Biology subjects

Mabee, P. M.

Publications and source records attributed to Mabee, P. M..

3 recordsLinked to original sources

bioRxiv2021

Gene network module changes associated with the vertebrate fin to limb transition

Evolutionary phenotypic transitions, such as the fin to limb transition in vertebrate evolution, result from changes in associated genes and their interactions, often in response to changing environment. Identifying the associated changes in gene networks is vital to achieve a better understanding of these transitions. Previous experimental studies have been typically limited to manipulating a small number of genes. To expand the number of analyzed genes and hence, biological knowledge, we computationally isolated and compared the gene modules for paired fins (pectoral fin, pelvic fin) of fishes (zebrafish) to those of the paired limbs (forelimb, hindlimb) of mammals (mouse) using quality-enhanced gene networks from zebrafish and mouse. We ranked module genes according to their weighted-degrees and identified the highest-ranking hub genes, which were important for the module stability. Further, we identified genes conserved during the fin to limb transition and investigated the fates of zebrafish-specific and mouse-specific module genes in relation to their involvements in newly emerged or lost anatomical structures during the aquatic to terrestrial vertebrate transition. This paper presents the results of our investigations and demonstrates a general network-based computational workflow to study evolutionary phenotypic transitions involving diverse model organisms and anatomical entities.

bioinformatics↗

bioRxiv2020

Integration of anatomy ontology data with protein-protein interaction networks improves the candidate gene prediction accuracy for anatomical entities

BackgroundIdentification of genes responsible for anatomical entities is a major requirement in many fields including developmental biology, medicine, and agriculture. Current wet-lab techniques used for this purpose, such as gene knockout, are high in resource and time consumption. Protein-protein interaction (PPI) networks are frequently used to predict disease genes for humans and gene candidates for molecular functions, but they are rarely used to predict genes for anatomical entities. This is because PPI networks suffer from network quality issues, which can be a limitation for their usage in predicting candidate genes for anatomical entities. We developed an integrative framework to predict candidate genes for anatomical entities by combining existing experimental knowledge about gene-anatomy relationships with PPI networks using anatomy ontology annotations. We expected this integration to improve the quality of the PPI networks and be better optimized to predict candidate genes for anatomical entities. We used existing Uberon anatomy entity annotations for zebrafish and mouse genes to construct gene networks by calculating semantic similarity between the genes. These anatomy-based gene networks are semantic networks, as they are constructed based on the Uberon anatomy ontology annotations that are obtained from the experimental data in the literature. We integrated these anatomy-based gene networks with mouse and zebrafish PPI networks retrieved from the STRING database, and we compared the performance of their network-based candidate gene predictions. ResultsAccording to candidate gene prediction performance evaluations tested under four different semantic similarity calculation methods (Lin, Resnik, Schlicker, and Wang), the integrated networks showed better receiver operating characteristic (ROC) and precision-recall curve performances than PPI networks for both zebrafish and mouse. ConclusionIntegration of existing experimental knowledge about gene-anatomical entity relationships with PPI networks via anatomy ontology improves the network quality, which makes them better optimized for predicting candidate genes for anatomical entities.

bioinformatics↗

bioRxiv2018

Annotation of phenotypes using ontologies: a Gold Standard for the training and evaluation of natural language processing systems

Natural language descriptions of organismal phenotypes - a principal object of study in biology, are abundant in biological literature. Expressing these phenotypes as logical statements using formal ontologies would enable large-scale analysis on phenotypic information from diverse systems. However, considerable human effort is required to make the semantics of phenotype descriptions amenable to machine reasoning by (a) recognizing appropriate on-tological terms for entities in text and (b) stringing these terms into logical statements. Most existing Natural Language Processing tools stop at entity recognition, leaving a need for tools that can assist with both aspects of the task. The recently described Semantic CharaParser aims to meet this need. We describe the first expert-curated Gold Standard corpus for ontology-based annotation of phenotypes from the systematics literature. We use it to evaluate Semantic CharaParsers annotations and explore differences in performance between humans and machine. We use four annotation accuracy metrics that can account for both semantically identical and similar matches. We found that machine-human consistency was significantly lower than inter-curator (human-human) consistency. Surprisingly, allowing curators access to external information that was not available to Semantic CharaParser did not significantly increase the similarity of their annotations to the Gold Standard nor have a significant effect on inter-curator consistency. We found that the similarity of machine annotations to the Gold Standard increased after new ontology terms relevant to the input text had been added. Evaluation by the original authors of the character descriptions indicated that the Gold Standard annotations came closer to representing their intended meaning than did either the curator or machine annotations. These findings point toward ways to better design of software to augment human curators, and the Gold Standard corpus will allow training and assessment of new tools to improve phenotype annotation accuracy at scale.

evolutionary biology↗