DeNovoCNN: a deep learning approach to de novo variant calling in next generation sequencing data.

De novo mutations (DNMs) are an important cause of genetic disorders. The accurate identification of DNMs from sequencing data is therefore fundamental to rare disease research and diagnostics. Unfortunately, identifying reliable DNMs remains a major challenge due to sequence errors, uneven coverage, and mapping artifacts.

De novo mutations in children born after medical assisted reproduction.

Study Question: Are there more de novo mutations (DNMs) present in the genomes of children born through medical assisted reproduction (MAR) compared to spontaneously conceived children?

Summary Answer: In this pilot study, no statistically significant difference was observed in the number of DNMs observed in the genomes of MAR children versus spontaneously conceived children.

What Is Known Already: DNMs are known to play a major role in sporadic disorders with reduced fitness such as severe developmental disorders, including intellectual disability and epilepsy. Advanced paternal age is known to place offspring at increased disease risk, amongst others by increasing the number of DNMs in their genome.

[PHENOTYPICAL CHARACTERISTICS AND GENETICAL DETERMINANTS OF PATHOGENICITY OF STAPHYLOCOCCUS AUREUS, ISOLATED FROM BACTERIAL CARRIERS, RESIDING ON THE TERRITORIES WITH VARIOUS LEVELS OF ANTHROPOGENIC POLLUTION OF AIR ENVIRONMENT].

Aim: Comparative phenotypical and genetical evaluation of pathogenic potential of Staphylococcus aureus strains, isolated from resident bacterial carriers, residing on the territories with anthropogenic pollution of air environment of varying intensity.

Materials And Methods: S. aureus, isolated 3 times from mucous membrane of the anterior of nose from 210 children, were the object of the study.