Gene mutations in gastric cancer: a review of recent next-generation sequencing studies

Tumor Biology ◽  
2015 ◽  
Vol 36 (10) ◽  
pp. 7385-7394 ◽  
Author(s):  
Y. Lin ◽  
Z. Wu ◽  
W. Guo ◽  
J. Li
Keyword(s):  
Gastric Cancer ◽  
Next Generation Sequencing ◽  
Gene Mutations ◽  
Next Generation ◽  
Sequencing Studies ◽  
Generation Sequencing

Next-generation sequencing reveals hsa_circ_0058092 being a potential oncogene candidate involved in gastric cancer

Gene ◽  
2020 ◽  
Vol 726 ◽  
pp. 144176 ◽  
Author(s):  
Xuefeng Bu ◽  
Xuanfeng Zhang ◽  
Wenkang Luan ◽  
Riting Zhang ◽  
Yao Zhang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Next Generation Sequencing ◽  
Next Generation ◽  
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 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
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