scholarly journals Highly Efficient and Comprehensive Identification of Ethyl Methanesulfonate-Induced Mutations in Nicotiana tabacum L. by Whole-Genome and Whole-Exome Sequencing

2021 ◽  
Vol 12 ◽  
Author(s):  
Hisashi Udagawa ◽  
Hiroyuki Ichida ◽  
Takanori Takeuchi ◽  
Tomoko Abe ◽  
Yoshimitsu Takakura
Keyword(s):  
Nicotiana Tabacum ◽  
Exome Sequencing ◽  
Whole Exome Sequencing ◽  
Ethyl Methanesulfonate ◽  
Set Covering ◽  
Whole Genome ◽  
Sequencing Data ◽  
Induced Mutations ◽  
Highly Efficient ◽  
Whole Exome

Tobacco (Nicotiana tabacum L.) is a complex allotetraploid species with a large 4.5-Gb genome that carries duplicated gene copies. In this study, we describe the development of a whole-exome sequencing (WES) procedure in tobacco and its application to characterize a test population of ethyl methanesulfonate (EMS)-induced mutations. A probe set covering 50.3-Mb protein coding regions was designed from a reference tobacco genome. The EMS-induced mutations in 19 individual M2 lines were analyzed using our mutation analysis pipeline optimized to minimize false positives/negatives. In the target regions, the on-target rate of WES was approximately 75%, and 61,146 mutations were detected in the 19 M2 lines. Most of the mutations (98.8%) were single nucleotide variants, and 95.6% of them were C/G to T/A transitions. The number of mutations detected in the target coding sequences by WES was 93.5% of the mutations detected by whole-genome sequencing (WGS). The amount of sequencing data necessary for efficient mutation detection was significantly lower in WES (11.2 Gb), which is only 6.2% of the required amount in WGS (180 Gb). Thus, WES was almost comparable to WGS in performance but is more cost effective. Therefore, the developed target exome sequencing, which could become a fundamental tool in high-throughput mutation identification, renders the genome-wide analysis of tobacco highly efficient.

scholarly journals BALSA: Integrated secondary analysis for whole-genome and whole-exome sequencing, accelerated by GPU

2014 ◽  
Author(s):  
Ruibang Luo ◽  
Yiu-Lun Wong ◽  
Wai-Chun Law ◽  
Lap-Kei Lee ◽  
Chi-Man Liu ◽  
...  
Keyword(s):  
Exome Sequencing ◽  
Whole Exome Sequencing ◽  
Secondary Analysis ◽  
Variant Calling ◽  
Statistical Testing ◽  
Next Generation Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
Accurate Analysis ◽  
Whole Exome

This paper reports an integrated solution, called BALSA, for the secondary analysis of next generation sequencing data; it exploits the computational power of GPU and an intricate memory management to give a fast and accurate analysis. From raw reads to variants (including SNPs and Indels), BALSA, using just a single computing node with a commodity GPU board, takes 5.5 hours to process 50-fold whole genome sequencing (~750 million 100bp paired-end reads), or just 25 minutes for 210-fold whole exome sequencing. BALSA’s speed is rooted at its parallel algorithms to effectively exploit a GPU to speed up processes like alignment, realignment and statistical testing. BALSA incorporates a 16-genotype model to support the calling of SNPs and Indels and achieves competitive variant calling accuracy and sensitivity when compared to the ensemble of six popular variant callers. BALSA also supports efficient identification of somatic SNVs and CNVs; experiments showed that BALSA recovers all the previously validated somatic SNVs and CNVs, and it is more sensitive for somatic Indel detection. BALSA outputs variants in VCF format. A pileup-like SNAPSHOT format, while maintaining the same fidelity as BAM in variant calling, enables efficient storage and indexing, and facilitates the App development of downstream analyses. BALSA is available at: http://sourceforge.net/p/balsa

scholarly journals BALSA: Integrated secondary analysis for whole-genome and whole-exome sequencing, accelerated by GPU

2014 ◽  
Author(s):  
Ruibang Luo ◽  
Yiu-Lun Wong ◽  
Wai-Chun Law ◽  
Lap-Kei Lee ◽  
Chi-Man Liu ◽  
...  
Keyword(s):  
Exome Sequencing ◽  
Whole Exome Sequencing ◽  
Secondary Analysis ◽  
Variant Calling ◽  
Statistical Testing ◽  
Next Generation Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
Accurate Analysis ◽  
Whole Exome

This paper reports an integrated solution, called BALSA, for the secondary analysis of next generation sequencing data; it exploits the computational power of GPU and an intricate memory management to give a fast and accurate analysis. From raw reads to variants (including SNPs and Indels), BALSA, using just a single computing node with a commodity GPU board, takes 5.5 hours to process 50-fold whole genome sequencing (~750 million 100bp paired-end reads), or just 25 minutes for 210-fold whole exome sequencing. BALSA’s speed is rooted at its parallel algorithms to effectively exploit a GPU to speed up processes like alignment, realignment and statistical testing. BALSA incorporates a 16-genotype model to support the calling of SNPs and Indels and achieves competitive variant calling accuracy and sensitivity when compared to the ensemble of six popular variant callers. BALSA also supports efficient identification of somatic SNVs and CNVs; experiments showed that BALSA recovers all the previously validated somatic SNVs and CNVs, and it is more sensitive for somatic Indel detection. BALSA outputs variants in VCF format. A pileup-like SNAPSHOT format, while maintaining the same fidelity as BAM in variant calling, enables efficient storage and indexing, and facilitates the App development of downstream analyses. BALSA is available at: http://sourceforge.net/p/balsa

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.

scholarly journals A Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing Data

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Jennifer D. Hintzsche ◽  
William A. Robinson ◽  
Aik Choon Tan
Keyword(s):  
Exome Sequencing ◽  
Whole Exome Sequencing ◽  
Sequencing Data ◽  
Disease Treatment ◽  
Computational Tools ◽  
Whole Exome ◽  
Data Production ◽  
Whole Exome Sequencing Data ◽  
Computationally Intensive ◽  
Generation Technology

Whole Exome Sequencing (WES) is the application of the next-generation technology to determine the variations in the exome and is becoming a standard approach in studying genetic variants in diseases. Understanding the exomes of individuals at single base resolution allows the identification of actionable mutations for disease treatment and management. WES technologies have shifted the bottleneck in experimental data production to computationally intensive informatics-based data analysis. Novel computational tools and methods have been developed to analyze and interpret WES data. Here, we review some of the current tools that are being used to analyze WES data. These tools range from the alignment of raw sequencing reads all the way to linking variants to actionable therapeutics. Strengths and weaknesses of each tool are discussed for the purpose of helping researchers make more informative decisions on selecting the best tools to analyze their WES data.

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