Effects of Improved DNA Integrity by Punch From Tissue Blocks as Compared to Pinpoint Extraction From Unstained Slides on Next-Generation Sequencing Quality Metrics

2019 ◽  
Vol 152 (1) ◽  
pp. 27-35 ◽  
Author(s):  
Diana Morlote ◽  
Karen M Janowski ◽  
Rance C Siniard ◽  
Rong Jun Guo ◽  
Thomas Winokur ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Quality Metrics ◽  
Dna Integrity ◽  
Next Generation ◽  
Sequencing Quality ◽  
Generation Sequencing

scholarly journals Impact of DNA integrity on the success rate of tissue‐based next‐generation sequencing: Lessons from nationwide cancer genome screening project SCRUM‐Japan GI‐SCREEN

2020 ◽  
Vol 70 (12) ◽  
pp. 932-942
Author(s):  
Takeshi Kuwata ◽  
Masashi Wakabayashi ◽  
Yutaka Hatanaka ◽  
Eiichi Morii ◽  
Yoshinao Oda ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Success Rate ◽  
Cancer Genome ◽  
Dna Integrity ◽  
Next Generation ◽  
Genome Screening ◽  
Generation Sequencing

scholarly journals Multiplex Picoliter-Droplet Digital PCR for Quantitative Assessment of DNA Integrity in Clinical Samples

2013 ◽  
Vol 59 (5) ◽  
pp. 815-823 ◽  
Author(s):  
Audrey Didelot ◽  
Steve K Kotsopoulos ◽  
Audrey Lupo ◽  
Deniz Pekin ◽  
Xinyu Li ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Simultaneous Detection ◽  
Digital Pcr ◽  
Clinical Samples ◽  
Dna Integrity ◽  
Next Generation ◽  
Sequencing Data ◽  
Integrity Test ◽  
Picoliter Droplet ◽  
Generation Sequencing

BACKGROUND Assessment of DNA integrity and quantity remains a bottleneck for high-throughput molecular genotyping technologies, including next-generation sequencing. In particular, DNA extracted from paraffin-embedded tissues, a major potential source of tumor DNA, varies widely in quality, leading to unpredictable sequencing data. We describe a picoliter droplet–based digital PCR method that enables simultaneous detection of DNA integrity and the quantity of amplifiable DNA. METHODS Using a multiplex assay, we detected 4 different target lengths (78, 159, 197, and 550 bp). Assays were validated with human genomic DNA fragmented to sizes of 170 bp to 3000 bp. The technique was validated with DNA quantities as low as 1 ng. We evaluated 12 DNA samples extracted from paraffin-embedded lung adenocarcinoma tissues. RESULTS One sample contained no amplifiable DNA. The fractions of amplifiable DNA for the 11 other samples were between 0.05% and 10.1% for 78-bp fragments and ≤1% for longer fragments. Four samples were chosen for enrichment and next-generation sequencing. The quality of the sequencing data was in agreement with the results of the DNA-integrity test. Specifically, DNA with low integrity yielded sequencing results with lower levels of coverage and uniformity and had higher levels of false-positive variants. CONCLUSIONS The development of DNA-quality assays will enable researchers to downselect samples or process more DNA to achieve reliable genome sequencing with the highest possible efficiency of cost and effort, as well as minimize the waste of precious samples.

scholarly journals A Fun Introductory Command Line Lesson: Next Generation Sequencing Quality Analysis with Emoji!

CourseSource ◽  
2021 ◽  
Vol 8 ◽  
Author(s):  
Rachael M. St. Jacques ◽  
William M. Maza ◽  
Sabrina D. Robertson ◽  
Andrew Lonsdale ◽  
Caylin S. Murray ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Quality Analysis ◽  
Command Line ◽  
Next Generation ◽  
Sequencing Quality ◽  
Generation Sequencing

scholarly journals A straightforward assay to evaluate DNA integrity and optimize next-generation sequencing for clinical diagnosis in oncology

2017 ◽  
Vol 103 (3) ◽  
pp. 294-299 ◽  
Author(s):  
Fabiana Bettoni ◽  
Fernanda Christtanini Koyama ◽  
Paola de Avelar Carpinetti ◽  
Pedro Alexandre Favoretto Galante ◽  
Anamaria Aranha Camargo ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Clinical Diagnosis ◽  
Dna Integrity ◽  
Next Generation ◽  
Generation Sequencing

scholarly journals Next-generation sequencing of cytologic preparations: An analysis of quality metrics

2017 ◽  
Vol 125 (10) ◽  
pp. 786-794 ◽  
Author(s):  
David H. Hwang ◽  
Elizabeth P. Garcia ◽  
Matthew D. Ducar ◽  
Edmund S. Cibas ◽  
Lynette M. Sholl
Keyword(s):  
Next Generation Sequencing ◽  
Quality Metrics ◽  
Next Generation ◽  
Generation Sequencing

scholarly journals Compression of next-generation sequencing quality scores using memetic algorithm

2014 ◽  
Vol 15 (Suppl 15) ◽  
pp. S10 ◽  
Author(s):  
Jiarui Zhou ◽  
Zhen Ji ◽  
Zexuan Zhu ◽  
Shan He
Keyword(s):  
Next Generation Sequencing ◽  
Memetic Algorithm ◽  
Next Generation ◽  
Sequencing Quality ◽  
Generation Sequencing

scholarly journals ForestQC: quality control on genetic variants from next-generation sequencing data using random forest

2018 ◽  
Author(s):  
Jiajin Li ◽  
Brandon Jew ◽  
Lingyu Zhan ◽  
Sungoo Hwang ◽  
Giovanni Coppola ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quality Control ◽  
Next Generation Sequencing ◽  
Genetic Variants ◽  
Next Generation ◽  
Sequencing Data ◽  
Machine Learning Approach ◽  
Sequencing Quality ◽  
Generation Sequencing ◽  
Filtering Approach

ABSTRACTNext-generation sequencing technology (NGS) enables discovery of nearly all genetic variants present in a genome. A subset of these variants, however, may have poor sequencing quality due to limitations in sequencing technology or in variant calling algorithms. In genetic studies that analyze a large number of sequenced individuals, it is critical to detect and remove those variants with poor quality as they may cause spurious findings. In this paper, we present a statistical approach for performing quality control on variants identified from NGS data by combining a traditional filtering approach and a machine learning approach. Our method uses information on sequencing quality such as sequencing depth, genotyping quality, and GC contents to predict whether a certain variant is likely to contain errors. To evaluate our method, we applied it to two whole-genome sequencing datasets where one dataset consists of related individuals from families while the other consists of unrelated individuals. Results indicate that our method outperforms widely used methods for performing quality control on variants such as VQSR of GATK by considerably improving the quality of variants to be included in the analysis. Our approach is also very efficient, and hence can be applied to large sequencing datasets. We conclude that combining a machine learning algorithm trained with sequencing quality information and the filtering approach is an effective approach to perform quality control on genetic variants from sequencing data.Author SummaryGenetic disorders can be caused by many types of genetic mutations, including common and rare single nucleotide variants, structural variants, insertions and deletions. Nowadays, next generation sequencing (NGS) technology allows us to identify various genetic variants that are associated with diseases. However, variants detected by NGS might have poor sequencing quality due to biases and errors in sequencing technologies and analysis tools. Therefore, it is critical to remove variants with low quality, which could cause spurious findings in follow-up analyses. Previously, people applied either hard filters or machine learning models for variant quality control (QC), which failed to filter out those variants accurately. Here, we developed a statistical tool, ForestQC, for variant QC by combining a filtering approach and a machine learning approach. We applied ForestQC to one family-based whole genome sequencing (WGS) dataset and one general case-control WGS dataset, to evaluate our method. Results show that ForestQC outperforms widely used methods for variant QC by considerably improving the quality of variants. Also, ForestQC is very efficient and scalable to large-scale sequencing datasets. Our study indicates that combining filtering approaches and machine learning approaches enables effective variant QC.

scholarly journals A Pilot Proficiency Testing Study for Assessing Cancer Gene Panel using Patient Samples and Next-generation Sequencing in Japan

2021 ◽  
Author(s):  
Masato Maekawa ◽  
Terumi Taniguchi ◽  
Kazuto Nishio ◽  
Kazuko Sakai ◽  
Kazuyuki Matsushita ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Proficiency Testing ◽  
Dna Integrity ◽  
Cancer Tissue ◽  
Sufficient Information ◽  
Precision Oncology ◽  
Cancer Gene ◽  
Next Generation ◽  
Advantages And Disadvantages ◽  
Generation Sequencing

Abstract To implement precision oncology, analytical validity as well as clinical validity and utility are important. However, proficiency testing (PT) to assess validity has not yet been systematically performed in Japan. To investigate the quality of next-generation sequencing (NGS) platforms and cancer genome testing prevalent in laboratories, we performed pilot PT using patient samples. We prepared 5 samples from patients with lung or colorectal cancer, extracted genomic DNA from the cancer tissue and peripheral blood, and distributed these to 15 laboratories. Most participating laboratories successfully identified the pathogenic variants, except for two closely located KRAS variants, and 25-bp delins in the EGFR exon 19, not identified by the in vitro diagnostics testing. Conversely, the EGFR L858R variant was successfully identified, and the allele frequency was similar for all the laboratories. A high DNA integrity number led to excellent depth and reliable NGS results. All NGS platforms and bioinformatics pipelines have advantages and disadvantages. We propose the use of a PT program using patient samples to ascertain the quality status of cancer gene testing in laboratories and to ensure that laboratories have sufficient information to develop advancements in precision medicine for cancer.

Sign in / Sign up

Export Citation Format

Share Document