scholarly journals Co-extraction of genomic DNA & total RNA from recalcitrant woody tissues for next-generation sequencing studies

2018 ◽  
Vol 4 (6) ◽  
pp. FSO309
Author(s):  
Zhen Zeng ◽  
Tommaso Raffaello ◽  
Meng-Xia Liu ◽  
Fred O Asiegbu
Keyword(s):  
Next Generation Sequencing ◽  
Genomic Dna ◽  
Next Generation ◽  
Total Rna ◽  
Sequencing Studies ◽  
Woody Tissues ◽  
Generation Sequencing

scholarly journals Targeted enrichment of genomic DNA regions for next-generation sequencing

2011 ◽  
Vol 10 (6) ◽  
pp. 374-386 ◽  
Author(s):  
F. Mertes ◽  
A. ElSharawy ◽  
S. Sauer ◽  
J. M. L. M. van Helvoort ◽  
P. J. van der Zaag ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Genomic Dna ◽  
Next Generation ◽  
Generation Sequencing ◽  
Targeted Enrichment

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 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

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 GeneMed: An Informatics Hub for the Coordination of Next-Generation Sequencing Studies that Support Precision Oncology Clinical Trials

2015 ◽  
Vol 14s2 ◽  
pp. CIN.S17282 ◽  
Author(s):  
Yingdong Zhao ◽  
Eric C. Polley ◽  
Ming-Chung Li ◽  
Chih-Jian Lih ◽  
Alida Palmisano ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Next Generation Sequencing ◽  
Treatment Selection ◽  
Control Treatment ◽  
Precision Oncology ◽  
Next Generation ◽  
Multiple Cancer ◽  
Randomized Design ◽  
Sequencing Studies ◽  
Generation Sequencing

We have developed an informatics system, GeneMed, for the National Cancer Institute (NCI) molecular profiling-based assignment of cancer therapy (MPACT) clinical trial (NCT01827384) being conducted in the National Institutes of Health (NIH) Clinical Center. This trial is one of the first to use a randomized design to examine whether assigning treatment based on genomic tumor screening can improve the rate and duration of response in patients with advanced solid tumors. An analytically validated next-generation sequencing (NGS) assay is applied to DNA from patients’ tumors to identify mutations in a panel of genes that are thought likely to affect the utility of targeted therapies available for use in the clinical trial. The patients are randomized to a treatment selected to target a somatic mutation in the tumor or with a control treatment. The GeneMed system streamlines the workflow of the clinical trial and serves as a communications hub among the sequencing lab, the treatment selection team, and clinical personnel. It automates the annotation of the genomic variants identified by sequencing, predicts the functional impact of mutations, identifies the actionable mutations, and facilitates quality control by the molecular characterization lab in the review of variants. The GeneMed system collects baseline information about the patients from the clinic team to determine eligibility for the panel of drugs available. The system performs randomized treatment assignments under the oversight of a supervising treatment selection team and generates a patient report containing detected genomic alterations. NCI is planning to expand the MPACT trial to multiple cancer centers soon. In summary, the GeneMed system has been proven to be an efficient and successful informatics hub for coordinating the reliable application of NGS to precision medicine studies.

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