Estimation of GFP-tagged RNA numbers from temporal fluorescence intensity data

Antti Häkkinen, Andre S. Ribeiro

    Research output: Contribution to journalArticleScientificpeer-review

    14 Citations (Scopus)

    Abstract

    Motivation: MS2-GFP-tagging of RNA is currently the only method to measure intervals between consecutive transcription events in live cells. For this, new transcripts must be accurately detected from intensity time traces. Results: We present a novel method for automatically estimating RNA numbers and production intervals from temporal data of cell fluorescence intensities that reduces uncertainty by exploiting temporal information. We also derive a robust variant, more resistant to outliers caused e.g. by RNAs moving out of focus. Using Monte Carlo simulations, we show that the quantification of RNA numbers and production intervals is generally improved compared with previous methods. Finally, we analyze data from live Escherichia coli and show statistically significant differences to previous methods. The new methods can be used to quantify numbers and production intervals of any fluorescent probes, which are present in low copy numbers, are brighter than the cell background and degrade slowly. Availability: Source code is available under Mozilla Public License at http://www.cs.tut.fi/%7ehakkin22/jumpdet/. Contact:

    Translated title of the contributionEstimation of GFP-tagged RNA numbers from temporal flourescence intensity data
    Original languageEnglish
    Pages (from-to)69-75
    Number of pages7
    JournalBioinformatics
    Volume31
    Issue number1
    DOIs
    Publication statusPublished - 1 Jan 2015
    Publication typeA1 Journal article-refereed

    Publication forum classification

    • Publication forum level 3

    ASJC Scopus subject areas

    • Biochemistry
    • Molecular Biology
    • Computational Theory and Mathematics
    • Computer Science Applications
    • Computational Mathematics
    • Statistics and Probability
    • General Medicine

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