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Integrating Transcriptome and Proteome Profiling of Plasmodium Falciparum Using TopS (309964)

Despite of the various efforts to correlate mRNA and protein levels on a global scale, still no commonly accepted method has been developed to address this crucial question of how to simultaneously analyze mRNA and protein datasets. To test our method, we made use of an available large-scale data set of Plasmodium falciparum, which provides the transcriptome and proteome for six different stages across its life cycle in order to provide new insights into this complex system, which is causative for most severe forms of human malaria, one of the most widespread disease and is responsible for millions of deaths especially children. Given the complexity of the life cycle of the Plasmodium falciparum as well as its resistance to the commonly used drugs, many efforts are still ongoing to understand the biology of this parasite and to discover new drugs and vaccines. Here, we present the first attempt to systematically group mRNA and proteins based on the dynamic profile patterns in proteome and transcriptome obtained from six different stages during the life cycle of P. falciparum using topological scores (TopS) and machine learning.