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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.
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.
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Maxam-Gilbert Sequencing01:05

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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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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 23, 2026

Novel Sequence Discovery by Subtractive Genomics
09:40

Novel Sequence Discovery by Subtractive Genomics

Published on: January 25, 2019

GRASS: a generic algorithm for scaffolding next-generation sequencing assemblies.

Alexey A Gritsenko1, Jurgen F Nijkamp, Marcel J T Reinders

  • 1The Delft Bioinformatics Lab, Department of Mediamatics, Delft University of Technology, Mekelweg 4, Delft. a.gritsenko@tudelft.nl

Bioinformatics (Oxford, England)
|April 12, 2012
PubMed
Summary
This summary is machine-generated.

GRASS, a new genome assembly scaffolder, uses diverse data for more accurate draft genomes from short-read sequencing. It constructs a similar number of scaffolds but with fewer errors compared to existing methods.

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Last Updated: May 23, 2026

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Published on: January 25, 2019

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08:43

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12:08

Hybrid De Novo Genome Assembly for the Generation of Complete Genomes of Urinary Bacteria using Short- and Long-read Sequencing Technologies

Published on: August 20, 2021

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • High-throughput sequencing (HTS) generates vast amounts of short-read data, necessitating efficient methods for de novo genome assembly.
  • Existing scaffolding algorithms often rely solely on paired-read data and may prioritize scaffold length over accuracy.
  • There is a need for advanced scaffolding tools that can integrate diverse data sources for improved draft genome quality.

Purpose of the Study:

  • To develop a novel scaffolding algorithm, GRASS (GeneRic ASsembly Scaffolder), for second-generation sequencing data.
  • To create a scaffolder capable of utilizing diverse information beyond paired-read data.
  • To enhance the accuracy and reliability of draft genome assemblies.

Main Methods:

  • GRASS employs a mixed-integer programming formulation to optimize contig order, distance, and orientation simultaneously.
  • The optimization problem is addressed using an expectation-maximization procedure combined with an unconstrained binary quadratic programming approximation.
  • The algorithm was evaluated using Illumina paired-read data from three bacterial genomes.

Main Results:

  • GRASS produced a comparable number of scaffolds to existing methods.
  • The GRASS algorithm demonstrated a significant reduction in scaffolding errors.
  • Performance was further enhanced when incorporating additional data, such as related genome sequences.

Conclusions:

  • GRASS offers a more accurate approach to scaffolding HTS genome assemblies.
  • The algorithm's ability to integrate diverse data sources improves draft genome quality.
  • GRASS provides a valuable tool for researchers involved in de novo genome sequencing projects.