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Malaria.tools-comparative genomic and transcriptomic database for Plasmodium species.

Qiao Wen Tan¹, Marek Mutwil¹

¹School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore 637551, Singapore.

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|August 3, 2019

Summary

This summary is machine-generated.

This study introduces malaria.tools, a database for understanding malaria-causing Plasmodium parasites. It uses gene co-expression networks to predict gene function and identify pathogenicity genes.

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Array Comparative Genomic Hybridization Array CGH for Detection of Genomic Copy Number Variants

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Area of Science:

Genomics and Bioinformatics
Parasitology
Molecular Biology

Background:

Malaria, a tropical disease caused by Plasmodium parasites, affects millions globally.
A significant portion of Plasmodium falciparum genes remain uncharacterized, hindering functional understanding.
Existing genomic data requires sophisticated tools for functional gene prediction.

Purpose of the Study:

To develop a comprehensive database (malaria.tools) for analyzing Plasmodium gene function.
To enable cross-species comparison of gene expression and co-expression networks.
To facilitate the identification of genes related to parasite pathogenicity and life stages.

Main Methods:

Construction of gene co-expression networks using transcriptomic data from Plasmodium falciparum and P. berghei.
Integration of genomic data from six Plasmodium species and two Apicomplexa species.
Development of bioinformatics tools within malaria.tools for network analysis and gene function prediction.

Main Results:

The malaria.tools database integrates genomic and transcriptomic data for multiple species.
Tools are available for identifying co-expressed gene neighborhoods, clusters, and life stage-specific expression.
The platform successfully demonstrates the identification of genes relevant to pathogenicity and parasite life stages.

Conclusions:

malaria.tools provides valuable resources for functional genomics in Plasmodium research.
The database facilitates cross-species comparative analysis to uncover conserved and unique gene functions.
This resource aids in predicting gene function and identifying targets for malaria control.