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Genome Annotation and Assembly

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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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Updated: May 15, 2026

A Computational Pipeline for Intergenic/Intragenic Enhancer RNA Quantification in Mouse Embryonic Stem Cells

A Computational Pipeline for Intergenic/Intragenic Enhancer RNA Quantification in Mouse Embryonic Stem Cells

Published on: October 28, 2025

TIGER: tiled iterative genome assembler.

Xiao-Long Wu¹, Yun Heo, Izzat El Hajj

¹Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.

BMC Bioinformatics

|January 4, 2013

Summary

This summary is machine-generated.

Tiger is a novel de novo genome assembly framework that adapts to available computing resources. This approach allows for high-quality genome assembly using modest memory, making it accessible for more researchers.

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Area of Science:

Genomics and Bioinformatics
Computational Biology

Background:

Next-generation sequencing (NGS) technologies have reduced costs, enabling deeper biological insights and disease research.
De novo genome assembly is crucial for reconstructing genomes but often requires substantial computational resources, limiting accessibility.

Purpose of the Study:

To develop a de novo genome assembly framework that is adaptable to varying computational resources.
To enable high-quality genome assembly with accessible memory requirements.

Main Methods:

Developed Tiger, a novel de novo assembly framework.
Implemented an iterative decomposition approach to adapt to available computing resources.
Designed flexibility to integrate diverse assemblers for various genome types.

Main Results:

Tiger achieved superior NG50 values and genome coverage compared to Velvet and SOAPdenovo on human chromosome data.
The framework demonstrated effective assembly with modest memory usage, suitable for commodity computers.
Slightly higher error rates were observed, but overall quality and accessibility were improved.

Conclusions:

Tiger offers a viable solution for utilizing memory-intensive de novo assemblers on widely available hardware.
The framework enables high-quality genome assembly with a low memory footprint.
Demonstrated scalability using distributed commodity computers for enhanced performance.