AIMC Topic: High-Throughput Nucleotide Sequencing

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Predicting mutant outcome by combining deep mutational scanning and machine learning.

Proteins
Deep mutational scanning provides unprecedented wealth of quantitative data regarding the functional outcome of mutations in proteins. A single experiment may measure properties (eg, structural stability) of numerous protein variants. Leveraging the ...

The use of a next-generation sequencing-derived machine-learning risk-prediction model (OncoCast-MPM) for malignant pleural mesothelioma: a retrospective study.

The Lancet. Digital health
BACKGROUND: Current risk stratification for patients with malignant pleural mesothelioma based on disease stage and histology is inadequate. For some individuals with early-stage epithelioid tumours, a good prognosis by current guidelines can progres...

Prediction model for malignant pulmonary nodules based on cfMeDIP-seq and machine learning.

Cancer science
Cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP-seq) is a new bisulfite-free technique, which can detect the whole-genome methylation of blood cell-free DNA (cfDNA). Using this technique, we identified differentia...

A deep learning model for predicting next-generation sequencing depth from DNA sequence.

Nature communications
Targeted high-throughput DNA sequencing is a primary approach for genomics and molecular diagnostics, and more recently as a readout for DNA information storage. Oligonucleotide probes used to enrich gene loci of interest have different hybridization...

Analysis of DNA Sequence Classification Using CNN and Hybrid Models.

Computational and mathematical methods in medicine
In a general computational context for biomedical data analysis, DNA sequence classification is a crucial challenge. Several machine learning techniques have used to complete this task in recent years successfully. Identification and classification o...

Ultrasensitive detection of circulating tumour DNA via deep methylation sequencing aided by machine learning.

Nature biomedical engineering
The low abundance of circulating tumour DNA (ctDNA) in plasma samples makes the analysis of ctDNA biomarkers for the detection or monitoring of early-stage cancers challenging. Here we show that deep methylation sequencing aided by a machine-learning...

DNAscent v2: detecting replication forks in nanopore sequencing data with deep learning.

BMC genomics
BACKGROUND: Measuring DNA replication dynamics with high throughput and single-molecule resolution is critical for understanding both the basic biology behind how cells replicate their DNA and how DNA replication can be used as a therapeutic target f...

MetaVelvet-DL: a MetaVelvet deep learning extension for de novo metagenome assembly.

BMC bioinformatics
BACKGROUND: The increasing use of whole metagenome sequencing has spurred the need to improve de novo assemblers to facilitate the discovery of unknown species and the analysis of their genomic functions. MetaVelvet-SL is a short-read de novo metagen...