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High-Throughput Nucleotide Sequencing

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A neural network based model effectively predicts enhancers from clinical ATAC-seq samples.

Scientific reports
Enhancers are cis-acting sequences that regulate transcription rates of their target genes in a cell-specific manner and harbor disease-associated sequence variants in cognate cell types. Many complex diseases are associated with enhancer malfunction...

A universal SNP and small-indel variant caller using deep neural networks.

Nature biotechnology
Despite rapid advances in sequencing technologies, accurately calling genetic variants present in an individual genome from billions of short, errorful sequence reads remains challenging. Here we show that a deep convolutional neural network can call...

Predicting oral malodour based on the microbiota in saliva samples using a deep learning approach.

BMC oral health
BACKGROUND: Oral malodour is mainly caused by volatile sulphur compounds produced by bacteria and bacterial interactions. It is difficult to predict the presence or absence of oral malodour based on the abundances of specific species and their combin...

Machine learning approaches infer vitamin D signaling: Critical impact of vitamin D receptor binding within topologically associated domains.

The Journal of steroid biochemistry and molecular biology
The vitamin D-modulated transcriptome of highly responsive human cells, such as THP-1 monocytes, comprises more than 500 genes, half of which are primary targets. Recently, we proposed a chromatin model of vitamin D signaling demonstrating that nearl...

HECIL: A Hybrid Error Correction Algorithm for Long Reads with Iterative Learning.

Scientific reports
Second-generation DNA sequencing techniques generate short reads that can result in fragmented genome assemblies. Third-generation sequencing platforms mitigate this limitation by producing longer reads that span across complex and repetitive regions...

Tracking antibiotic resistance gene pollution from different sources using machine-learning classification.

Microbiome
BACKGROUND: Antimicrobial resistance (AMR) has been a worldwide public health concern. Current widespread AMR pollution has posed a big challenge in accurately disentangling source-sink relationship, which has been further confounded by point and non...

Identifying 5-methylcytosine sites in RNA sequence using composite encoding feature into Chou's PseKNC.

Journal of theoretical biology
This study examines accurate and efficient computational method for identification of 5-methylcytosine sites in RNA modification. The occurrence of 5-methylcytosine (mC) plays a vital role in a number of biological processes. For better comprehension...

A machine learning model to determine the accuracy of variant calls in capture-based next generation sequencing.

BMC genomics
BACKGROUND: Next generation sequencing (NGS) has become a common technology for clinical genetic tests. The quality of NGS calls varies widely and is influenced by features like reference sequence characteristics, read depth, and mapping accuracy. Wi...

Why Deep Learning Is Changing the Way to Approach NGS Data Processing: A Review.

IEEE reviews in biomedical engineering
Nowadays, big data analytics in genomics is an emerging research topic. In fact, the large amount of genomics data originated by emerging next-generation sequencing (NGS) techniques requires more and more fast and sophisticated algorithms. In this co...

Cox-nnet: An artificial neural network method for prognosis prediction of high-throughput omics data.

PLoS computational biology
Artificial neural networks (ANN) are computing architectures with many interconnections of simple neural-inspired computing elements, and have been applied to biomedical fields such as imaging analysis and diagnosis. We have developed a new ANN frame...