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Related Concept Videos

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...
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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...
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.

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Rare Event Detection Using Error-corrected DNA and RNA Sequencing
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Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Detecting Alu insertions from high-throughput sequencing data.

Matei David1, Harun Mustafa, Michael Brudno

  • 1Department of Computer Science, University of Toronto, 10 King's College Road, Toronto, ON M5S 3G4, Canada and Centre for Computational Medicine, Genetics and Genome Biology Program, The Hospital for Sick Children, 555 University Avenue, Toronto, ON M5G 1X8, Canada.

Nucleic Acids Research
|August 8, 2013
PubMed
Summary
This summary is machine-generated.

A new tool, alu-detect, identifies novel Alu insertions in human genomes using high-throughput sequencing data. This method achieves high precision and recall, discovering hundreds of previously undescribed Alu elements per individual.

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Area of Science:

  • Genomics and Bioinformatics
  • Molecular Biology
  • Human Genetics

Background:

  • High-throughput sequencing enables comprehensive human genome variation cataloging.
  • Alu elements are mobile genetic elements that contribute to genomic diversity and disease.
  • Accurate detection of novel Alu insertions and their breakpoints is crucial for understanding genome evolution.

Purpose of the Study:

  • To introduce alu-detect, a novel computational tool for identifying Alu insertions.
  • To precisely map novel Alu insertions and their breakpoints using whole-genome or whole-exome sequencing data.
  • To analyze insertion preferences and characterize newly identified Alu elements.

Main Methods:

  • Development of alu-detect, integrating read-pair and split-read information.
  • Utilized a faux-reference simulation approach for parameter optimization and performance evaluation (precision/recall).
  • Applied alu-detect to Illumina paired-end sequencing data from seven individuals (including trios).

Main Results:

  • Detected an average of 1519 novel Alu insertions per sample.
  • Estimated method precision at 97% and recall at 85% based on simulations.
  • Identified 808 novel Alu insertions not previously reported in other studies.

Conclusions:

  • alu-detect is an effective tool for discovering and characterizing novel Alu insertions from sequencing data.
  • The study identified a significant number of previously unknown Alu elements, expanding the known human mobilome.
  • Demonstrated the utility of alu-detect for investigating Alu insertion patterns and preferences.