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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.

RNA-seq

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...

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Related Experiment Video

Updated: Feb 16, 2026

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

Published on: May 9, 2017

A divide-and-conquer algorithm for large-scale de novo transcriptome assembly through combining small assemblies from

Sing-Hoi Sze^1,2, Jonathan J Parrott³, Aaron M Tarone³

¹Department of Computer Science and Engineering, Texas A&M University, College Station, Mexico, 77843, TX, USA. shsze@cse.tamu.edu.

|December 16, 2017

Summary

A new divide-and-conquer strategy enables memory-intensive de novo transcriptome assembly algorithms to process large RNA-Seq datasets. This approach subdivides data, allowing for efficient and accurate large-scale transcriptome construction.

Keywords:

Divide-and-conquer RNA-Seq de novo transcriptome assembly

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Last Updated: Feb 16, 2026

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

High-throughput sequencing advances transcriptomic studies in non-model organisms.
Increasing RNA-Seq library sizes challenges de novo transcriptome assembly.
Existing memory-intensive algorithms are limited to small assembly sizes.

Purpose of the Study:

To develop a method for utilizing memory-intensive de novo transcriptome assembly algorithms on large RNA-Seq datasets.
To overcome the limitations of current algorithms in handling extensive transcriptomic data.

Main Methods:

A divide-and-conquer strategy was developed to subdivide large RNA-Seq datasets into smaller libraries.
Each library was independently assembled using existing algorithms.
A merging algorithm was created to combine individual assemblies by selecting high-quality transcripts.

Main Results:

The strategy enables the use of memory-intensive algorithms for large-scale transcriptome assembly.
Accuracy is comparable or increased compared to memory-efficient algorithms.
Accuracy is comparable or decreased compared to memory-intensive algorithms on small assemblies.

Conclusions:

The divide-and-conquer strategy successfully allows memory-intensive de novo transcriptome assembly algorithms to construct large assemblies.
This method enhances the scalability of transcriptome assembly for large datasets.