scholarly journals Challenges in detecting genomic copy number aberrations using next-generation sequencing data and the eXome Hidden Markov Model: a clinical exome-first diagnostic approach

2016 ◽  
Vol 3 (1) ◽  
Author(s):  
Toshiyuki Yamamoto ◽  
Keiko Shimojima ◽  
Yumiko Ondo ◽  
Katsumi Imai ◽  
Pin Fee Chong ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Copy Number ◽  
Hidden Markov ◽  
Next Generation Sequencing Data ◽  
Sequencing Data ◽  
Copy Number Aberrations ◽  
Genomic Copy Number ◽  
Generation Sequencing

scholarly journals BCFtools/RoH: a hidden Markov model approach for detecting autozygosity from next-generation sequencing data

2016 ◽  
Vol 32 (11) ◽  
pp. 1749-1751 ◽  
Author(s):  
Vagheesh Narasimhan ◽  
Petr Danecek ◽  
Aylwyn Scally ◽  
Yali Xue ◽  
Chris Tyler-Smith ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
Generation Sequencing ◽  
Model Approach

A hidden Markov-model for gene mapping based on whole-genome next generation sequencing data

2015 ◽  
Vol 14 (1) ◽  
Author(s):  
Jürgen Claesen ◽  
Tomasz Burzykowski
Keyword(s):  
Next Generation Sequencing ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Genetic Markers ◽  
High Throughput Sequencing ◽  
Transition Probabilities ◽  
Hidden Markov ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Generation Sequencing

AbstractThe analysis of polygenic, phenotypic characteristics such as quantitative traits or inheritable diseases requires reliable scoring of many genetic markers covering the entire genome. The advent of high-throughput sequencing technologies provides a new way to evaluate large numbers of single nucleotide polymorphisms as genetic markers. Combining the technologies with pooling of segregants, as performed in bulk segregant analysis, should, in principle, allow the simultaneous mapping of multiple genetic loci present throughout the genome. We propose a hidden Markov-model to analyze the marker data obtained by the bulk segregant next generation sequencing. The model includes several states, each associated with a different probability of observing the same/different nucleotide in an offspring as compared to the parent. The transitions between the molecular markers imply transitions between the states of the model. After estimating the transition probabilities and state-related probabilities of nucleotide (dis)similarity, the most probable state for each SNP is selected. The most probable states can then be used to indicate which genomic regions may be likely to contain trait-related genes. The application of the model is illustrated on the data from a study of ethanol tolerance in yeast. Software is written in R. R-functions, R-scripts and documentation are available on

scholarly journals cn.MOPS: mixture of Poissons for discovering copy number variations in next-generation sequencing data with a low false discovery rate

2012 ◽  
Vol 40 (9) ◽  
pp. e69-e69 ◽  
Author(s):  
Günter Klambauer ◽  
Karin Schwarzbauer ◽  
Andreas Mayr ◽  
Djork-Arné Clevert ◽  
Andreas Mitterecker ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
False Discovery Rate ◽  
Copy Number ◽  
Copy Number Variations ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
False Discovery ◽  
Generation Sequencing

scholarly journals Assessing Copy Number Alterations in Targeted, Amplicon-Based Next-Generation Sequencing Data

2015 ◽  
Vol 17 (1) ◽  
pp. 53-63 ◽  
Author(s):  
Catherine Grasso ◽  
Timothy Butler ◽  
Katherine Rhodes ◽  
Michael Quist ◽  
Tanaya L. Neff ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Copy Number ◽  
Next Generation Sequencing Data ◽  
Copy Number Alterations ◽  
Next Generation ◽  
Sequencing Data ◽  
Generation Sequencing
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