AIMC Topic: High-Throughput Nucleotide Sequencing

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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...

A deep learning approach reveals unexplored landscape of viral expression in cancer.

Nature communications
About 15% of human cancer cases are attributed to viral infections. To date, virus expression in tumor tissues has been mostly studied by aligning tumor RNA sequencing reads to databases of known viruses. To allow identification of divergent viruses ...

Detecting genomic deletions from high-throughput sequence data with unsupervised learning.

BMC bioinformatics
BACKGROUND: Structural variation (SV), which ranges from 50 bp to [Formula: see text] 3 Mb in size, is an important type of genetic variations. Deletion is a type of SV in which a part of a chromosome or a sequence of DNA is lost during DNA replicati...

Symphonizing pileup and full-alignment for deep learning-based long-read variant calling.

Nature computational science
Deep learning-based variant callers are becoming the standard and have achieved superior single nucleotide polymorphisms calling performance using long reads. Here we present Clair3, which leverages two major method categories: pileup calling handles...

OrganoID: A versatile deep learning platform for tracking and analysis of single-organoid dynamics.

PLoS computational biology
Organoids have immense potential as ex vivo disease models for drug discovery and personalized drug screening. Dynamic changes in individual organoid morphology, number, and size can indicate important drug responses. However, these metrics are diffi...

Detection of Breast Cancer Lump and BRCA1/2 Genetic Mutation under Deep Learning.

Computational intelligence and neuroscience
To diagnose and cure breast cancer early, thus reducing the mortality of patients with breast cancer, a method was provided to judge threshold of image segmentation by wavelet transform (WT). It was used to obtain information about the general area o...

SVision: a deep learning approach to resolve complex structural variants.

Nature methods
Complex structural variants (CSVs) encompass multiple breakpoints and are often missed or misinterpreted. We developed SVision, a deep-learning-based multi-object-recognition framework, to automatically detect and haracterize CSVs from long-read sequ...

Automated filtering of genome-wide large deletions through an ensemble deep learning framework.

Methods (San Diego, Calif.)
Computational methods based on whole genome linked-reads and short-reads have been successful in genome assembly and detection of structural variants (SVs). Numerous variant callers that rely on linked-reads and short reads can detect genetic variati...

Challenges and opportunities associated with rare-variant pharmacogenomics.

Trends in pharmacological sciences
Recent advances in next-generation sequencing (NGS) have resulted in the identification of tens of thousands of rare pharmacogenetic variations with unknown functional effects. However, although such pharmacogenetic variations have been estimated to ...

A review of deep learning applications in human genomics using next-generation sequencing data.

Human genomics
Genomics is advancing towards data-driven science. Through the advent of high-throughput data generating technologies in human genomics, we are overwhelmed with the heap of genomic data. To extract knowledge and pattern out of this genomic data, arti...