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Related Concept Videos

Next-generation Sequencing03:00

Next-generation Sequencing

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
Although all next-generation methods use different technologies, they all share a set of standard features.
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
Sanger Sequencing01:57

Sanger Sequencing

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...
Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved DNA...
RNA-seq03:21

RNA-seq

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. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...
Modern Molecular Taxonomy01:29

Modern Molecular Taxonomy

Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...

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Rare Event Detection Using Error-corrected DNA and RNA Sequencing
10:36

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Consensus rules in variant detection from next-generation sequencing data.

Peilin Jia1, Fei Li, Jufeng Xia

  • 1Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America.

Plos One
|June 21, 2012
PubMed
Summary
This summary is machine-generated.

Accurate variant detection in next-generation sequencing (NGS) relies on filtering. This study identifies four key parameters—quality, mapping, allele balance, and gene examination—to improve variant calling accuracy and reduce costs.

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Area of Science:

  • Genomics and Bioinformatics
  • Molecular Biology

Background:

  • Next-generation sequencing (NGS) generates vast amounts of data for variant detection.
  • Post hoc filtering of computationally predicted variants is essential for accuracy.
  • Current filtering methods may not fully optimize variant validation.

Purpose of the Study:

  • To identify critical parameters for enhancing the accuracy of variant calling in NGS data.
  • To provide a framework for improving the validation of single nucleotide variants (SNVs) and insertions/deletions (indels).

Main Methods:

  • Analysis of four key sequence features for variant filtering: quality and deepness, mapping refinement, allele/strand balance, and spurious gene examination.
  • Evaluation of the impact of these parameters on the accuracy of variant detection.

Main Results:

  • Appropriate application of quality/deepness metrics significantly improves variant calling.
  • Refined mapping strategies enhance the reliability of detected variants.
  • Allele/strand balance assessment and spurious gene checks are crucial for filtering accuracy.

Conclusions:

  • Implementing the highlighted filtering parameters can substantially increase variant validation rates in NGS projects.
  • Optimized variant filtering saves significant time and resources in genomic research.
  • These sequence features offer a robust approach to improving the precision of variant detection.