scholarly journals MAPPI-DAT: data management and analysis for protein-protein interaction data from the high-throughput MAPPIT cell microarray platform

2017 ◽  
pp. btx014 ◽  
Author(s):  
Surya Gupta ◽  
Veronic De Puysseleyr ◽  
José Van der Heyden ◽  
Davy Maddelein ◽  
Irma Lemmens ◽  
...  
Keyword(s):  
Data Management ◽  
High Throughput ◽  
Protein Interaction ◽  
Microarray Platform ◽  
Protein Interaction Data ◽  
Interaction Data ◽  
Protein Protein Interaction ◽  
Cell Microarray

scholarly journals Computational identification of signaling pathways in protein interaction networks

F1000Research ◽  
2015 ◽  
Vol 4 ◽  
pp. 1522
Author(s):  
Angela U. Makolo ◽  
Temitayo A. Olagunju
Keyword(s):  
Signaling Pathways ◽  
High Throughput ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Protein Interaction Data ◽  
Interaction Data ◽  
Protein Protein Interaction

The knowledge of signaling pathways is central to understanding the biological mechanisms of organisms since it has been identified that in eukaryotic organisms, the number of signaling pathways determines the number of ways the organism will react to external stimuli. Signaling pathways are studied using protein interaction networks constructed from protein-protein interaction data obtained from high-throughput experiments. However, these high-throughput methods are known to produce very high rates of false positive and negative interactions. To construct a useful protein interaction network from this noisy data, computational methods are applied to validate the protein-protein interactions. In this study, a computational technique to identify signaling pathways from a protein interaction network constructed using validated protein-protein interaction data was designed.A weighted interaction graph of Saccharomyces Cerevisiae was constructed. The weights were obtained using a Bayesian probabilistic network to estimate the posterior probability of interaction between two proteins given the gene expression measurement as biological evidence. Only interactions above a threshold were accepted for the network model.We were able to identify some pathway segments, one of which is a segment of the pathway that signals the start of the process of meiosis in S. Cerevisiae.

scholarly journals Categorizing Biases in High-Confidence High-Throughput Protein-Protein Interaction Data Sets

2011 ◽  
Vol 10 (12) ◽  
pp. M111.012500 ◽  
Author(s):  
Xueping Yu ◽  
Joseph Ivanic ◽  
Vesna Memišević ◽  
Anders Wallqvist ◽  
Jaques Reifman
Keyword(s):  
High Throughput ◽  
Protein Interaction ◽  
Protein Interaction Data ◽  
Data Sets ◽  
Interaction Data ◽  
High Confidence ◽  
Protein Protein Interaction

Discovering Interaction Motifs from Protein Interaction Networks

2009 ◽  
pp. 99-116 ◽  
Author(s):  
Hugo Willy
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Protein Interaction Data ◽  
Interaction Data ◽  
Protein Patterns ◽  
Protein Protein Interaction ◽  
Family Based ◽  
High Throughput Experiments

Recent breakthroughs in high throughput experiments to determine protein-protein interaction have generated a vast amount of protein interaction data. However, most of the experiments could only answer the question of whether two proteins interact but not the question on the mechanisms by which proteins interact. Such understanding is crucial for understanding the protein interaction of an organism as a whole (the interactome) and even predicting novel protein interactions. Protein interaction usually occurs at some specific sites on the proteins and, given their importance, they are usually well conserved throughout the evolution of the proteins of the same family. Based on this observation, a number of works on finding protein patterns/motifs conserved in interacting proteins have emerged in the last few years. Such motifs are collectively termed as the interaction motifs. This chapter provides a review on the different approaches on finding interaction motifs with a discussion on their implications, potentials and possible areas of improvements in the future.

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