ArticleProcedia computer science2012
Measuring gene expression noise in early Drosophila embryos: nucleus-to-nucleus variability.
Article in Procedia computer science, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Article
- Causality analysis detects the regulatory role of maternal effect genes in the earlyGenomics data · 2017Article
- Bicoid signal extraction with a selection of parametric and nonparametric signal processing techniques.Genomics, proteomics & bioinformatics · 2015Article
- A glance at the applications of Singular Spectrum Analysis in gene expression data.Biomolecular detection and quantification · 2015Review
- Shaped 3D singular spectrum analysis for quantifying gene expression, with application to the early zebrafish embryo.BioMed research international · 2015Article
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Authors and funding
6 authors.
Funding
Abstract
In recent years the analysis of noise in gene expression has widely attracted the attention of experimentalists and theoreticians. Experimentally, the approaches based on in vivo fluorescent reporters in single cells appear to be straightforward and effective tools for bacteria and yeast. However, transferring these approaches to multicellular organisms presents many methodological problems. Here we describe our approach to measure between-nucleus variability (noise) in the primary morphogenetic gradient of Bicoid (Bcd) in the precellular blastoderm stage of fruit fly (Drosophila) embryos. The approach is based on the comparison of results for fixed immunostained embryos with observations of live embryos carrying fluorescent Bcd (Bcd-GFP). We measure the noise using two-dimensional Singular Spectrum Analysis (2D SSA). We have found that the nucleus-to-nucleus noise in Bcd intensity, both for live (Bcd-GFP) and for fixed immunstained embryos, tends to be signal-independent. In addition, the character of the noise is sensitive to the nuclear masking technique used to extract quantitative intensities. Further, the method of decomposing the raw quantitative expression data into a signal (expression surface) and residual noise affects the character of the residual noise. We find that careful masking of confocal images and use of appropriate computational tools to decompose raw expression data into trend and noise makes it possible to extract and study the biological noise of gene expression.
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Registered trials
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