scholarly journals In-depth comparison of somatic point mutation callers based on different tumor next-generation sequencing depth data

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Lei Cai ◽  
Wei Yuan ◽  
Zhou Zhang ◽  
Lin He ◽  
Kuo-Chen Chou
Keyword(s):  
Next Generation Sequencing ◽  
Point Mutation ◽  
Sequencing Depth ◽  
Next Generation ◽  
Depth Data ◽  
Generation Sequencing ◽  
Somatic Point Mutation

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

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jinny X. Zhang ◽  
Boyan Yordanov ◽  
Alexander Gaunt ◽  
Michael X. Wang ◽  
Peng Dai ◽  
...  
Keyword(s):  
Deep Learning ◽  
Next Generation Sequencing ◽  
Learning Model ◽  
Sequencing Depth ◽  
Information Storage ◽  
Nucleotide Polymorphisms ◽  
Next Generation ◽  
Snp Panel ◽  
Deep Learning Model ◽  
Generation Sequencing

AbstractTargeted 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 kinetics, resulting in non-uniform coverage that increases sequencing costs and decreases sequencing sensitivities. Here, we present a deep learning model (DLM) for predicting Next-Generation Sequencing (NGS) depth from DNA probe sequences. Our DLM includes a bidirectional recurrent neural network that takes as input both DNA nucleotide identities as well as the calculated probability of the nucleotide being unpaired. We apply our DLM to three different NGS panels: a 39,145-plex panel for human single nucleotide polymorphisms (SNP), a 2000-plex panel for human long non-coding RNA (lncRNA), and a 7373-plex panel targeting non-human sequences for DNA information storage. In cross-validation, our DLM predicts sequencing depth to within a factor of 3 with 93% accuracy for the SNP panel, and 99% accuracy for the non-human panel. In independent testing, the DLM predicts the lncRNA panel with 89% accuracy when trained on the SNP panel. The same model is also effective at predicting the measured single-plex kinetic rate constants of DNA hybridization and strand displacement.

Labordiagnostik bei gastrointestinalen Tumoren

2020 ◽  
Vol 11 (05) ◽  
pp. 232-238
Author(s):  
Marcus Kleber
Keyword(s):  
Next Generation Sequencing ◽  
Kolorektales Karzinom ◽  
Next Generation ◽  
Generation Sequencing

ZUSAMMENFASSUNGDas kolorektale Karzinom (KRK) ist einer der häufigsten malignen Tumoren in Deutschland. Einer frühzeitigen Diagnostik kommt große Bedeutung zu. Goldstandard ist hier die Koloskopie. Die aktuelle S3-Leitlinie Kolorektales Karzinom empfiehlt zum KRK-Screening den fäkalen okkulten Bluttest. Für das Monitoring von Patienten vor und nach Tumorresektion werden die Messung des Carcinoembryonalen Antigens (CEA) und der Mikrosatellitenstabilität empfohlen. Für die Auswahl der korrekten Chemotherapie scheint derzeit eine Überprüfung des Mutationsstatus, mindestens des KRAS-Gens und des BRAF-Gens, sinnvoll zu sein. Eine Reihe an neuartigen Tumormarkern befindet sich momentan in der Entwicklung, hat jedoch noch nicht die Reife für eine mögliche Anwendung in der Routinediagnostik erreicht. Den schnellsten Weg in die breite Anwendung können Next-Generation-Sequencing-basierte genetische Tests finden.

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