scholarly journals High-precision and cost-efficient sequencing for real-time COVID-19 surveillance

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sung Yong Park ◽  
Gina Faraci ◽  
Pamela M. Ward ◽  
Jane F. Emerson ◽  
Ha Youn Lee
Keyword(s):  
Los Angeles ◽  
Whole Genome Sequencing ◽  
Real Time ◽  
Genome Sequencing ◽  
High Precision ◽  
High Throughput Sequencing ◽  
Whole Genome ◽  
Sequencing Data ◽  
Public Health Response ◽  
Cost Efficient

AbstractCOVID-19 global cases have climbed to more than 33 million, with over a million total deaths, as of September, 2020. Real-time massive SARS-CoV-2 whole genome sequencing is key to tracking chains of transmission and estimating the origin of disease outbreaks. Yet no methods have simultaneously achieved high precision, simple workflow, and low cost. We developed a high-precision, cost-efficient SARS-CoV-2 whole genome sequencing platform for COVID-19 genomic surveillance, CorvGenSurv (Coronavirus Genomic Surveillance). CorvGenSurv directly amplified viral RNA from COVID-19 patients’ Nasopharyngeal/Oropharyngeal (NP/OP) swab specimens and sequenced the SARS-CoV-2 whole genome in three segments by long-read, high-throughput sequencing. Sequencing of the whole genome in three segments significantly reduced sequencing data waste, thereby preventing dropouts in genome coverage. We validated the precision of our pipeline by both control genomic RNA sequencing and Sanger sequencing. We produced near full-length whole genome sequences from individuals who were COVID-19 test positive during April to June 2020 in Los Angeles County, California, USA. These sequences were highly diverse in the G clade with nine novel amino acid mutations including NSP12-M755I and ORF8-V117F. With its readily adaptable design, CorvGenSurv grants wide access to genomic surveillance, permitting immediate public health response to sudden threats.

scholarly journals Identification of Medium-Sized Copy Number Alterations in Whole-Genome Sequencing

2014 ◽  
Vol 13s3 ◽  
pp. CIN.S14023
Author(s):  
Hatice Gulcin Ozer ◽  
Aisulu Usubalieva ◽  
Adrienne Dorrance ◽  
Ayse Selen Yilmaz ◽  
Michael Caligiuri ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Copy Number ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Copy Number Alterations ◽  
Sequencing Data ◽  
Entire Genome ◽  
Multiple Challenges ◽  
Cost Efficient

The genome-wide discoveries such as detection of copy number alterations (CNA) from high-throughput whole-genome sequencing data enabled new developments in personalized medicine. The CNAs have been reported to be associated with various diseases and cancers including acute myeloid leukemia. However, there are multiple challenges to the use of current CNA detection tools that lead to high false-positive rates and thus impede widespread use of such tools in cancer research. In this paper, we discuss these issues and propose possible solutions. First, since the entire genome cannot be mapped due to some regions lacking sequence uniqueness, current methods cannot be appropriately adjusted to handle these regions in the analyses. Thus, detection of medium-sized CNAs is also being directly affected by these mappability problems. The requirement for matching control samples is also an important limitation because acquiring matching controls might not be possible or might not be cost efficient. Here we present an approach that addresses these issues and detects medium-sized CNAs in cancer genomes by (1) masking unmappable regions during the initial CNA detection phase, (2) using pool of a few normal samples as control, and (3) employing median filtering to adjust CNA ratios to its surrounding coverage and eliminate false positives.

scholarly journals Whole genome sequencing reveals high differentiation, low levels of genetic diversity and short runs of homozygosity among Swedish wels catfish

Heredity ◽  
2021 ◽  
Author(s):  
Axel Jensen ◽  
Mette Lillie ◽  
Kristofer Bergström ◽  
Per Larsson ◽  
Jacob Höglund
Keyword(s):  
Genetic Diversity ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Whole Genome Sequencing Data ◽  
Peripheral Populations ◽  
Whole Genome ◽  
Runs Of Homozygosity ◽  
Sequencing Data ◽  
Isolated Populations ◽  
Native Populations

AbstractThe use of genetic markers in the context of conservation is largely being outcompeted by whole-genome data. Comparative studies between the two are sparse, and the knowledge about potential effects of this methodology shift is limited. Here, we used whole-genome sequencing data to assess the genetic status of peripheral populations of the wels catfish (Silurus glanis), and discuss the results in light of a recent microsatellite study of the same populations. The Swedish populations of the wels catfish have suffered from severe declines during the last centuries and persists in only a few isolated water systems. Fragmented populations generally are at greater risk of extinction, for example due to loss of genetic diversity, and may thus require conservation actions. We sequenced individuals from the three remaining native populations (Båven, Emån, and Möckeln) and one reintroduced population of admixed origin (Helge å), and found that genetic diversity was highest in Emån but low overall, with strong differentiation among the populations. No signature of recent inbreeding was found, but a considerable number of short runs of homozygosity were present in all populations, likely linked to historically small population sizes and bottleneck events. Genetic substructure within any of the native populations was at best weak. Individuals from the admixed population Helge å shared most genetic ancestry with the Båven population (72%). Our results are largely in agreement with the microsatellite study, and stresses the need to protect these isolated populations at the northern edge of the distribution of the species.

scholarly journals Estimating sequencing error rates using families

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Kelley Paskov ◽  
Jae-Yoon Jung ◽  
Brianna Chrisman ◽  
Nate T. Stockham ◽  
Peter Washington ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Exome Sequencing ◽  
Genome Sequencing ◽  
Variant Calling ◽  
Error Rates ◽  
Sequencing Error ◽  
Whole Genome ◽  
Sequencing Data ◽  
Sequencing Platform ◽  
Whole Exome

Abstract Background As next-generation sequencing technologies make their way into the clinic, knowledge of their error rates is essential if they are to be used to guide patient care. However, sequencing platforms and variant-calling pipelines are continuously evolving, making it difficult to accurately quantify error rates for the particular combination of assay and software parameters used on each sample. Family data provide a unique opportunity for estimating sequencing error rates since it allows us to observe a fraction of sequencing errors as Mendelian errors in the family, which we can then use to produce genome-wide error estimates for each sample. Results We introduce a method that uses Mendelian errors in sequencing data to make highly granular per-sample estimates of precision and recall for any set of variant calls, regardless of sequencing platform or calling methodology. We validate the accuracy of our estimates using monozygotic twins, and we use a set of monozygotic quadruplets to show that our predictions closely match the consensus method. We demonstrate our method’s versatility by estimating sequencing error rates for whole genome sequencing, whole exome sequencing, and microarray datasets, and we highlight its sensitivity by quantifying performance increases between different versions of the GATK variant-calling pipeline. We then use our method to demonstrate that: 1) Sequencing error rates between samples in the same dataset can vary by over an order of magnitude. 2) Variant calling performance decreases substantially in low-complexity regions of the genome. 3) Variant calling performance in whole exome sequencing data decreases with distance from the nearest target region. 4) Variant calls from lymphoblastoid cell lines can be as accurate as those from whole blood. 5) Whole-genome sequencing can attain microarray-level precision and recall at disease-associated SNV sites. Conclusion Genotype datasets from families are powerful resources that can be used to make fine-grained estimates of sequencing error for any sequencing platform and variant-calling methodology.

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