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Related Concept Videos

Genome Annotation and Assembly03:36

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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.
Genome Copying Errors02:46

Genome Copying Errors

DNA replication is a well-evolved process that copies millions of base pairs with high fidelity during each cell division. Occasionally a wrong base or a long stretch of wrong bases may get added to the daughter strands. If the errors are left unchecked, cells might accumulate several mutations that might endanger their  survival. Therefore, the copying errors are checked and repaired at three levels.

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Related Experiment Video

Updated: Jun 25, 2026

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

PEMer: a computational framework with simulation-based error models for inferring genomic structural variants from

Jan O Korbel1, Alexej Abyzov, Xinmeng Jasmine Mu

  • 1Gene Expression Unit, European Molecular Biology Laboratory (EMBL), Meyerhofstr, Heidelberg, 69117, Germany. korbel@embl.de

Genome Biology
|February 25, 2009
PubMed
Summary

Paired-End Mapper (PEMer) accurately reconstructs genomic structural variants from next-generation sequencing data. This tool enhances personal genomics studies by providing reliable variant mapping and confidence values.

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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Related Experiment Videos

Last Updated: Jun 25, 2026

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

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Personal genomics projects generate vast amounts of data on genomic structural variants.
  • Analyzing these variants is crucial for understanding human genetic diversity and disease.

Purpose of the Study:

  • To develop and present Paired-End Mapper (PEMer), a computational tool for analyzing genomic structural variants.
  • To provide a robust method for reconstructing structural variants from next-generation sequencing data.

Main Methods:

  • Developed Paired-End Mapper (PEMer) analysis pipeline compatible with multiple sequencing platforms.
  • Incorporated simulation-based error models to generate confidence values for structural variants.
  • Utilized a coverage-adjusted multi-cutoff scoring strategy for variant reconstruction.

Main Results:

  • PEMer demonstrated high efficiency in reconstructing structural variants.
  • The tool showed relative insensitivity to base-calling errors, improving data reliability.
  • Simulation results validated PEMer's performance and accuracy.

Conclusions:

  • PEMer is an effective tool for mapping genomic structural variants in personal genomics research.
  • The developed methods enhance the accuracy and reliability of structural variant detection.
  • PEMer contributes to advancing the analysis of large-scale genomic datasets.