Genomic selection in pig breeding: comparative analysis of machine learning algorithms.

Background: The effectiveness of genomic prediction (GP) significantly influences breeding progress, and employing SNP markers to predict phenotypic values is a pivotal aspect of pig breeding. Machine learning (ML) methods are usually used to predict phenotypic values since their advantages in processing high dimensional data. While, the existing researches have not indicated which ML methods are suitable for most pig genomic prediction.

Integrated metabolomics and transcriptomic analysis of the flavonoid regulatory networks in Sorghum bicolor seeds.

Background: The objective of this study was to reveal the flavonoid biosynthesis pathway in white (Z6), red (Z27) and black (HC4) seeds of the sweet sorghum (Sorghum bicolor) using metabolomics and transcriptomics, to identify different flavonoid metabolites, and to analyze the differentially expressed genes involved in flavonoid biosynthesis.

Results: We analyzed the metabolomics and transcriptomics data of sweet sorghum seeds. Six hundred and fifty-one metabolites including 171 flavonoids were identified in three samples.

Reactive oxygen species, antioxidant enzyme activity, and gene expression patterns in a pair of nearly isogenic lines of nicosulfuron-exposed waxy maize (Zea mays L.).

Nicosulfuron is a post-emergence herbicide used for weed control in maize fields (Zea mays L.). Here, the pair of nearly isogenic inbred lines SN509-R (nicosulfuron resistant) and SN509-S (nicosulfuron sensitive) was used to study the effect of nicosulfuron on growth, oxidative stress, and the activity and gene expression of antioxidant enzymes in waxy maize seedlings.