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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.
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...
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.
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...

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Related Experiment Video

Updated: May 19, 2026

Targeted DNA Methylation Analysis by Next-generation Sequencing
08:38

Targeted DNA Methylation Analysis by Next-generation Sequencing

Published on: February 24, 2015

Qualimap: evaluating next-generation sequencing alignment data.

Fernando García-Alcalde1, Konstantin Okonechnikov, José Carbonell

  • 1Bioinformatics and Genomics Department, Centro de Investigación Príncipe Felipe, Valencia, Spain.

Bioinformatics (Oxford, England)
|August 24, 2012
PubMed
Summary
This summary is machine-generated.

Qualimap is a Java application for quality control of next-generation sequencing data. It detects biases in sequence alignment/map (SAM) and binary alignment/map (BAM) files, ensuring data accuracy for downstream analyses.

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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

Related Experiment Videos

Last Updated: May 19, 2026

Targeted DNA Methylation Analysis by Next-generation Sequencing
08:38

Targeted DNA Methylation Analysis by Next-generation Sequencing

Published on: February 24, 2015

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
09:34

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

Area of Science:

  • Bioinformatics
  • Genomics
  • Computational Biology

Background:

  • Sequence Alignment/Map (SAM) and Binary Alignment/Map (BAM) formats are standard for next-generation sequencing data.
  • These files contain millions of reads, often with biases from sequencing technology, protocols, or mapping algorithms.
  • Detecting these biases is critical for reliable downstream analyses.

Purpose of the Study:

  • To develop a user-friendly tool for comprehensive quality control of sequence alignment data.
  • To identify and evaluate biases introduced during sequencing and mapping processes.

Main Methods:

  • Developed Qualimap, a Java application.
  • Analyzes sequence alignment data (SAM/BAM files).
  • Considers sequence features and their genomic properties for quality assessment.

Main Results:

  • Qualimap provides graphical and statistical analyses of mapping data.
  • Identifies potential issues in sequencing and/or mapping processes.
  • Facilitates the evaluation of data quality.

Conclusions:

  • Qualimap is a vital tool for ensuring the quality of next-generation sequencing data.
  • Highlights the importance of addressing biases before further analysis.
  • Aims to improve the reliability of genomic data interpretation.