On the relation of gene essentiality to intron structure: a computational and deep learning approach

InDel mutations within the bovine PER2 gene are significantly associated with reproductive traits

Functioning as a key regulator of circadian rhythms, the PER2 gene exerts a substantial impact on the reproductive traits of animals. However, the effect of the PER2 gene on ovarian development remains unclear. In order to examine the relationship between bovine reproductive trait and the PER2 gene, a total of 901 ovarian samples were collected, categorized into different oestrus cycles (proestrus, oestrus, post-oestrus, anoestrous), and subjected to analysis for two potential insertion/deletions (InDels) in the PER2 gene. Through agarose gel electrophoresis and DNA sequencing, two polymorphic deletion mutations (P2-D5-bp, P3-D13-bp) were identified. Furthermore, a significant association between mature follicle diameter and P2-D5-bp was found (P < 0.05). Additionally, several significant correlations with ovarian length, width, height, and white body diameter were found for P3-D13-bp (P < 0.05). These findings suggested that the bovine PER2 gene plays an important role in above-mentioned reproductive traits, offering new avenues for improving cow fertility through marker-assisted selection (MAS).

Genes divided according to the relative position of the longest intron show increased representation in different KEGG pathways

Despite the fact that introns mean an energy and time burden for eukaryotic cells, they play an irreplaceable role in the diversification and regulation of protein production. As a common feature of eukaryotic genomes, it has been reported that in protein-coding genes, the longest intron is usually one of the first introns. The goal of our work was to find a possible difference in the biological function of genes that fulfill this common feature compared to genes that do not. Data on the lengths of all introns in genes were extracted from the genomes of six vertebrates (human, mouse, koala, chicken, zebrafish and fugu) and two other model organisms (nematode worm and arabidopsis). We showed that more than 40% of protein-coding genes have the relative position of the longest intron located in the second or third tertile of all introns. Genes divided according to the relative position of the longest intron were found to be significantly increased in different KEGG pathways. Genes with the longest intron in the first tertile predominate in a range of pathways for amino acid and lipid metabolism, various signaling, cell junctions or ABC transporters. Genes with the longest intron in the second or third tertile show increased representation in pathways associated with the formation and function of the spliceosome and ribosomes. In the two groups of genes defined in this way, we further demonstrated the difference in the length of the longest introns and the distribution of their absolute positions. We also pointed out other characteristics, namely the positive correlation between the length of the longest intron and the sum of the lengths of all other introns in the gene and the preservation of the exact same absolute and relative position of the longest intron between orthologous genes.

A Survey of Biological Data in a Big Data Perspective

The amount of available data is continuously growing. This phenomenon promotes a new concept, named big data. The highlight technologies related to big data are cloud computing (infrastructure) and Not Only SQL (NoSQL; data storage). In addition, for data analysis, machine learning algorithms such as decision trees, support vector machines, artificial neural networks, and clustering techniques present promising results. In a biological context, big data has many applications due to the large number of biological databases available. Some limitations of biological big data are related to the inherent features of these data, such as high degrees of complexity and heterogeneity, since biological systems provide information from an atomic level to interactions between organisms or their environment. Such characteristics make most bioinformatic-based applications difficult to build, configure, and maintain. Although the rise of big data is relatively recent, it has contributed to a better understanding of the underlying mechanisms of life. The main goal of this article is to provide a concise and reliable survey of the application of big data-related technologies in biology. As such, some fundamental concepts of information technology, including storage resources, analysis, and data sharing, are described along with their relation to biological data.