scholarly journals PhredEM: a phred-score-informed genotype-calling approach for next-generation sequencing studies

2017 ◽  
Vol 41 (5) ◽  
pp. 375-387 ◽  
Author(s):  
Peizhou Liao ◽  
Glen A. Satten ◽  
Yi-Juan Hu
Keyword(s):  
Next Generation Sequencing ◽  
Next Generation ◽  
Genotype Calling ◽  
Phred Score ◽  
Sequencing Studies ◽  
Generation Sequencing

scholarly journals PhredEM: A Phred-Score-Informed Genotype-Calling Approach for Next-Generation Sequencing Studies

2016 ◽  
Author(s):  
Peizhou Liao ◽  
Glen A. Satten ◽  
Yi-juan Hu
Keyword(s):  
Logistic Regression ◽  
Next Generation Sequencing ◽  
Em Algorithm ◽  
Error Rates ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
Genotype Calling ◽  
Sequencing Studies ◽  
Generation Sequencing

ABSTRACTA fundamental challenge in analyzing next-generation sequencing data is to determine an individual’s genotype correctly as the accuracy of the inferred genotype is essential to downstream analyses. Some genotype callers, such as GATK and SAMtools, directly calculate the base-calling error rates from phred scores or recalibrated base quality scores. Others, such as SeqEM, estimate error rates from the read data without using any quality scores. It is also a common quality control procedure to filter out reads with low phred scores. However, choosing an appropriate phred score threshold is problematic as a too-high threshold may lose data while a too-low threshold may introduce errors. We propose a new likelihood-based genotype-calling approach that exploits all reads and estimates the per-base error rates by incorporating phred scores through a logistic regression model. The algorithm, which we call PhredEM, uses the Expectation-Maximization (EM) algorithm to obtain consistent estimates of genotype frequencies and logistic regression parameters. We also develop a simple, computationally efficient screening algorithm to identify loci that are estimated to be monomorphic, so that only loci estimated to be non-monomorphic require application of the EM algorithm. We evaluate the performance of PhredEM using both simulated data and real sequencing data from the UK10K project. The results demonstrate that PhredEM is an improved, robust and widely applicable genotype-calling approach for next-generation sequencing studies. The relevant software is freely available.

scholarly journals SeqEM: an adaptive genotype-calling approach for next-generation sequencing studies

2010 ◽  
Vol 26 (22) ◽  
pp. 2803-2810 ◽  
Author(s):  
E. R. Martin ◽  
D. D. Kinnamon ◽  
M. A. Schmidt ◽  
E. H. Powell ◽  
S. Zuchner ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Next Generation ◽  
Genotype Calling ◽  
Sequencing Studies ◽  
Generation Sequencing

scholarly journals HDAM: a resource of human disease associated mutations from next generation sequencing studies

2013 ◽  
Vol 6 (S1) ◽  
Author(s):  
Meiwen Jia ◽  
Yanli Liu ◽  
Zhongchao Shen ◽  
Chen Zhao ◽  
Meixia Zhang ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Human Disease ◽  
Next Generation ◽  
Sequencing Studies ◽  
Generation Sequencing

Pharmacogenomics variants are associated with BMI differences between individuals with bipolar and other psychiatric disorders

2021 ◽  
Vol 22 (12) ◽  
pp. 749-760
Author(s):  
Aggeliki Charalampidi ◽  
Zoe Kordou ◽  
Evangelia-Eirini Tsermpini ◽  
Panagiotis Bosganas ◽  
Wasun Chantratita ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Psychiatric Disorder ◽  
Psychiatric Disorders ◽  
Statistical Methods ◽  
Potential Effect ◽  
Next Generation ◽  
Potential Influence ◽  
Sequencing Studies ◽  
The Mean ◽  
Generation Sequencing

Aim: Regardless of the plethora of next-generation sequencing studies in the field of pharmacogenomics (PGx), the potential effect of covariate variables on PGx response within deeply phenotyped cohorts remains unexplored. Materials & methods: We explored with advanced statistical methods the potential influence of BMI, as a covariate variable, on PGx response in a Greek cohort with psychiatric disorders. Results: Nine PGx variants within UGT1A6, SLC22A4, GSTP1, CYP4B1, CES1, SLC29A3 and DPYD were associated with altered BMI in different psychiatric disorder groups. Carriers of rs2070959 ( UGT1A6), rs199861210 ( SLC29A3) and rs2297595 ( DPYD) were also characterized by significant changes in the mean BMI, depending on the presence of psychiatric disorders. Conclusion: Specific PGx variants are significantly associated with BMI in a Greek cohort with psychiatric disorders.

scholarly journals Genes, pathways and vulvar carcinoma - New insights from next-generation sequencing studies

2020 ◽  
Vol 158 (2) ◽  
pp. 498-506 ◽  
Author(s):  
Sebastian Zięba ◽  
Magdalena Chechlińska ◽  
Artur Kowalik ◽  
Magdalena Kowalewska
Keyword(s):  
Next Generation Sequencing ◽  
Vulvar Carcinoma ◽  
Next Generation ◽  
Sequencing Studies ◽  
Generation Sequencing
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