scholarly journals Accurate detection of low-level somatic mutations with technical replication for next-generation sequencing

2017 ◽  
Author(s):  
Junho Kim ◽  
Dachan Kim ◽  
Jae Seok Lim ◽  
Ju Heon Maeng ◽  
Hyeonju Son ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Somatic Mutations ◽  
Computational Method ◽  
Joint Analysis ◽  
Detection Accuracy ◽  
Sequencing Analysis ◽  
Next Generation ◽  
Sequencing Data ◽  
New Paradigm ◽  
Generation Sequencing

ABSTRACTAccurate genome-wide detection of somatic mutations with low variant allele frequency (VAF, <1%) has proven difficult, for which generalized, scalable methods are lacking. Herein, we describe a new computational method, called RePlow that we developed to detect low-VAF somatic mutations based on simple, library-level replicates for next-generation sequencing on any platform. Through joint analysis of replicates, RePlow is able to remove prevailing background errors in next-generation sequencing analysis, facilitating remarkable improvement in the detection accuracy for low-VAF somatic mutations (up to ∼99% reduction in false positives). The method was validated in independent cancer panel and brain tissue sequencing data. Our study suggests a new paradigm with which to exploit an overwhelming abundance of sequencing data for accurate variant detection.

scholarly journals Detection of Pathogenic Microbe Composition Using Next-Generation Sequencing Data

2020 ◽  
Vol 11 ◽  
Author(s):  
Haiyong Zhao ◽  
Shuang Wang ◽  
Xiguo Yuan
Keyword(s):  
Next Generation Sequencing ◽  
Computational Method ◽  
Superior Performance ◽  
Next Generation Sequencing Data ◽  
Support Vector ◽  
Next Generation ◽  
Sequencing Data ◽  
Microbial Composition ◽  
High Resolution Data ◽  
Generation Sequencing

Next-generation sequencing (NGS) technologies have provided great opportunities to analyze pathogenic microbes with high-resolution data. The main goal is to accurately detect microbial composition and abundances in a sample. However, high similarity among sequences from different species and the existence of sequencing errors pose various challenges. Numerous methods have been developed for quantifying microbial composition and abundance, but they are not versatile enough for the analysis of samples with mixtures of noise. In this paper, we propose a new computational method, PGMicroD, for the detection of pathogenic microbial composition in a sample using NGS data. The method first filters the potentially mistakenly mapped reads and extracts multiple species-related features from the sequencing reads of 16S rRNA. Then it trains an Support Vector Machine classifier to predict the microbial composition. Finally, it groups all multiple-mapped sequencing reads into the references of the predicted species to estimate the abundance for each kind of species. The performance of PGMicroD is evaluated based on both simulation and real sequencing data and is compared with several existing methods. The results demonstrate that our proposed method achieves superior performance. The software package of PGMicroD is available at https://github.com/BDanalysis/PGMicroD.

Challenges in next generation sequencing analysis of somatic mutations in transplant patients

2018 ◽  
Vol 226-227 ◽  
pp. 17-22 ◽  
Author(s):  
Hui Chen ◽  
Rajyalakshmi Luthra ◽  
Keyur P Patel ◽  
Mark Routbort ◽  
Asif Rashid ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Somatic Mutations ◽  
Sequencing Analysis ◽  
Next Generation ◽  
Transplant Patients ◽  
Next Generation Sequencing Analysis ◽  
Generation Sequencing

scholarly journals Next-generation sequencing for identification of actionable gene mutations in intestinal-type sinonasal adenocarcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Paula Sánchez-Fernández ◽  
Cristina Riobello ◽  
María Costales ◽  
Blanca Vivanco ◽  
Virginia N. Cabal ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Somatic Mutations ◽  
Treatment Options ◽  
Gene Mutations ◽  
Tumor Stage ◽  
Intestinal Type ◽  
Sequencing Analysis ◽  
Next Generation ◽  
Sinonasal Adenocarcinoma ◽  
Generation Sequencing

AbstractIntestinal-type sinonasal adenocarcinoma (ITAC) is a rare tumor carrying poor prognosis and needing new treatment options. The aim of this study was to identify actionable gene mutations that can guide new personalized target-specific therapies in ITAC patients. A series of 48 tumor and 27 corresponding germline DNA samples were analyzed by next generation sequencing using a panel of 120 genes. In total, 223 sequence variants were found in 70 genes. Matched tumor/germline comparison in 27 cases revealed that 57% were in fact germline variants. In 20 of these 27 cases, 58 somatic variants in 33 different genes were identified, the most frequent being PIK3CA (5 cases), APC and ATM (4 cases), and KRAS, NF1, LRP1B and BRCA1 (3 cases). Many of the somatic gene variants affected PI3K, MAPK/ERK, WNT and DNA repair signaling pathways, although not in a mutually exclusive manner. None of the alterations were related to histological ITAC subtype, tumor stage or survival. Our data showed that thorough interpretation of somatic mutations requires sequencing analysis of the corresponding germline DNA. Potentially actionable somatic mutations were found in 20 of 27 cases, 8 of which being biomarkers of FDA-approved targeted therapies. Our data implicate new possibilities for personalized treatment of ITAC patients.

scholarly journals Carrier molecules and extraction of circulating tumor DNA for next generation sequencing in colorectal cancer

2016 ◽  
Vol 59 (2) ◽  
pp. 54-58 ◽  
Author(s):  
Martin Beránek ◽  
Igor Sirák ◽  
Milan Vošmik ◽  
Jiří Petera ◽  
Monika Drastíková ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Next Generation Sequencing ◽  
Somatic Mutations ◽  
Circulating Tumor Dna ◽  
Next Generation ◽  
Sequencing Data ◽  
Polyadenylic Acid ◽  
Sperm Dna ◽  
Tumor Dna ◽  
Generation Sequencing

The aims of the study were:i) to compare circulating tumor DNA (ctDNA) yields obtained by different manual extraction procedures,ii) to evaluate the addition of various carrier molecules into the plasma to improve ctDNA extraction recovery, andiii) to use next generation sequencing (NGS) technology to analyzeKRAS,BRAF, andNRASsomatic mutations in ctDNA from patients with metastatic colorectal cancer. Venous blood was obtained from patients who suffered from metastatic colorectal carcinoma. For plasma ctDNA extraction, the following carriers were tested: carrier RNA, polyadenylic acid, glycogen, linear acrylamide, yeast tRNA, salmon sperm DNA, and herring sperm DNA. Each extract was characterized by quantitative real-time PCR and next generation sequencing. The addition of polyadenylic acid had a significant positive effect on the amount of ctDNA eluted. The sequencing data revealed five cases of ctDNA mutated inKRASand one patient with aBRAFmutation. An agreement of 86% was found between tumor tissues and ctDNA. Testing somatic mutations in ctDNA seems to be a promising tool to monitor dynamically changing genotypes of tumor cells circulating in the body. The optimized process of ctDNA extraction should help to obtain more reliable sequencing data in patients with metastatic colorectal cancer.

scholarly journals SequencErr: measuring and suppressing sequencer errors in next-generation sequencing data

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Eric M. Davis ◽  
Yu Sun ◽  
Yanling Liu ◽  
Pandurang Kolekar ◽  
Ying Shao ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Error Rate ◽  
Control Method ◽  
Error Rates ◽  
Computational Method ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
Flow Cells ◽  
Generation Sequencing

Abstract Background There is currently no method to precisely measure the errors that occur in the sequencing instrument/sequencer, which is critical for next-generation sequencing applications aimed at discovering the genetic makeup of heterogeneous cellular populations. Results We propose a novel computational method, SequencErr, to address this challenge by measuring the base correspondence between overlapping regions in forward and reverse reads. An analysis of 3777 public datasets from 75 research institutions in 18 countries revealed the sequencer error rate to be ~ 10 per million (pm) and 1.4% of sequencers and 2.7% of flow cells have error rates > 100 pm. At the flow cell level, error rates are elevated in the bottom surfaces and > 90% of HiSeq and NovaSeq flow cells have at least one outlier error-prone tile. By sequencing a common DNA library on different sequencers, we demonstrate that sequencers with high error rates have reduced overall sequencing accuracy, and removal of outlier error-prone tiles improves sequencing accuracy. We demonstrate that SequencErr can reveal novel insights relative to the popular quality control method FastQC and achieve a 10-fold lower error rate than popular error correction methods including Lighter and Musket. Conclusions Our study reveals novel insights into the nature of DNA sequencing errors incurred on DNA sequencers. Our method can be used to assess, calibrate, and monitor sequencer accuracy, and to computationally suppress sequencer errors in existing datasets.

scholarly journals Profiling of Somatic Mutations in Phaeochromocytoma and Paraganglioma by Targeted Next Generation Sequencing Analysis

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Andrea Luchetti ◽  
Diana Walsh ◽  
Fay Rodger ◽  
Graeme Clark ◽  
Tom Martin ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Somatic Mutations ◽  
Human Cancer ◽  
Sequencing Analysis ◽  
Cancer Genes ◽  
Next Generation ◽  
Targeted Next Generation Sequencing ◽  
Next Generation Sequencing Analysis ◽  
Sequencing Strategy ◽  
Generation Sequencing

At least 12 genes (FH, HIF2A, MAX, NF1, RET, SDHA, SDHB, SDHC, SDHD, SDHAF2, TMEM127,andVHL) have been implicated in inherited predisposition to phaeochromocytoma (PCC), paraganglioma (PGL), or head and neck paraganglioma (HNPGL) and a germline mutation may be detected in more than 30% of cases. Knowledge of somatic mutations contributing to PCC/PGL/HNPGL pathogenesis has received less attention though mutations inHRAS, HIF2A, NF1, RET,andVHLhave been reported. To further elucidate the role of somatic mutation in PCC/PGL/HNPGL tumourigenesis, we employed a next generation sequencing strategy to analyse “mutation hotspots” in 50 human cancer genes. Mutations were identified forHRAS(c.37G>C; p.G13R and c.182A>G; p.Q61R) in 7.1% (6/85); forBRAF(c.1799T>A; p.V600E) in 1.2% (1/85) of tumours; and forTP53(c.1010G>A; p.R337H) in 2.35% (2/85) of cases. Twenty-one tumours harboured mutations in inherited PCC/PGL/HNPGL genes and noHRAS, BRAF, orTP53mutations occurred in this group. Combining our data with previous reports ofHRASmutations in PCC/PGL we find that the mean frequency ofHRAS/BRAFmutations in sporadic PCC/PGL is 8.9% (24/269) and in PCC/PGL with an inherited gene mutation 0% (0/148) suggesting thatHRAS/BRAFmutations and inherited PCC/PGL genes mutations might be mutually exclusive. We report the first evidence forBRAFmutations in the pathogenesis of PCC/PGL/HNPGL.

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