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Computational synchronization of microarray data with application to Plasmodium falciparum

BACKGROUND: Microarrays are widely used to investigate the blood stage of Plasmodium falciparum infection. Starting with synchronized cells, gene expression levels are continually measured over the 48-hour intra-erythrocytic cycle (IDC). However, the cell population gradually loses synchrony during...

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Autores principales: Zhao, Wei, Dauwels, Justin, Niles, Jacquin C, Cao, Jianshu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3380736/
https://www.ncbi.nlm.nih.gov/pubmed/22759568
http://dx.doi.org/10.1186/1477-5956-10-S1-S10
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author Zhao, Wei
Dauwels, Justin
Niles, Jacquin C
Cao, Jianshu
author_facet Zhao, Wei
Dauwels, Justin
Niles, Jacquin C
Cao, Jianshu
author_sort Zhao, Wei
collection PubMed
description BACKGROUND: Microarrays are widely used to investigate the blood stage of Plasmodium falciparum infection. Starting with synchronized cells, gene expression levels are continually measured over the 48-hour intra-erythrocytic cycle (IDC). However, the cell population gradually loses synchrony during the experiment. As a result, the microarray measurements are blurred. In this paper, we propose a generalized deconvolution approach to reconstruct the intrinsic expression pattern, and apply it to P. falciparum IDC microarray data. METHODS: We develop a statistical model for the decay of synchrony among cells, and reconstruct the expression pattern through statistical inference. The proposed method can handle microarray measurements with noise and missing data. The original gene expression patterns become more apparent in the reconstructed profiles, making it easier to analyze and interpret the data. We hypothesize that reconstructed gene expression patterns represent better temporally resolved expression profiles that can be probabilistically modeled to match changes in expression level to IDC transitions. In particular, we identify transcriptionally regulated protein kinases putatively involved in regulating the P. falciparum IDC. RESULTS: By analyzing publicly available microarray data sets for the P. falciparum IDC, protein kinases are ranked in terms of their likelihood to be involved in regulating transitions between the ring, trophozoite and schizont developmental stages of the P. falciparum IDC. In our theoretical framework, a few protein kinases have high probability rankings, and could potentially be involved in regulating these developmental transitions. CONCLUSIONS: This study proposes a new methodology for extracting intrinsic expression patterns from microarray data. By applying this method to P. falciparum microarray data, several protein kinases are predicted to play a significant role in the P. falciparum IDC. Earlier experiments have indeed confirmed that several of these kinases are involved in this process. Overall, these results indicate that further functional analysis of these additional putative protein kinases may reveal new insights into how the P. falciparum IDC is regulated.
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spelling pubmed-33807362012-06-25 Computational synchronization of microarray data with application to Plasmodium falciparum Zhao, Wei Dauwels, Justin Niles, Jacquin C Cao, Jianshu Proteome Sci Proceedings BACKGROUND: Microarrays are widely used to investigate the blood stage of Plasmodium falciparum infection. Starting with synchronized cells, gene expression levels are continually measured over the 48-hour intra-erythrocytic cycle (IDC). However, the cell population gradually loses synchrony during the experiment. As a result, the microarray measurements are blurred. In this paper, we propose a generalized deconvolution approach to reconstruct the intrinsic expression pattern, and apply it to P. falciparum IDC microarray data. METHODS: We develop a statistical model for the decay of synchrony among cells, and reconstruct the expression pattern through statistical inference. The proposed method can handle microarray measurements with noise and missing data. The original gene expression patterns become more apparent in the reconstructed profiles, making it easier to analyze and interpret the data. We hypothesize that reconstructed gene expression patterns represent better temporally resolved expression profiles that can be probabilistically modeled to match changes in expression level to IDC transitions. In particular, we identify transcriptionally regulated protein kinases putatively involved in regulating the P. falciparum IDC. RESULTS: By analyzing publicly available microarray data sets for the P. falciparum IDC, protein kinases are ranked in terms of their likelihood to be involved in regulating transitions between the ring, trophozoite and schizont developmental stages of the P. falciparum IDC. In our theoretical framework, a few protein kinases have high probability rankings, and could potentially be involved in regulating these developmental transitions. CONCLUSIONS: This study proposes a new methodology for extracting intrinsic expression patterns from microarray data. By applying this method to P. falciparum microarray data, several protein kinases are predicted to play a significant role in the P. falciparum IDC. Earlier experiments have indeed confirmed that several of these kinases are involved in this process. Overall, these results indicate that further functional analysis of these additional putative protein kinases may reveal new insights into how the P. falciparum IDC is regulated. BioMed Central 2012-06-21 /pmc/articles/PMC3380736/ /pubmed/22759568 http://dx.doi.org/10.1186/1477-5956-10-S1-S10 Text en Copyright ©2012 Zhao et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Proceedings
Zhao, Wei
Dauwels, Justin
Niles, Jacquin C
Cao, Jianshu
Computational synchronization of microarray data with application to Plasmodium falciparum
title Computational synchronization of microarray data with application to Plasmodium falciparum
title_full Computational synchronization of microarray data with application to Plasmodium falciparum
title_fullStr Computational synchronization of microarray data with application to Plasmodium falciparum
title_full_unstemmed Computational synchronization of microarray data with application to Plasmodium falciparum
title_short Computational synchronization of microarray data with application to Plasmodium falciparum
title_sort computational synchronization of microarray data with application to plasmodium falciparum
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3380736/
https://www.ncbi.nlm.nih.gov/pubmed/22759568
http://dx.doi.org/10.1186/1477-5956-10-S1-S10
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