scholarly journals Alcov: Estimating Variant of Concern Abundance from SARS-CoV-2 Wastewater Sequencing Data

2021 ◽  
Author(s):  
Isaac Ellmen ◽  
Michael D.J. Lynch ◽  
Delaney Nash ◽  
Jiujun Cheng ◽  
Jozef I. Nissimov ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Clinical Data ◽  
Relative Abundance ◽  
Community Level ◽  
Next Generation ◽  
Sequencing Data ◽  
Complete Genomes ◽  
Relative Abundances ◽  
Wastewater Samples ◽  
Generation Sequencing

Detection of SARS-CoV-2 in wastewater is an important strategy for community level surveillance. Variants of concern (VOCs) can be detected in the wastewater samples using next generation sequencing, however it can be challenging to determine the relative abundance of different VOCs since the reads cannot be assembled into complete genomes. Here, we present Alcov (abundance learning of SARS-CoV-2 variants), a tool that uses mutation frequencies in SARS-CoV-2 sequencing data to predict the distribution of VOC lineages in the sample. We used Alcov to predict the distributions of lineages from three wastewater samples which agreed well with clinical data. By predicting not just which VOCs are present, but their relative abundances in the population, Alcov extracts a more complete snapshot of the variants which are circulating in a community.

scholarly journals Next-Generation Sequencing–Based Cancer Panel Data Conversion Using International Standards to Implement a Clinical Next-Generation Sequencing Research System: Single-Institution Study

10.2196/14710 ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. e14710 ◽  
Author(s):  
Phillip Park ◽  
Soo-Yong Shin ◽  
Seog Yun Park ◽  
Jeonghee Yun ◽  
Chulmin Shin ◽  
...  
Keyword(s):  
Clinical Practice ◽  
Next Generation Sequencing ◽  
Clinical Data ◽  
Genomic Data ◽  
International Standards ◽  
Research System ◽  
Next Generation ◽  
Sequencing Data ◽  
Clinical Sequencing ◽  
Generation Sequencing

Background The analytical capacity and speed of next-generation sequencing (NGS) technology have been improved. Many genetic variants associated with various diseases have been discovered using NGS. Therefore, applying NGS to clinical practice results in precision or personalized medicine. However, as clinical sequencing reports in electronic health records (EHRs) are not structured according to recommended standards, clinical decision support systems have not been fully utilized. In addition, integrating genomic data with clinical data for translational research remains a great challenge. Objective To apply international standards to clinical sequencing reports and to develop a clinical research information system to integrate standardized genomic data with clinical data. Methods We applied the recently published ISO/TS 20428 standard to 367 clinical sequencing reports generated by panel (91 genes) sequencing in EHRs and implemented a clinical NGS research system by extending the clinical data warehouse to integrate the necessary clinical data for each patient. We also developed a user interface with a clinical research portal and an NGS result viewer. Results A single clinical sequencing report with 28 items was restructured into four database tables and 49 entities. As a result, 367 patients’ clinical sequencing data were connected with clinical data in EHRs, such as diagnosis, surgery, and death information. This system can support the development of cohort or case-control datasets as well. Conclusions The standardized clinical sequencing data are not only for clinical practice and could be further applied to translational research.

scholarly journals Next-Generation Sequencing–Based Cancer Panel Data Conversion Using International Standards to Implement a Clinical Next-Generation Sequencing Research System: Single-Institution Study (Preprint)

2019 ◽  
Author(s):  
Phillip Park ◽  
Soo-Yong Shin ◽  
Seog Yun Park ◽  
Jeonghee Yun ◽  
Chulmin Shin ◽  
...  
Keyword(s):  
Clinical Practice ◽  
Next Generation Sequencing ◽  
Clinical Data ◽  
Genomic Data ◽  
International Standards ◽  
Research System ◽  
Next Generation ◽  
Sequencing Data ◽  
Clinical Sequencing ◽  
Generation Sequencing

BACKGROUND The analytical capacity and speed of next-generation sequencing (NGS) technology have been improved. Many genetic variants associated with various diseases have been discovered using NGS. Therefore, applying NGS to clinical practice results in precision or personalized medicine. However, as clinical sequencing reports in electronic health records (EHRs) are not structured according to recommended standards, clinical decision support systems have not been fully utilized. In addition, integrating genomic data with clinical data for translational research remains a great challenge. OBJECTIVE To apply international standards to clinical sequencing reports and to develop a clinical research information system to integrate standardized genomic data with clinical data. METHODS We applied the recently published ISO/TS 20428 standard to 367 clinical sequencing reports generated by panel (91 genes) sequencing in EHRs and implemented a clinical NGS research system by extending the clinical data warehouse to integrate the necessary clinical data for each patient. We also developed a user interface with a clinical research portal and an NGS result viewer. RESULTS A single clinical sequencing report with 28 items was restructured into four database tables and 49 entities. As a result, 367 patients’ clinical sequencing data were connected with clinical data in EHRs, such as diagnosis, surgery, and death information. This system can support the development of cohort or case-control datasets as well. CONCLUSIONS The standardized clinical sequencing data are not only for clinical practice and could be further applied to translational research.

scholarly journals NGSremix: A software tool for estimating pairwise relatedness between admixed individuals from next-generation sequencing data

2021 ◽  
Author(s):  
Anne Krogh Nøhr ◽  
Kristian Hanghøj ◽  
Genis Garcia Erill ◽  
Zilong Li ◽  
Ida Moltke ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Genetic Research ◽  
Likelihood Estimation ◽  
Software Tool ◽  
Estimation Methods ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
Ngs Data ◽  
Generation Sequencing

Abstract Estimation of relatedness between pairs of individuals is important in many genetic research areas. When estimating relatedness, it is important to account for admixture if this is present. However, the methods that can account for admixture are all based on genotype data as input, which is a problem for low-depth next-generation sequencing (NGS) data from which genotypes are called with high uncertainty. Here we present a software tool, NGSremix, for maximum likelihood estimation of relatedness between pairs of admixed individuals from low-depth NGS data, which takes the uncertainty of the genotypes into account via genotype likelihoods. Using both simulated and real NGS data for admixed individuals with an average depth of 4x or below we show that our method works well and clearly outperforms all the commonly used state-of-the-art relatedness estimation methods PLINK, KING, relateAdmix, and ngsRelate that all perform quite poorly. Hence, NGSremix is a useful new tool for estimating relatedness in admixed populations from low-depth NGS data. NGSremix is implemented in C/C ++ in a multi-threaded software and is freely available on Github https://github.com/KHanghoj/NGSremix.

scholarly journals recoup: flexible and versatile signal visualization from next generation sequencing

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Panagiotis Moulos
Keyword(s):  
Next Generation Sequencing ◽  
Next Generation Sequencing Data ◽  
Special Focus ◽  
Next Generation ◽  
Sequencing Data ◽  
User Friendliness ◽  
Computational Environment ◽  
Level Data ◽  
Data Signal ◽  
Generation Sequencing

Abstract Background The relentless continuing emergence of new genomic sequencing protocols and the resulting generation of ever larger datasets continue to challenge the meaningful summarization and visualization of the underlying signal generated to answer important qualitative and quantitative biological questions. As a result, the need for novel software able to reliably produce quick, comprehensive, and easily repeatable genomic signal visualizations in a user-friendly manner is rapidly re-emerging. Results recoup is a Bioconductor package for quick, flexible, versatile, and accurate visualization of genomic coverage profiles generated from Next Generation Sequencing data. Coupled with a database of precalculated genomic regions for multiple organisms, recoup offers processing mechanisms for quick, efficient, and multi-level data interrogation with minimal effort, while at the same time creating publication-quality visualizations. Special focus is given on plot reusability, reproducibility, and real-time exploration and formatting options, operations rarely supported in similar visualization tools in a profound way. recoup was assessed using several qualitative user metrics and found to balance the tradeoff between important package features, including speed, visualization quality, overall friendliness, and the reusability of the results with minimal additional calculations. Conclusion While some existing solutions for the comprehensive visualization of NGS data signal offer satisfying results, they are often compromised regarding issues such as effortless tracking of processing and preparation steps under a common computational environment, visualization quality and user friendliness. recoup is a unique package presenting a balanced tradeoff for a combination of assessment criteria while remaining fast and friendly.

scholarly journals BIGpre: A Quality Assessment Package for Next-Generation Sequencing Data

2011 ◽  
Vol 9 (6) ◽  
pp. 238-244 ◽  
Author(s):  
Tongwu Zhang ◽  
Yingfeng Luo ◽  
Kan Liu ◽  
Linlin Pan ◽  
Bing Zhang ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Quality Assessment ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
Generation Sequencing
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