AIMC Topic: High-Throughput Nucleotide Sequencing

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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...

Performance analysis of conventional and AI-based variant callers using short and long reads.

BMC bioinformatics
BACKGROUND: The accurate detection of variants is essential for genomics-based studies. Currently, there are various tools designed to detect genomic variants, however, it has always been a challenge to decide which tool to use, especially when vario...

Generating bulk RNA-Seq gene expression data based on generative deep learning models and utilizing it for data augmentation.

Computers in biology and medicine
Large-scale high-throughput transcriptome sequencing data holds significant value in biomedical research. However, practical challenges such as difficulty in sample acquisition often limit the availability of large sample sizes, leading to decreased ...

Deep-learning-assisted Sort-Seq enables high-throughput profiling of gene expression characteristics with high precision.

Science advances
Owing to the nondeterministic and nonlinear nature of gene expression, the steady-state intracellular protein abundance of a clonal population forms a distribution. The characteristics of this distribution, including expression strength and noise, ar...

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...

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 ...

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...

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...

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 ...