Branching process deconvolution algorithm reveals a detailed cell-cycle transcription program

Due to cell-to-cell variability and asymmetric cell division, cells in a synchronized population lose synchrony over time. As a result, time-series measurements from synchronized cell populations do not reflect the underlying dynamics of cell-cycle processes. Here, we present a branching process dec...

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Published inProceedings of the National Academy of Sciences - PNAS Vol. 110; no. 10; pp. E968 - E977
Main Authors Guo, Xin, Bernard, Allister, Orlando, David A, Haase, Steven B, Hartemink, Alexander J
Format Journal Article
LanguageEnglish
Published United States National Academy of Sciences 05.03.2013
National Acad Sciences
SeriesPNAS Plus
Subjects
Algorithms
Animal populations
Biological Sciences
Cell cycle
Cell Cycle - genetics
Cell Cycle - physiology
Cell division
Eukaryotes
G1 Phase - genetics
G1 Phase - physiology
Gene Expression Regulation, Fungal
Genes, Fungal
Models, Biological
Models, Genetic
Physical Sciences
PNAS Plus
Saccharomyces cerevisiae - cytology
Saccharomyces cerevisiae - genetics
Saccharomyces cerevisiae - physiology
Systems Biology
Transcription, Genetic
Transcriptome
Yeast
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Summary:Due to cell-to-cell variability and asymmetric cell division, cells in a synchronized population lose synchrony over time. As a result, time-series measurements from synchronized cell populations do not reflect the underlying dynamics of cell-cycle processes. Here, we present a branching process deconvolution algorithm that learns a more accurate view of dynamic cell-cycle processes, free from the convolution effects associated with imperfect cell synchronization. Through wavelet-basis regularization, our method sharpens signal without sharpening noise and can remarkably increase both the dynamic range and the temporal resolution of time-series data. Although applicable to any such data, we demonstrate the utility of our method by applying it to a recent cell-cycle transcription time course in the eukaryote Saccharomyces cerevisiae . Our method more sensitively detects cell-cycle–regulated transcription and reveals subtle timing differences that are masked in the original population measurements. Our algorithm also explicitly learns distinct transcription programs for mother and daughter cells, enabling us to identify 82 genes transcribed almost entirely in early G1 in a daughter-specific manner.
Bibliography:http://dx.doi.org/10.1073/pnas.1120991110
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Author contributions: X.G., A.B., D.A.O., S.B.H., and A.J.H. designed research; X.G., A.B., and A.J.H. performed research; X.G., A.B., S.B.H., and A.J.H. analyzed data; and X.G. and A.J.H. wrote the paper.
Edited by Gilbert Chu, Stanford University School of Medicine, Stanford, CA, and accepted by the Editorial Board December 28, 2012 (received for review December 23, 2011)
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.1120991110