scholarly journals SOLQC: Synthetic Oligo Library Quality Control tool

2020 ◽  
Author(s):  
Omer Sabary ◽  
Yoav Orlev ◽  
Roy Shafir ◽  
Leon Anavy ◽  
Eitan Yaakobi ◽  
...  
Keyword(s):  
Quality Control ◽  
Complexity Analysis ◽  
Error Rates ◽  
Comprehensive Analysis ◽  
Free Software ◽  
Supplementary Information ◽  
Analysis Tool ◽  
Commercial Use ◽  
Quality Control Tool ◽  
Potential Benefits

Abstract Motivation Recent years have seen a growing number and an expanding scope of studies using synthetic oligo libraries for a range of applications in synthetic biology. As experiments are growing by numbers and complexity, analysis tools can facilitate quality control and support better assessment and inference. Results We present a novel analysis tool, called SOLQC, which enables fast and comprehensive analysis of synthetic oligo libraries, based on NGS analysis performed by the user. SOLQC provides statistical information such as the distribution of variant representation, different error rates and their dependence on sequence or library properties. SOLQC produces graphical reports from the analysis, in a flexible format. We demonstrate SOLQC by analyzing literature libraries. We also discuss the potential benefits and relevance of the different components of the analysis. Availability and implementation SOLQC is a free software for non-commercial use, available at https://app.gitbook.com/@yoav-orlev/s/solqc/. For commercial use please contact the authors. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals SOLQC : Synthetic Oligo Library Quality Control Tool

2019 ◽  
Author(s):  
Omer Sabary ◽  
Yoav Orlev ◽  
Roy Shafir ◽  
Leon Anavy ◽  
Eitan Yaakobi ◽  
...  
Keyword(s):  
Quality Control ◽  
Synthetic Biology ◽  
Complexity Analysis ◽  
Error Rates ◽  
Comprehensive Analysis ◽  
Statistical Information ◽  
Analysis Tool ◽  
Quality Control Tool ◽  
Potential Benefits ◽  
Control Tool

AbstractMotivationRecent years have seen a growing number and a broadening scope of studies using synthetic oligo libraries for a range of applications in synthetic biology. As experiments are growing by numbers and complexity, analysis tools can facilitate quality control and help in assessment and inference.ResultsWe present a novel analysis tool, called SOLQC, which enables fast and comprehensive analysis of synthetic oligo libraries, based on NGS analysis performed by the user. SOLQC provides statistical information such as the distribution of variant representation, different error rates and their dependence on sequence or library properties. SOLQC produces graphical descriptions of the analysis results. The results are reported in a flexible report format. We demonstrate SOLQC by analyzing literature libraries. We also discuss the potential benefits and relevance of the different components of the analysis.Availabilityhttps://app.gitbook.com/@yoav-orlev/s/solqc/

RabbitQC: high-speed scalable quality control for sequencing data

2020 ◽  
Author(s):  
Zekun Yin ◽  
Hao Zhang ◽  
Meiyang Liu ◽  
Wen Zhang ◽  
Honglei Song ◽  
...  
Keyword(s):  
Quality Control ◽  
High Speed ◽  
State Of The Art ◽  
Supplementary Information ◽  
Control Methods ◽  
Sequencing Data ◽  
Sequencing Technologies ◽  
Oxford Nanopore ◽  
Quality Control Tool ◽  
Computing Platforms

Abstract Motivation Modern sequencing technologies continue to revolutionize many areas of biology and medicine. Since the generated datasets are error-prone, downstream applications usually require quality control methods to pre-process FASTQ files. However, existing tools for this task are currently not able to fully exploit the capabilities of computing platforms leading to slow runtimes. Results We present RabbitQC, an extremely fast integrated quality control tool for FASTQ files, which can take full advantage of modern hardware. It includes a variety of operations and supports different sequencing technologies (Illumina, Oxford Nanopore and PacBio). RabbitQC achieves speedups between one and two orders-of-magnitude compared to other state-of-the-art tools. Availability and implementation C++ sources and binaries are available at https://github.com/ZekunYin/RabbitQC. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals SeeCiTe: a method to assess CNV calls from SNP arrays using trio data

2021 ◽  
Author(s):  
Ksenia Lavrichenko ◽  
Øyvind Helgeland ◽  
Pål R Njølstad ◽  
Inge Jonassen ◽  
Stefan Johansson
Keyword(s):  
Quality Control ◽  
Nuclear Family ◽  
Large Population ◽  
Copy Number Variants ◽  
Supplementary Information ◽  
Single Nucleotide ◽  
Specificity And Sensitivity ◽  
Quality Control Tool ◽  
The Common ◽  
Quality Categories

Abstract Motivation Single nucleotide polymorphism (SNP) genotyping arrays remain an attractive platform for assaying copy number variants (CNVs) in large population-wide cohorts. However, current tools for calling CNVs are still prone to extensive false positive calls when applied to biobank scale arrays. Moreover, there is a lack of methods exploiting cohorts with trios available (e.g. nuclear family) to assist in quality control and downstream analyses following the calling. Results We developed SeeCiTe (Seeing CNVs in Trios), a novel CNV-quality control tool that postprocesses output from current CNV-calling tools exploiting child-parent trio data to classify calls in quality categories and provide a set of visualizations for each putative CNV call in the offspring. We apply it to the Norwegian Mother, Father and Child Cohort Study (MoBa) and show that SeeCiTe improves the specificity and sensitivity compared to the common empiric filtering strategies. To our knowledge, it is the first tool that utilizes probe-level CNV data in trios (and singletons) to systematically highlight potential artifacts and visualize signal intensities in a streamlined fashion suitable for biobank scale studies. Availability and implementation The software is implemented in R with the source code freely available at https://github.com/aksenia/SeeCiTe Supplementary information Supplementary data are available at Bioinformatics online.

The Complexity Analysis Tool

1988 ◽  
Author(s):  
Paul E. Janusz
Keyword(s):  
Complexity Analysis ◽  
Analysis Tool

scholarly journals Range probing as a quality control tool for CBCT based synthetic CTs: in vivo application for head and neck cancer patients

2021 ◽  
Author(s):  
Carmen Seller Oria ◽  
Adrian Thummerer ◽  
Jeffrey Free ◽  
Johannes A. Langendijk ◽  
Stefan Both ◽  
...  
Keyword(s):  
Quality Control ◽  
Head And Neck Cancer ◽  
Head And Neck ◽  
Cancer Patients ◽  
Neck Cancer ◽  
Quality Control Tool ◽  
Control Tool

scholarly journals Applying Ancestry and Sex Computation as a Quality Control Tool in Targeted Next-Generation Sequencing

2016 ◽  
Vol 145 (3) ◽  
pp. 308-315 ◽  
Author(s):  
Patrick C. Mathias ◽  
Emily H. Turner ◽  
Sheena M. Scroggins ◽  
Stephen J. Salipante ◽  
Noah G. Hoffman ◽  
...  
Keyword(s):  
Quality Control ◽  
Next Generation Sequencing ◽  
Next Generation ◽  
Targeted Next Generation Sequencing ◽  
Quality Control Tool ◽  
Generation Sequencing ◽  
Control Tool

scholarly journals TriPOINT: a software tool to prioritize important genes in pathways and their non-coding regulators

2018 ◽  
Vol 35 (15) ◽  
pp. 2686-2689
Author(s):  
Asa Thibodeau ◽  
Dong-Guk Shin
Keyword(s):  
Gene Expression ◽  
Software Tool ◽  
Supplementary Information ◽  
Analysis Tool ◽  
Graph Representations ◽  
Expression Levels ◽  
Conducting Pathway ◽  
Pathway Analysis Tool ◽  
Pathway Analyses ◽  
Gene Expression Levels

Abstract Summary Current approaches for pathway analyses focus on representing gene expression levels on graph representations of pathways and conducting pathway enrichment among differentially expressed genes. However, gene expression levels by themselves do not reflect the overall picture as non-coding factors play an important role to regulate gene expression. To incorporate these non-coding factors into pathway analyses and to systematically prioritize genes in a pathway we introduce a new software: Triangulation of Perturbation Origins and Identification of Non-Coding Targets. Triangulation of Perturbation Origins and Identification of Non-Coding Targets is a pathway analysis tool, implemented in Java that identifies the significance of a gene under a condition (e.g. a disease phenotype) by studying graph representations of pathways, analyzing upstream and downstream gene interactions and integrating non-coding regions that may be regulating gene expression levels. Availability and implementation The TriPOINT open source software is freely available at https://github.uconn.edu/ajt06004/TriPOINT under the GPL v3.0 license. Supplementary information Supplementary data are available at Bioinformatics online.

Successful Application of Ground-Penetrating Radar for Quality Assurance-Quality Control of New Pavements

2003 ◽  
Vol 1861 (1) ◽  
pp. 86-97 ◽  
Author(s):  
Imad L. Al-Qadi ◽  
Samer Lahouar ◽  
Amara Loulizi
Keyword(s):  
Quality Control ◽  
Quality Assurance ◽  
Ground Penetrating Radar ◽  
Test Section ◽  
Base Layer ◽  
Quality Control Tool ◽  
Granular Base ◽  
The One ◽  
The Time Domain ◽  
Ground Penetrating

The successful application of ground-penetrating radar (GPR) as a quality assurance–quality control tool to measure the layer thicknesses of newly built pavement systems is described. A study was conducted on a newly built test section of Route 288 located near Richmond, Virginia. The test section is a three-lane, 370-m-long flexible pavement system composed of a granular base layer and three different hot-mix asphalt (HMA) lifts. GPR surveys were conducted on each lift of the HMA layers after they were constructed. To estimate the layer thicknesses, GPR data were analyzed by using simplified equations in the time domain. The accuracies of the GPR system results were checked by comparing the thicknesses predicted with the GPR to the thicknesses measured directly from a large number of cores taken from the different HMA lifts. This comparison revealed a mean thickness error of 2.9% for HMA layers ranging in thickness from 100 mm (4 in.) to 250 mm (10 in.). This error is similar to the one obtained from the direct measurement of core thickness.

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