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Genetic Variation

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16S classifier: a tool for fast and accurate taxonomic classification of 16S rRNA hypervariable regions in metagenomic datasets.

PloS one
The diversity of microbial species in a metagenomic study is commonly assessed using 16S rRNA gene sequencing. With the rapid developments in genome sequencing technologies, the focus has shifted towards the sequencing of hypervariable regions of 16S...

Types and effects of protein variations.

Human genetics
Variations in proteins have very large number of diverse effects affecting sequence, structure, stability, interactions, activity, abundance and other properties. Although protein-coding exons cover just over 1 % of the human genome they harbor an di...

DANN: a deep learning approach for annotating the pathogenicity of genetic variants.

Bioinformatics (Oxford, England)
UNLABELLED: Annotating genetic variants, especially non-coding variants, for the purpose of identifying pathogenic variants remains a challenge. Combined annotation-dependent depletion (CADD) is an algorithm designed to annotate both coding and non-c...

MIRACN: a residual convolutional neural network for predicting cell line specific functional regulatory variants.

Briefings in bioinformatics
In post-genome-wide association study era, interpretation of noncoding variants remains a significant challenge due to their complexity and the limited understanding of their functions. Here, we developed MIRACN, a novel residual convolutional neural...

A graph neural network approach for accurate prediction of pathogenicity in multi-type variants.

Briefings in bioinformatics
Accurate prediction of pathogenic variants in human disease-associated genes would have a profound effect on clinical decision-making; however, it remains a significant challenge due to the overwhelming number of these variants. We propose graph neur...

Hepatitis C Virus Saint Petersburg Variant Detection With Machine Learning Methods.

Journal of medical virology
Hepatitis C virus infection is a significant global health concern, affecting millions worldwide. Although direct-acting antivirals achieve over 90% success rate, treatment failures still occur, particularly when pan-genotypic DAAs are unavailable, a...

Using GWAS and Machine Learning to Identify and Predict Genetic Variants Associated with Foodborne Bacteria Phenotypic Traits.

Methods in molecular biology (Clifton, N.J.)
One of the main challenges in food microbiology is to prevent the risk of outbreaks by avoiding the distribution of food contaminated by bacteria. This requires constant monitoring of the circulating strains throughout the food production chain. Bact...

LOGOWheat: deep learning-based prediction of regulatory effects for noncoding variants in wheats.

Briefings in bioinformatics
Identifying the regulatory effects of noncoding variants presents a significant challenge. Recently, the accumulation of epigenomic profiling data in wheat has provided an opportunity to model the functional impacts of these variants. In this study, ...

Deep learning approaches for non-coding genetic variant effect prediction: current progress and future prospects.

Briefings in bioinformatics
Recent advancements in high-throughput sequencing technologies have significantly enhanced our ability to unravel the intricacies of gene regulatory processes. A critical challenge in this endeavor is the identification of variant effects, a key fact...