Häkkinen Antti, Ribeiro Andre S
Laboratory of Biosystem Dynamics, Computational Systems Biology Research Group, Department of Signal Processing, Tampere University of Technology, P.O. box 553, 33101 Tampere, Finland.
Bioinformatics. 2015 Jan 1;31(1):69-75. doi: 10.1093/bioinformatics/btu592. Epub 2014 Sep 3.
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.
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.
Source code is available under Mozilla Public License at http://www.cs.tut.fi/%7ehakkin22/jumpdet/.
RNA的MS2 - GFP标记是目前测量活细胞中连续转录事件之间间隔的唯一方法。为此，必须从强度时间轨迹中准确检测新转录本。
我们提出了一种从细胞荧光强度的时间数据自动估计RNA数量和产生间隔的新方法，该方法通过利用时间信息降低了不确定性。我们还推导了一种更稳健的变体，对例如RNA移出焦点等引起的异常值更具抗性。通过蒙特卡罗模拟，我们表明与以前的方法相比，RNA数量和产生间隔的量化通常得到了改善。最后，我们分析了来自活大肠杆菌的数据，并显示出与以前方法的统计学显著差异。新方法可用于量化任何荧光探针的数量和产生间隔，这些荧光探针以低拷贝数存在，比细胞背景亮且降解缓慢。
源代码可在http://www.cs.tut.fi/%7ehakkin22/jumpdet/ 以Mozilla公共许可证获取。
