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Related Concept Videos

Next-generation Sequencing03:00

Next-generation Sequencing

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The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
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Comparing Copy Number Variations and SNPs02:26

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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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Sanger Sequencing01:57

Sanger Sequencing

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DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...
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RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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Maxam-Gilbert Sequencing01:05

Maxam-Gilbert Sequencing

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In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
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Benchmarking variant callers in next-generation and third-generation sequencing analysis.

Surui Pei1, Tao Liu2, Xue Ren2

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Briefings in Bioinformatics
|July 23, 2020
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Summary

Choosing the right DNA variant calling tool is crucial for accurate genetic analysis. This study evaluates 11 variant callers across next-generation sequencing (NGS) and third-generation sequencing (TGS) data, providing guidance for optimal performance.

Keywords:
germline variantsomatic variantvariant callers

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • DNA variants are key to individual genetic differences.
  • Next-generation sequencing (NGS) and third-generation sequencing (TGS) are primary technologies for variant calling.
  • Existing variant callers have limitations in sensitivity and specificity across different sequencing types.

Purpose of the Study:

  • To systematically evaluate the performance of 11 variant calling tools on both NGS and TGS datasets.
  • To compare the accuracy of germline and somatic variant calling.
  • To assess computational costs associated with different variant callers.

Main Methods:

  • Evaluation of 11 variant callers on 12 diverse NGS and TGS datasets.
  • Testing germline variant callers (Sentieon, GATK, DeepVariant) on both sequencing types.
  • Testing somatic variant callers (Sentieon, GATK, NeuSomatic, VarScan2, Strelka2) on NGS data.
  • Analysis of variant calling performance concerning sequencing coverage and tumor purity.

Main Results:

  • For germline calling on NGS, Sentieon, GATK, and DeepVariant showed comparable performance; 30× coverage is recommended.
  • For germline calling on TGS, DeepVariant excelled in InDel calling, while SNP calling was similar across tested callers.
  • TGS identified more variants than NGS, especially in complex regions; Sentieon demonstrated the lowest computational cost.

Conclusions:

  • No single variant caller is optimal for all scenarios.
  • Tool and parameter selection are critical for accurate SNP and InDel calling.
  • TGS offers advantages for variant detection in challenging genomic regions.