scholarly journals QuAdTrim: Overcoming computational bottlenecks in sequence quality control

2019 ◽  
Author(s):  
Andrew J. Robinson ◽  
Elizabeth M. Ross
Keyword(s):  
Quality Control ◽  
High Throughput Sequencing ◽  
Source Code ◽  
Poor Quality ◽  
Data Quality Control ◽  
Common Error ◽  
Control Programs ◽  
Sequence Quality ◽  
Dot Com ◽  
Adapter Trimming

AbstractWith the recent torrent of high throughput sequencing (HTS) data the necessity for highly efficient algorithms for common tasks is paramount. One task for which the basis for all further analysis of HTS data is initial data quality control, that is, the removal or trimming of poor quality reads from the dataset. Here we present QuAdTrim, a quality control and adapter trimming algorithm for HTS data that is up to 57 times faster and uses less than 0.06% of the memory of other commonly used HTS quality control programs. QuAdTrim will reduce the time and memory required for quality control of HTS data, and in doing, will reduce the computational demands of a fundamental step in HTS data analysis. Additionally, QuAdTrim impliments the removal of homopolymer Gs from the 3’ end of sequence reads, a common error generated on the NovaSeq, NextSeq and iSeq100 platforms.Availability and ImplementationThe source code is freely available on bitbucket under a BSD licence, see COPYING file for details: https://bitbucket.org/arobinson/quadtrimContactAndrew Robinson andrewjrobinson at gmail dot com

scholarly journals fastp: an ultra-fast all-in-one FASTQ preprocessor

2018 ◽  
Author(s):  
Shifu Chen ◽  
Yanqing Zhou ◽  
Yaru Chen ◽  
Jia Gu
Keyword(s):  
Quality Control ◽  
Open Source ◽  
Programming Languages ◽  
Source Code ◽  
Data Filtering ◽  
Quality Filtering ◽  
Downstream Analysis ◽  
High Level ◽  
Unique Molecular Identifier ◽  
Adapter Trimming

AbstractMotivationQuality control and preprocessing of FASTQ files are essential to providing clean data for downstream analysis. Traditionally, a different tool is used for each operation, such as quality control, adapter trimming, and quality filtering. These tools are often insufficiently fast as most are developed using high-level programming languages (e.g., Python and Java) and provide limited multi-threading support. Reading and loading data multiple times also renders preprocessing slow and I/O inefficient.ResultsWe developed fastp as an ultra-fast FASTQ preprocessor with useful quality control and data-filtering features. It can perform quality control, adapter trimming, quality filtering, per-read quality cutting, and many other operations with a single scan of the FASTQ data. It also supports unique molecular identifier preprocessing, poly tail trimming, output splitting, and base correction for paired-end data. It can automatically detect adapters for single-end and paired-end FASTQ data. This tool is developed in C++ and has multi-threading support. Based on our evaluation, fastp is 2–5 times faster than other FASTQ preprocessing tools such as Trimmomatic or Cutadapt despite performing far more operations than similar tools.Availability and ImplementationThe open-source code and corresponding instructions are available at https://github.com/OpenGene/[email protected]

ANALISIS FAKTOR DAN USULAN PERBAIKAN KUALITAS HASIL CETAK KORAN INDUSTRI GRAFIKA

2019 ◽  
Vol 3 (2) ◽  
Author(s):  
Yusnia Sinambela
Keyword(s):  
Quality Control ◽  
Publishing Industry ◽  
Production Quality ◽  
Fish Bone ◽  
Poor Quality ◽  
Quality Analysis ◽  
Print Quality ◽  
Large Defect ◽  
Production Quality Control ◽  
Control Programs

PT. X as a company engaged in the printing and publishing industry. In carrying out its business activities the company has implemented a production quality control system. Various quality control programs are carried out by the company so as to produce good products and in accordance with established quality standards. But in reality there are still products that are of poor quality. The damaged product is then rejected (separated from the product that falls into good criteria) and will be resold to other parties at a price lower than the general price. This is certainly a loss for the company because it results in a waste of production, especially if the damaged product exceeds the tolerance limit set by the company. Based on the results of the study obtained a large defect in printout Newspapers include blurred colors, not registers and cut. By using a quality analysis using a Fish Bone Diagram, the factors that generally affect print quality are human, material, method, environment and machine

scholarly journals Automated Data Quality Control in FDOPA brain PET Imaging using Deep Learning

2021 ◽  
pp. 106239
Author(s):  
Antonella D. Pontoriero ◽  
Giovanna Nordio ◽  
Rubaida Easmin ◽  
Alessio Giacomel ◽  
Barbara Santangelo ◽  
...  
Keyword(s):  
Quality Control ◽  
Deep Learning ◽  
Data Quality ◽  
Pet Imaging ◽  
Data Quality Control ◽  
Brain Pet

scholarly journals An interactive graphical system for XBT data quality control and visualization

2001 ◽  
Vol 27 (7) ◽  
pp. 867-876 ◽  
Author(s):  
Pankajakshan Thadathil ◽  
Aravind K Ghosh ◽  
J.S Sarupria ◽  
V.V Gopalakrishna
Keyword(s):  
Quality Control ◽  
Data Quality ◽  
Data Quality Control ◽  
Graphical System

Implementation of Marine Environment Monitoring Data Quality Control System

2014 ◽  
Vol 926-930 ◽  
pp. 4254-4257 ◽  
Author(s):  
Jin Xu ◽  
Da Tao Yu ◽  
Zhong Jie Yuan ◽  
Bo Li ◽  
Zi Zhou Xu
Keyword(s):  
Quality Control ◽  
Control System ◽  
Data Quality ◽  
Marine Environment ◽  
Quality Control System ◽  
Monitoring Data ◽  
Environment Monitoring ◽  
Data Quality Control ◽  
Labor Costs ◽  
Marine Environment Monitoring

Traditional artificial perception quality control methods of marine environment monitoring data have many disadvantages, including high labor costs and mistakes of data review. Based on GIS spatial analysis technology, Marine Environment Monitoring Data Quality Control System is established according to the Bohai Sea monitoring regulation. In the practical application process, it plays the role of improving efficiency of quality control, saving the manpower and financial resources. It also provides an important guarantee for the comprehensive analysis and management of marine environment data.

Data quality control in a multi-insitutional cancer trial

1980 ◽  
Vol 1 (2) ◽  
pp. 171-172
Author(s):  
M.M. Koretz ◽  
M. Kohler ◽  
E. McGuigan ◽  
J.F. Hannigan ◽  
B.W. Brown
Keyword(s):  
Quality Control ◽  
Data Quality ◽  
Data Quality Control ◽  
Cancer Trial
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