interconnectedness is not readily apparent when browsing through larger textual presentations of term class hierarchies, be it Manchester text format OWL files or within an ontology editor like Protege. Users must either note ontology sources in term identifiers, or look at ontology import file term origins. Diagrammatically, this same information may be easier to perceive in 2 dimensional network or hierarchical graphs that visually code ontology term origins. However, humans, having stereoscopic vision and navigational acuity around colored and textured shapes, should benefit even more from a coherent 3-dimensional interactive visualization of ontology that takes advantage of perspective to offer both foreground focus on content and a stable background context. We present OntoTrek, a 3D ontology visualizer that enables ontology stakeholders-students, software developers, curation teams, and funders-to recognize the presence of imported terms and their domains, ultimately illustrating how projects can capture knowledge through a vocabulary of interwoven community-supported ontology resources.

Volumetric muscle loss (VML) is the loss of skeletal muscle that results in significant and persistent impairment of function. The unique characteristics of craniofacial muscle compared trunk and limb skeletal muscle, including differences in gene expression, satellite cell phenotype, and regenerative capacity, suggest that VML injuries may affect craniofacial muscle more severely. However, despite these notable differences, there are currently no animal models of craniofacial VML. In a previous sheep hindlimb VML study, we showed that our lab's tissue engineered skeletal muscle units (SMUs) were able to restore muscle force production to a level that was statistically indistinguishable from the uninjured contralateral muscle. Thus, the goals of this study were to: 1) develop a model of craniofacial VML in a large animal model and 2) to evaluate the efficacy of our SMUs in repairing a 30% VML in the ovine zygomaticus major muscle. Overall, there was no significant difference in functional recovery between the SMU-treated group and the unrepaired control. Despite the use of the same injury and repair model used in our previous study, results showed differences in pathophysiology between craniofacial and hindlimb VML. Specifically, the craniofacial model was affected by concomitant denervation and ischemia injuries that were not exhibited in the hindlimb model. While clinically realistic, the additional ischemia and denervation likely created an injury that was too severe for our SMUs to repair. This study highlights the importance of balancing the use of a clinically realistic model while also maintaining control over variables related to the severity of the injury. These variables include the volume of muscle removed, the location of the VML injury, and the geometry of the injury, as these affect both the muscle's ability to self-regenerate as well as the probability of success of the treatment.