TY - JOUR
T1 - Defining the protein interaction network of human malaria parasite Plasmodium falciparum
AU - Ramaprasad, Abhinay
AU - Pain, Arnab
AU - Ravasi, Timothy
N1 - KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work was funded by King Abdullah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia.
PY - 2012/2
Y1 - 2012/2
N2 - Malaria, caused by the protozoan parasite Plasmodium falciparum, affects around 225. million people yearly and a huge international effort is directed towards combating this grave threat to world health and economic development. Considerable advances have been made in malaria research triggered by the sequencing of its genome in 2002, followed by several high-throughput studies defining the malaria transcriptome and proteome. A protein-protein interaction (PPI) network seeks to trace the dynamic interactions between proteins, thereby elucidating their local and global functional relationships. Experimentally derived PPI network from high-throughput methods such as yeast two hybrid (Y2H) screens are inherently noisy, but combining these independent datasets by computational methods tends to give a greater accuracy and coverage. This review aims to discuss the computational approaches used till date to construct a malaria protein interaction network and to catalog the functional predictions and biological inferences made from analysis of the PPI network. © 2011 Elsevier Inc.
AB - Malaria, caused by the protozoan parasite Plasmodium falciparum, affects around 225. million people yearly and a huge international effort is directed towards combating this grave threat to world health and economic development. Considerable advances have been made in malaria research triggered by the sequencing of its genome in 2002, followed by several high-throughput studies defining the malaria transcriptome and proteome. A protein-protein interaction (PPI) network seeks to trace the dynamic interactions between proteins, thereby elucidating their local and global functional relationships. Experimentally derived PPI network from high-throughput methods such as yeast two hybrid (Y2H) screens are inherently noisy, but combining these independent datasets by computational methods tends to give a greater accuracy and coverage. This review aims to discuss the computational approaches used till date to construct a malaria protein interaction network and to catalog the functional predictions and biological inferences made from analysis of the PPI network. © 2011 Elsevier Inc.
UR - http://hdl.handle.net/10754/565967
UR - https://linkinghub.elsevier.com/retrieve/pii/S0888754311002631
UR - http://www.scopus.com/inward/record.url?scp=84856471096&partnerID=8YFLogxK
U2 - 10.1016/j.ygeno.2011.11.006
DO - 10.1016/j.ygeno.2011.11.006
M3 - Article
C2 - 22178265
SN - 0888-7543
VL - 99
SP - 69
EP - 75
JO - Genomics
JF - Genomics
IS - 2
ER -