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High-Throughput Nucleotide Sequencing

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Deep Learning on Chromatin Accessibility.

Methods in molecular biology (Clifton, N.J.)
DNA accessibility has been a powerful tool in locating active regulatory elements in a cell type, but dissecting the combinatorial logic within these regulatory elements has been a continued challenge in the field. Deep learning models have been show...

Single-cell RNA-seq data analysis based on directed graph neural network.

Methods (San Diego, Calif.)
Single-cell RNA sequencing (scRNA-seq) data scale surges with high-throughput sequencing technology development. However, although single-cell data analysis is a powerful tool, various issues have been reported, such as sequencing sparsity and comple...

Applying T-classifier, binary classifiers, upon high-throughput TCR sequencing output to identify cytomegalovirus exposure history.

Scientific reports
With the continuous development of information technology and the running speed of computers, the development of informatization has led to the generation of increasingly more medical data. Solving unmet needs such as employing the constantly develop...

DeltaMSI: artificial intelligence-based modeling of microsatellite instability scoring on next-generation sequencing data.

BMC bioinformatics
BACKGROUND: DNA mismatch repair deficiency (dMMR) testing is crucial for detection of microsatellite unstable (MSI) tumors. MSI is detected by aberrant indel length distributions of microsatellite markers, either by visual inspection of PCR-fragment ...

A scoping review on deep learning for next-generation RNA-Seq. data analysis.

Functional & integrative genomics
In the last decade, transcriptome research adopting next-generation sequencing (NGS) technologies has gathered incredible momentum amongst functional genomics scientists, particularly amongst clinical/biomedical research groups. The progressive enfol...

DeepBIO: an automated and interpretable deep-learning platform for high-throughput biological sequence prediction, functional annotation and visualization analysis.

Nucleic acids research
Here, we present DeepBIO, the first-of-its-kind automated and interpretable deep-learning platform for high-throughput biological sequence functional analysis. DeepBIO is a one-stop-shop web service that enables researchers to develop new deep-learni...

Improving variant calling using population data and deep learning.

BMC bioinformatics
Large-scale population variant data is often used to filter and aid interpretation of variant calls in a single sample. These approaches do not incorporate population information directly into the process of variant calling, and are often limited to ...

The impact of misclassification errors on the performance of biomarkers based on next-generation sequencing, a simulation study.

Journal of biopharmaceutical statistics
The development of next-generation sequencing (NGS) opens opportunities for new applications such as liquid biopsy, in which tumor mutation genotypes can be determined by sequencing circulating tumor DNA after blood draws. However, with highly dilute...

Validation of genetic variants from NGS data using deep convolutional neural networks.

BMC bioinformatics
Accurate somatic variant calling from next-generation sequencing data is one most important tasks in personalised cancer therapy. The sophistication of the available technologies is ever-increasing, yet, manual candidate refinement is still a necessa...

Advances in next-generation sequencing and emerging technologies for hematologic malignancies.

Haematologica
Innovations in molecular diagnostics have often evolved through the study of hematologic malignancies. Examples include the pioneering characterization of the Philadelphia chromosome by cytogenetics in the 1970s, the implementation of polymerase chai...