scholarly journals A Simple Deep Learning Approach for Detecting Duplications and Deletions in Next-Generation Sequencing Data

2019 ◽  
Author(s):  
Tom Hill ◽  
Robert L. Unckless
Keyword(s):  
Machine Learning ◽  
Next Generation Sequencing ◽  
Copy Number Variants ◽  
Difficult Problem ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
High Coverage ◽  
Long Read ◽  
Generation Sequencing

AbstractCopy number variants (CNV) are associated with phenotypic variation in several species. However, properly detecting changes in copy numbers of sequences remains a difficult problem, especially in lower quality or lower coverage next-generation sequencing data. Here, inspired by recent applications of machine learning in genomics, we describe a method to detect duplications and deletions in short-read sequencing data. In low coverage data, machine learning appears to be more powerful in the detection of CNVs than the gold-standard methods or coverage estimation alone, and of equal power in high coverage data. We also demonstrate how replicating training sets allows a more precise detection of CNVs, even identifying novel CNVs in two genomes previously surveyed thoroughly for CNVs using long read data.Available at: https://github.com/tomh1lll/dudeml

scholarly journals Methods for analyzing next-generation sequencing data VII. long-read assembly

2016 ◽  
Vol 27 (2) ◽  
pp. 101-110
Author(s):  
Yasuhiro Tanizawa ◽  
Eli Kaminuma ◽  
Yasukazu Nakamura ◽  
Masanori Tohno ◽  
Ken Osaki ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
Long Read ◽  
Generation Sequencing

scholarly journals Exogene: A performant workflow for detecting viral integrations from paired-end next-generation sequencing data

2021 ◽  
Author(s):  
Jean-Pierre Kocher ◽  
Zachary Stephens ◽  
Daniel O'Brien ◽  
Mrunal Dehankar ◽  
Lewis Roberts ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Sequence Data ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
Long Read ◽  
Breakpoint Detection ◽  
Targeted Capture ◽  
Genome Heterogeneity ◽  
Generation Sequencing

The integration of viruses into the human genome is known to be associated with tumorigenesis in many cancers, but the accurate detection of integration breakpoints from short read sequencing data is made difficult by human-viral homologies, viral genome heterogeneity, coverage limitations, and other factors. To address this, we present Exogene, a sensitive and efficient workflow for detecting viral integrations from paired-end next generation sequencing data. Exogene's read filtering and breakpoint detection strategies yield integration coordinates that are highly concordant with those found in long read validation sets. We demonstrate this concordance across 6 TCGA Hepatocellular carcinoma (HCC) tumor samples, identifying integrations of hepatitis B virus that are validated by long reads. Additionally, we applied Exogene to targeted capture data from 426 previously studied HCC samples, achieving 98.9% concordance with existing methods and identifying 238 high-confidence integrations that were not previously reported. Exogene is applicable to multiple types of paired-end sequence data, including genome, exome, RNA-Seq or targeted capture.

scholarly journals Exogene: A performant workflow for detecting viral integrations from paired-end next-generation sequencing data

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0250915
Author(s):  
Zachary Stephens ◽  
Daniel O’Brien ◽  
Mrunal Dehankar ◽  
Lewis R. Roberts ◽  
Ravishankar K. Iyer ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Sequence Data ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
Long Read ◽  
Breakpoint Detection ◽  
Targeted Capture ◽  
Genome Heterogeneity ◽  
Generation Sequencing

The integration of viruses into the human genome is known to be associated with tumorigenesis in many cancers, but the accurate detection of integration breakpoints from short read sequencing data is made difficult by human-viral homologies, viral genome heterogeneity, coverage limitations, and other factors. To address this, we present Exogene, a sensitive and efficient workflow for detecting viral integrations from paired-end next generation sequencing data. Exogene’s read filtering and breakpoint detection strategies yield integration coordinates that are highly concordant with long read validation. We demonstrate this concordance across 6 TCGA Hepatocellular carcinoma (HCC) tumor samples, identifying integrations of hepatitis B virus that are also supported by long reads. Additionally, we applied Exogene to targeted capture data from 426 previously studied HCC samples, achieving 98.9% concordance with existing methods and identifying 238 high-confidence integrations that were not previously reported. Exogene is applicable to multiple types of paired-end sequence data, including genome, exome, RNA-Seq and targeted capture.

scholarly journals Machine Learning Methods as a Tool for Predicting Risk of Illness Applying Next‐Generation Sequencing Data

Risk Analysis ◽  
2018 ◽  
Author(s):  
Patrick Murigu Kamau Njage ◽  
Clementine Henri ◽  
Pimlapas Leekitcharoenphon ◽  
Michel‐Yves Mistou ◽  
Rene S. Hendriksen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Next Generation Sequencing ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Generation Sequencing
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