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Fibroblast growth factors (FGFs) are important polypeptide growth factors that play a critical role in many developmental processes, including differentiation, cell proliferation, and migration in mammals. This study employs in silico analyses to characterize the FGF gene family in buffalo, investigating their genome-wide identification, physicochemical properties, and evolutionary patterns. For this purpose, genomic and proteomic sequences of buffalo, cattle, goat, and sheep were retrieved from NCBI database. We identified a total of 22 FGF genes in buffalo. Physicochemical properties observed through ProtParam tool showed notable features of these proteins including in-vitro instability, thermostability, hydrophilicity, and basic nature. Phylogenetic analysis grouped 22 identified genes into nine sub-families based on evolutionary relationships. Additionally, analysis of gene structure, motif patterns, and conserved domains using TBtools revealed the remarkable conservation of this gene family across selected species throughout the course of evolution. Comparative amino acid analysis performed through ClustalW demonstrated significant conservation between buffalo and cattle FGF proteins. Mutational analysis showed three non-synonymous mutations at positions R103 > G, P7 > L, and E98 > Q in FGF4, FGF6, and FGF19, respectively in buffalo. Duplication events revealed only one segmental duplication (FGF10/FGF22) in buffalo and two in cattle (FGF10/FGF22 and FGF13/FGF13-like) with Ka/Ks values <1 indicating purifying selection pressure for these duplications. Comparison of protein structures of buffalo, goat, and sheep exhibited more similarities in respective structures. In conclusion, our study highlights the conservation of the FGF gene family in buffalo during evolution. Furthermore, the identified non-synonymous mutations may have implications for the selection of animals with better performance.Communicated by Ramaswamy H. Sarma.
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INTRODUCTION: A required step for presenting results of clinical studies is the declaration of participants demographic and baseline characteristics as claimed by the FDAAA 801. The common workflow to accomplish this task is to export the clinical data from the used electronic data capture system and import it into statistical software like SAS software or IBM SPSS. This software requires trained users, who have to implement the analysis individually for each item. These expenditures may become an obstacle for small studies. Objective of this work is to design, implement and evaluate an open source application, called ODM Data Analysis, for the semi-automatic analysis of clinical study data. METHODS: The system requires clinical data in the CDISC Operational Data Model format. After uploading the file, its syntax and data type conformity of the collected data is validated. The completeness of the study data is determined and basic statistics, including illustrative charts for each item, are generated. Datasets from four clinical studies have been used to evaluate the application's performance and functionality. RESULTS: The system is implemented as an open source web application (available at https://odmanalysis.uni-muenster.de) and also provided as Docker image which enables an easy distribution and installation on local systems. Study data is only stored in the application as long as the calculations are performed which is compliant with data protection endeavors. Analysis times are below half an hour, even for larger studies with over 6000 subjects. DISCUSSION: Medical experts have ensured the usefulness of this application to grant an overview of their collected study data for monitoring purposes and to generate descriptive statistics without further user interaction. The semi-automatic analysis has its limitations and cannot replace the complex analysis of statisticians, but it can be used as a starting point for their examination and reporting.