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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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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 new method for decontamination of de novo transcriptomes using a hierarchical clustering algorithm.

Joël Lafond-Lapalme^1,2, Marc-Olivier Duceppe¹, Shengrui Wang³

¹Agriculture and Agri-Food Canada, Saint-Jean-sur-Richelieu, QC J3B 3E6, Canada.

Bioinformatics (Oxford, England)

|December 25, 2016

Summary

Identifying contaminating DNA sequences in de novo assemblies is difficult. A new method, MCSC, uses sequence patterns for accurate decontamination, even with unknown or misaligned sequences.

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

De novo genome assembly is crucial for understanding novel organisms.
Contaminating sequences are a significant challenge in de novo assemblies, especially for complex sample types.
Existing decontamination methods relying solely on database alignments often yield suboptimal results.

Purpose of the Study:

To develop and present a novel computational method for identifying and removing contaminating sequences from de novo assemblies.
To improve the accuracy of genome assembly by addressing the challenge of unavoidable contamination in certain sample types.

Main Methods:

A hierarchical clustering algorithm named MCSC (Mining Sequence Clusters) was developed.
The MCSC method utilizes frequent sequence patterns to form clusters.
Clusters are classified as target species or contaminants using alignment tools, with an advantage for unknown or misaligned sequences.

Main Results:

The MCSC method effectively identifies contaminating sequences in de novo assemblies.
This approach improves decontamination accuracy, particularly for sequences that are unknown or misaligned to existing databases.
The method offers a robust solution for samples where target organism isolation is challenging.

Conclusions:

The MCSC method provides a powerful new approach to decontaminate de novo assemblies.
This tool enhances the reliability of genomic data derived from complex environmental or host-associated samples.
The developed algorithm offers improved performance over traditional alignment-based decontamination techniques.