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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.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features.
Maxam-Gilbert Sequencing01:05

Maxam-Gilbert Sequencing

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.
Challenges of the Maxam-Gilbert Method
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Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
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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Related Experiment Video

Updated: Jun 21, 2026

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

Maximum likelihood genome assembly.

Paul Medvedev1, Michael Brudno

  • 1Department of Computer Science, University of Toronto , Toronto, Canada.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|August 4, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a novel bidirected network flow method for whole genome shotgun assembly, accurately reconstructing double-stranded DNA sequences. The approach provides the first exact polynomial time algorithm for this complex genomic challenge.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Whole genome shotgun assembly reconstructs genomes from short DNA sequences (reads).
  • Existing methods often simplify DNA's double-stranded nature, posing challenges for accurate reconstruction.

Purpose of the Study:

  • To develop an exact polynomial time algorithm for double-stranded genome assembly.
  • To introduce a maximum likelihood framework for more accurate genome reconstruction.
  • To improve the estimation of repeat copy counts and contig assembly.

Main Methods:

  • Utilized bidirected network flow and Chinese Postman Problem algorithms on bidirected graphs.
  • Constructed bidirected de Bruijn graphs for DNA sequence reconstruction.
  • Developed a maximum likelihood framework incorporating repeat copy number estimation and matepair data.

Main Results:

  • Achieved the first exact polynomial time algorithm for double-stranded genome assembly.
  • Demonstrated highly accurate estimation of repeat copy counts using a bidirected network flow algorithm.
  • Successfully assembled long contigs from simulated Escherichia coli data, outperforming standard approaches.

Conclusions:

  • Bidirected network flow offers a powerful approach for accurate double-stranded genome assembly.
  • The maximum likelihood framework improves genomic reconstruction by accurately estimating repeat copy numbers.
  • This methodology advances the field of computational genomics and DNA sequence assembly.