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Genome Annotation and Assembly

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 Sequencing

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 Sequencing

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
The...

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Related Experiment Video

Updated: Mar 15, 2026

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

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

Information-optimal genome assembly via sparse read-overlap graphs.

Ilan Shomorony¹, Samuel H Kim¹, Thomas A Courtade¹

¹Department of Electrical Engineering & Computer Sciences, University of California, Berkeley, CA, USA.

Bioinformatics (Oxford, England)

|September 3, 2016

Summary

This summary is machine-generated.

This study introduces the Not-So-Greedy algorithm for long-read sequencing assembly. It guarantees accurate genome reconstruction when sufficient data is available, outperforming standard methods.

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Last Updated: Mar 15, 2026

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

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

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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

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Published on: June 23, 2012

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Third-generation long-read sequencing generates large datasets crucial for genome assembly.
Read-overlap graphs are central to assembly, but the NP-hard nature of finding Hamiltonian paths limits heuristic approaches.
Existing methods often struggle with accuracy and efficiency due to computational complexity.

Purpose of the Study:

To investigate the information limits for unambiguous genome reconstruction from sequencing reads.
To develop a novel algorithm that overcomes the NP-hard complexity of sequence assembly.
To provide a theoretically guaranteed and practically efficient assembly method.

Main Methods:

The study focuses on the information feasibility of read sets for unambiguous reconstruction.
An algorithm, termed Not-So-Greedy, is developed based on insights from information feasibility.
The algorithm constructs a sparse read-overlap graph, transforming assembly into an Eulerian path problem.

Main Results:

The Not-So-Greedy algorithm guarantees linear time assembly when information feasibility conditions are met.
Evaluations show improved accuracy in read-overlap graph construction and contig N50 compared to standard string graph methods.
The algorithm demonstrated strong performance on simulated data and a PacBio Escherichia coli K12 dataset.

Conclusions:

The Not-So-Greedy algorithm offers a theoretically sound and practically effective solution for long-read genome assembly.
It provides a performance guarantee, reducing assembly to a solvable Eulerian path problem under specific conditions.
This approach enhances the accuracy and efficiency of genome assembly from long sequencing reads.