Current Genomics - Volume 23, Issue 5, 2022

Genome sequences indicate a wide variety of characteristics, which include species and sub-species type, genotype, diseases, growth indicators, yield quality, etc. To analyze and study the characteristics of the genome sequences across different species, various deep learning models have been proposed by researchers, such as Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), Multilayer Perceptrons (ML Read More

Background: Circular RNAs (circRNAs) are transcribed by RNA polymerase II and are mostly generated by the back-splicing of exons in the protein-coding gene. Massive circRNAs are reported to be differentially expressed in different species, implicating their prospects as aging biomarkers or regulators in the aging progression. Methods: The possible role of circRNAs in aging and longevity was reviewed by the query of circRNA Read More

In the decade since non-invasive prenatal testing (NIPT) was first implemented as a prenatal screening tool, it has gained recognition for its sensitivity and specificity in the detection of common aneuploidies. This review mainly focuses on the emerging role of NIPT in pregnancies following assisted reproductive technology (ART) in the light of current evidence and recommendations. It also deals with the challenges Read More

Preimplantation Genetic Testing (PGT) aims to reduce the chance of an affected pregnancy or improve success in an assisted reproduction cycle. Since the first established pregnancies in 1990, methodological approaches have greatly evolved, combined with significant advances in the embryological laboratory. The application of preimplantation testing has expanded, while the accuracy and reliability of monogenic and Read More

Background: One major challenge in binning Metagenomics data is the limited availability of reference datasets, as only 1% of the total microbial population is yet cultured. This has given rise to the efficacy of unsupervised methods for binning in the absence of any reference datasets. Objective: To develop a deep clustering-based binning approach for Metagenomics data and to evaluate results with suitable measures. Read More