Solid-Support Directional (SSD) RNA-Seq as a Companion Method to CLIP-Seq.

CLIP-Seq (Deep Sequencing after in vivo Crosslinking and Immunoprecipitation, HITS-CLIP) has emerged as a key method for the study of RNA-binding proteins (RBPs), as it can scrutinize the RNAs bound by an RBP in vivo, with minimum manipulation of biological samples. CLIP-Seq is best used to reveal changes of the RNA cargo of an RBP and differences on binding patterns of the bound RNAs in living cells in different genetic backgrounds or after experimental treatment, rather than simply identifying RNA species. It is therefore crucial that a reference of the steady state levels of the RNAs present in the samples used for the CLIP-Seq experiment is included in the bioinformatic analysis.

Exploring the effects of genetic variation on gene regulation in cancer in the context of 3D genome structure.

Background: Numerous genome-wide association studies (GWAS) conducted to date revealed genetic variants associated with various diseases, including breast and prostate cancers. Despite the availability of these large-scale data, relatively few variants have been functionally characterized, mainly because the majority of single-nucleotide polymorphisms (SNPs) map to the non-coding regions of the human genome. The functional characterization of these non-coding variants and the identification of their target genes remain challenging.

BionoiNet: ligand-binding site classification with off-the-shelf deep neural network.

Motivation: Fast and accurate classification of ligand-binding sites in proteins with respect to the class of binding molecules is invaluable not only to the automatic functional annotation of large datasets of protein structures but also to projects in protein evolution, protein engineering and drug development. Deep learning techniques, which have already been successfully applied to address challenging problems across various fields, are inherently suitable to classify ligand-binding pockets. Our goal is to demonstrate that off-the-shelf deep learning models can be employed with minimum development effort to recognize nucleotide- and heme-binding sites with a comparable accuracy to highly specialized, voxel-based methods.

Effect of complement component 5 polymorphisms on mastitis resistance in Egyptian buffalo and cattle.

Mastitis is one of the costliest diseases affecting the world's dairy industry. The important contribution of complement Component 5 (C5) to phagocytosis, which plays a major role in the defence of the bovine mammary gland against infection, makes this component of innate immunity a potential contributor in defending udder against mastitis. The objectives of this study were to sequence and analyse the whole coding region of the C5 gene in Egyptian buffalo and cattle, to detect any nucleotide variations (polymorphisms) and to investigate their associations with milk somatic cell score (SCS) as an indicator of mastitis in dairy animals.

Complement component 3: characterization and association with mastitis resistance in Egyptian water buffalo and cattle.

Mastitis is an infectious disease of the mammary gland that leads to reduced milk production and change in milk composition. Complement component C3 plays a major role as a central molecule of the complement cascade involving in killing of microorganisms, either directly or in cooperation with phagocytic cells. C3 cDNA were isolated, from Egyptian buffalo and cattle, sequenced and characterized.