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Gondret, F.

Publications and source records attributed to Gondret, F..

2 recordsLinked to original sources

Daily feeding rhythms may play a role in the genetic variability of feed efficiency in growing pigs

AO_SCPLOWBSTRACTC_SCPLOWImproving feed efficiency in pigs is essential for reducing production costs and environmental impacts. This study examines the influence of circadian feeding rhythms and genetic polymorphisms on feed efficiency variability using two pig lines divergently selected for Residual Feed Intake (RFI) over ten generations. Feeding behavior was monitored using automatic concentrate dispensers, recording 6,494,097 visits from 3,824 pigs to analyze meal frequency, duration, and diurnal patterns. LRFI pigs ate less frequently, with larger meals and longer durations, they exhibited two distinct feeding peaks: one around 8:00 AM and a higher one at 5:00 PM and they consumed more feed during the diurnal period and less at night. HRFI pigs showed a smoother, less rhythmic feeding behavior with increased nocturnal intake. The differences between the two RFI lines became more pronounced as the number of generations of selection increased, suggesting a genetic basis. Feeding behaviors, including intake during the two main diurnal peaks, were found to be heritable (heritability estimates: 0.30-0.40) and genetic correlations were observed between feed intake and RFI, especially for intake between the two peaks. Then, we investigated the evolution of allele frequencies of single nucleotide polymorphisms (SNPs) in DNA sequences surrounding 10 core clock genes (ARNTL, CLOCK, CRY1, CRY2, NPAS2, NR1D1, PER1, PER2, PER3, RORA) along generations of selection. SNPs with significant frequency changes were mapped to regulatory regions and transposable elements, especially in HRFI line, suggesting potential functional impacts on circadian regulation. These results underscore the role of feeding behavior and genetic variation in feed efficiency, offering insights for breeding programs aimed at improving metabolic efficiency and sustainability in pig production.

zoology↗

BioPAX-Explorer: a Python Object-Oriented framework for overcoming the complexity of querying biological networks

MotivationBiological Pathway Exchange (BioPAX) is a standard language, represented in OWL, that aims to enable the integration, exchange, visualization and analysis of biological pathway data. While public databanks increasingly provide datasets in BioPAX format, their use remains below potential. Users may encounter challenges in harnessing the data due to the BioPAX intricately detailed underlying model. Moreover, extracting data demands specific technical skills, posing a barrier for many potential users. ResultsTo address these obstacles, we developped BioPAX-Explorer. This toolis designed to facilitate the adoption and usage of BioPAX for extracting data or build algorithms and models, within the Python community. BioPAX-Explorer is a Python package that provides an object-oriented data model automatically generated from the BioPAX OWL specification. Moreover, it offers expressive query capabilities that shield users from BioPAX inner complexity. BioPAX-Explorer supports dataset building features, validation facilities and pre-build queries. It simplifies the extraction and processing of data from BioPAX sources by automatically generating SPARQL queries. BioPAX-Explorer also offers a user-friendly interface for Python users, allowing exhaustive exploration of large datasets through features such as memory-efficient query execution, entity-oriented queries without the need for SPARQL knowledge. It also allows to learn and reuse complex SPARQL queries for biological network analysis. Additionally, BioPAX-Explorer can accelerate the development of Python-based network analysis software, since it generates graph data structures from BioPAX queries and facilitates the creation of transparent, reproducible workflows based on the BioPAX OWL standard. Availability and implementationBioPAX-Explorer is freely available. We provide the source code, documentation, installation instructions and a Jupyter notebook with tutorial at https://fjrmoreews.github.io/biopax-explorer/

bioinformatics↗