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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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Updated: Mar 21, 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

Informed kmer selection for de novo transcriptome assembly.

Dilip A Durai¹, Marcel H Schulz¹

¹Cluster of Excellence on Multimodal Computing and Interaction, Saarland University, Saarbrücken, 66123, Germany Department for Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarbrücken, 66123, Germany.

Bioinformatics (Oxford, England)

|May 7, 2016

Summary

This summary is machine-generated.

This study introduces an automated method to optimize de novo transcriptome assembly by determining the ideal k-mer value. This approach saves computational time without sacrificing assembly quality, making multi-k-mer methods more accessible.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

De novo transcriptome assembly is crucial for RNA-seq workflows, especially for non-model organisms.
Most assemblers utilize de Bruijn graphs (DBGs), where assembly quality depends heavily on the chosen k-mer length.
Current multi-k-mer approaches merge assemblies from various k-mer values but lack an optimal strategy for selecting these values.

Purpose of the Study:

To develop an automated method for identifying the optimal k-mer value to stop transcriptome assembly.
To improve the efficiency and reliability of multi-k-mer assembly strategies.
To reduce computational time in transcriptome assembly without compromising assembly quality.

Main Methods:

Investigated the contribution of individual k-mer values in multi-k-mer assembly.
Employed comparative clustering of related assemblies to estimate the importance of additional k-mer assemblies.
Developed a model-fit algorithm to predict the optimal k-mer value for assembly termination.

Main Results:

Demonstrated that comparative clustering can effectively estimate the importance of k-mer assemblies.
Successfully predicted the k-mer value at which further assembly is unnecessary.
Validated the approach across different de novo assemblers, coverage values, and read lengths.
Identified a post-processing step that significantly enhances multi-k-mer assembly quality.

Conclusions:

An automated method was developed to limit k-mer values in transcriptome assembly.
The method achieves significant savings in assembly time with minimal loss in assembly quality.
This advancement makes multi-k-mer assembly methods more practical and user-friendly.