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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.

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Related Experiment Video

Updated: Jun 17, 2025

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

Maptcha: an efficient parallel workflow for hybrid genome scaffolding.

Oieswarya Bhowmik¹, Tazin Rahman², Ananth Kalyanaraman²

¹School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, 99164, USA. oieswarya.bhowmik@wsu.edu.

BMC Bioinformatics

|August 8, 2024

Summary

This summary is machine-generated.

This study introduces Maptcha, a novel parallel workflow for hybrid genome scaffolding. Maptcha generates significantly longer and more accurate genome scaffolds faster than existing methods, even in low-coverage scenarios.

Keywords:

Genome assembly Hybrid scaffolding Long read mapping Sketching

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Last Updated: Jun 17, 2025

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

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Published on: August 20, 2021

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Genome assembly reconstructs genomes by organizing and linking DNA fragments.
Hybrid assembly workflows are crucial, integrating diverse sequencing technologies (long and short reads).
Challenges in hybrid scaffolding include scale, data diversity, and repetitive genomic regions.

Purpose of the Study:

To present Maptcha, a new parallel workflow for hybrid genome scaffolding.
To improve genome assembly accuracy and contiguity by combining partial assemblies with long reads.
To address the complexities of hybrid scaffolding with diverse sequencing data.

Main Methods:

Developed a parallel workflow (Maptcha) for hybrid genome scaffolding.
Utilizes an alignment-free mapping step to construct a contig-contig graph using long reads.
Employs a graph-theoretic heuristic for scaffold generation and a batching technique for parallelization.

Main Results:

Maptcha generates substantially longer and more accurate genome scaffolds compared to state-of-the-art tools.
Scaffold lengths produced by Maptcha are often one to two orders of magnitude greater.
Maptcha significantly reduces processing time from hours to minutes.

Conclusions:

Maptcha offers a faster and more effective solution for hybrid genome scaffolding.
Demonstrates robust performance across various genomes and sequencing coverages.
Shows potential for generating high-quality scaffolds even in low-coverage sequencing settings.