Computerized screening of G-protein coupled receptors to identify and characterize olfactory receptors

In silico prediction of the full United Nations Globally Harmonized System eye irritation categories of liquid chemicals by IATA-like bottom-up approach of random forest method

As an alternative to in vivo Draize rabbit eye irritation test, this study aimed to construct an in silico model to predict the complete United Nations (UN) Globally Harmonized System (GHS) for classification and labeling of chemicals for eye irritation category [eye damage (Category 1), irritating to eye (Category 2) and nonirritating (No category)] of liquid chemicals with Integrated approaches to testing and assessment (IATA)-like two-stage random forest approach. Liquid chemicals (n = 219) with 34 physicochemical descriptors and quality in vivo data were collected with no missing values. Seven machine learning algorithms (Naive Bayes, Logistic Regression, First Large Margin, Neural Net, Random Forest (RF), Gradient Boosted Tree, and Support Vector Machine) were examined for the ternary categorization of eye irritation potential at a single run through 10-fold cross-validation. RF, which performed best, was further improved by applying the 'Bottom-up approach' concept of IATA, namely, separating No category first, and discriminating Category 1 from 2, thereafter. The best performing training dataset achieved an overall accuracy of 73% and the correct prediction for Category 1, 2, and No category was 80%, 50%, and 77%, respectively for the test dataset. This prediction model was further validated with an external dataset of 28 chemicals, for which an overall accuracy of 71% was achieved.

Genome-wide selective sweep analysis of the high-altitude adaptability of yaks by using the copy number variant

The domestic yak (Bos grunniens) from the Qinghai-Tibet Plateau is an important animal model in high-altitude adaptation studies. Here, we performed the genome-wide selective sweep analysis to identify the candidate copy number variation (CNV) for the high-altitude adaptation of yaks. A total of 531 autosomal CNVs were determined from 29 yak genome-wide resequencing data (15 high- and 14 low-altitude distributions) by using a CNV caller with a CNV identification interval > 5 kb, CNV silhouette score > 0.7, and minimum allele frequency > 0.05. Most high-frequency CNVs were located at the exonic (44.63%) and intergenic (46.52%) regions. In accordance with the results of the selective sweep analysis, 7 candidate CNVs were identified from the interaction of the top 20 CNVs with highest divergence from the F _ST and V _ST between the low (LA) and high (HA) altitudes. Five genes (i.e., GRIK4, IFNLR1, LOC102275985, GRHL3, and LOC102275713) were also annotated from the seven candidate CNVs and their upstream and downstream ranges at 300 kb. GRIK4, IFNLR1, and LOC102275985 were enriched in five known signal pathways, namely, glutamatergic synapse, JAK-STAT signaling pathway, cytokine-cytokine receptor interaction, neuroactive ligand-receptor interaction, and olfactory transduction. These pathways are involved in the environmental adaptability and various physiological functions of animals, especially the physiological regulation under a hypoxic environment. The results of this study advanced the understanding of CNV as an important genomic structure variant type that contributes to HA adaptation and helped further explain the molecular mechanisms underlying the altitude adaptability of yaks.