CarcinoHPVPred: An ensemble of machine learning models for HPV carcinogenicity prediction using genomic data

Human papillomavirus (HPV) infections often show no symptoms but sometimes lead to either warts or carcinoma based on the HPV genotype. The relationship between HPV infections and cervical cancer have been well studied in the past two decades. However, distinguishing carcinogenic HPV variants from non-carcinogenic ones remains a major challenge in clinical genetic testing of HPV-induced cancer samples. All of the published HPV carcinogenicity prediction methods are neither publically available nor tested with two-thirds of available HPV variants. The nucleotide composition-based studies are the simplest and most precise methods of characterizing new genomes. Hence, there is a need for machine learning models which can predict the carcinogenic nature of newly discovered HPV based on their genomic composition. We developed a standalone and web tool, CarcinoHPVPred (h t t p :// test5.bicpu.edu.in/CarcinoHPVPred.php), for predicting the phenotype of HPV with a range of a high accuracy between 94% - 100%. This tool consists of machine learning models build upon genomic features of two genes namely E2 and E6. Overall, the accurate and early prediction of carcinogenic nature of HPV can be performed with this only available tool of its kind till date.

Association of the Recessive Allele vrn-D1 With Winter Frost Tolerance in Bread Wheat

Winter frost has been considered the primary limiting factor in wheat production. Shimai 12 is an elite wheat cultivar grown in central and southern Hebei province of China, but sensitive to winter frost. In this study, the winter frost tolerant cultivar Lunxuan 103 was bred by introducing the recessive allele vrn-D1 from winter wheat Shijiazhuang 8 (frost tolerance) into Shimai 12 using marker-assisted selection (MAS). Different from Shimai 12, Lunxuan 103 exhibited a winter growth habit with strong winter frost tolerance. In the Shimai 12 × Shijiazhuang 8 population, the winter progenies (vrn-D1vrn-D1) had significantly lower winter-killed seedling/tiller rates than spring progenies (Vrn-D1aVrn-D1a), and the consistent result was observed in an association population. Winter frost damage caused a significant decrease in grain yield and spike number/m2 in Shimai 12, but not in Lunxuan 103 and Shijiazhuang 8. The time-course expression analysis showed that the transcript accumulation levels of the cold-responsive genes were higher in Lunxuan 103 and Shijiazhuang 8 than in Shimai 12. Lunxuan 103 possessed the same alleles as its parents in the loci for plant height, vernalization, and photoperiod, except for the vernalization gene Vrn-D1. An analysis of genomic composition showed that the two parents contributed similar proportions of genetic compositions to Lunxuan 103. This study provides an example of the improvement of winter frost tolerance by introducing the recessive vernalization gene in bread wheat.

Pathogenicity and virulence of hepatitis A virus

Hepatitis A is an acute infection of the liver, which is mostly asymptomatic in children and increases the severity with age. Although in most patients the infection resolves completely, in a few of them it may follow a prolonged or relapsed course or even a fulminant form. The reason for these different outcomes is unknown, but it is generally accepted that host factors such as the immunological status, age and the occurrence of underlaying hepatic diseases are the main determinants of the severity. However, it cannot be ruled out that some virus traits may also contribute to the severe clinical outcomes. In this review, we will analyze which genetic determinants of the virus may determine virulence, in the context of a paradigmatic virus in terms of its genomic, molecular, replicative, and evolutionary features.

A Causality Perspective of Genomic Breed Composition for Composite Animals

Genomic breed composition (GBC) of an individual animal refers to the partition of its genome according to the inheritance from its ancestors or ancestral breeds. For crossbred or composite animals, knowing their GBC is useful to estimate heterosis, to characterize their actual inheritance from foundation breeds, and to make management decisions for crossbreeding programs. Various statistical approaches have been proposed to estimate GBC in animals, but the interpretations of estimates have varied with these methods. In the present study, we proposed a causality interpretation of GBC based on path analysis. We applied this method to estimating GBC in two composite breeds of beef cattle, namely Brangus and Beefmaster. Three SNP panels were used to estimate GBC: a 10K SNP panel consisting of 10,226 common SNPs across three GeneSeek Genomic Profiler (GGP) bovine SNP arrays (GGP 30K, GGP 40K, and GGP 50K), and two subsets (1K and 5K) of uniformly distributed SNPs. The path analysis decomposed the relationships between the ancestors and the composite animals into direct and indirect path effects, and GBC was measured by the relative ratio of the coefficients of direct (D-GBC) and combined (C-GBC) effects from each ancestral breed to the progeny, respectively. Estimated GBC varied only slightly between different genotyping platforms and between the three SNP panels. In the Brangus cattle, because the two ancestral breeds had a very distant relationship, the estimated D-GBC and C-GBC were comparable to each other in the path analysis, and they corresponded roughly to the estimated GBC from the linear regression and the admixture model. In the Beefmaster, however, the strong relationship in allelic frequencies between Hereford and Shorthorn imposed a challenge for the linear regression and the admixture model to estimated GBC reliably. Instead, D-GBC by the path analysis included only direct ancestral effects, and it was robust to bias due to high genomic correlations between reference (ancestral) breeds.