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 contribution | Estimation of GFP-tagged RNA numbers from temporal flourescence intensity data |
|---|---|
| Original language | English |
| Pages (from-to) | 69-75 |
| Number of pages | 7 |
| Journal | Bioinformatics |
| Volume | 31 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 1 Jan 2015 |
| Publication type | A1 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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