Mammalian genomic regulatory regions predicted by utilizing human genomics, transcriptomics, and epigenetics data

Area of Science:

• Genomics and Bioinformatics
• Comparative Genomics
• Regulatory Element Prediction

Background:

• Mammalian genome sequences are abundant, but functional annotation of regulatory regions remains limited.
• Understanding regulatory DNA is crucial for studying genetic variation in evolution, domestication, and animal production.
• Predicting active regulatory elements is essential for functional genomics and trait association studies.

Purpose of the Study:

• To develop a computational method for predicting active regulatory DNA sequences (promoters, enhancers, transcription factor binding sites) in production animals.
• To extend the method's applicability to diverse mammalian species.
• To demonstrate the utility of predicted regulatory regions for prioritizing genetic variants and identifying genome editing targets.

Main Methods:

• Utilized human regulatory features to identify conserved homologous regions in other mammalian genomes.
• Employed sequence conservation and genome organization analysis to predict potential regulatory elements.
• Developed a machine learning-based filtering strategy using minimal species-specific data to identify active regulatory regions.

Main Results:

• Successfully predicted regulatory DNA sequences in cattle and pigs, demonstrating broad mammalian applicability.
• The method achieves a balance of sensitivity and accuracy for unbiased regulatory region prediction.
• Demonstrated the practical application of predicted regulatory data in cattle for trait variant prioritization and genome editing target identification.

Conclusions:

• The developed computational method effectively predicts active regulatory regions across mammalian species.
• This tool facilitates functional genomics studies, variant interpretation, and genetic improvement in production animals.
• The approach provides a valuable resource for understanding mammalian genome regulation and its impact on traits.