Be part of our H2020 Innovative Training Network Grant

Circular RNAs (circRNAs) are a large, newly discovered class of non-coding RNAs [1]. Due to their specific circular structure they display unusually high stability and can just recently be detected using new sequencing and computational technologies. Characterizing function of circRNAs has recently spiked very high interest in many fields including basic molecular biology, neurobiology, neurodegenerative diseases, aging, cancer, and biomarker research. As member of the circRTrain project, which is a Marie Curie Innovative Training Network (ITN) funded by the European Union within the H2020 Programme, we are seeking highly motivated PhD students to join the Preibisch Lab.

In collaboration with an experimental scientist the candidate will develop software and imaging strategies to quantify circRNA expression in terabyte sized reconstructions of whole animal lightsheet microscopy acquisitions as shown in figure 1 [2,3]. Using ImgLib2 [4] and BigDataViewer [5], the candidate will design and implement algorithms using machine learning (e.g. deep learning) and model-based approaches to measure differences in circRNA expression and localization. The candidate will relate those to developmental differences in between wildtype and mutant animals to understand the mechanisms of circRNA regulation.

Candidates should apply for this competetive position with an email to Dr. Preibisch containing their CV, previous achievements and github profile (or comparable). Candidates are encouraged to add an implementation of an algorithm of their choice in ImgLib2, examples can be found here.

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Fig. 1: Screenshot showing the interactive visualization of the image registration using the BigDataViewer of an adult 4-mm thick mouse brain section. Shown is a transverse cutplane through all tiles of one of the aligned views. Each random color indicates one individual image tile, 1920x1920x1039 pixel in size.

[1] Memczak S., Jens M., Elefsinioti A., Torti F., Krueger J., Rybak A., Maier L., Mackowiak S.D., Gregersen L.H., Munschauer M., Loewer A., Ziebold U., Landthaler M., Kocks C., le Noble F., Rajewsky N. (2013) Circular RNAs are a large class of animal RNAs with regulatory potency. Nature 495(7441), 333–338. Link to Paper

[2] Preibisch S., Saalfeld S., Schindelin J., Tomancak P. (2010) Software for bead-based registration of selective plane illumination microscopy data. Nature Methods 7(6), 418-419. Link to Paper

[3] Preibisch S., Amat F., Stamataki E., Sarov M., Singer R.H., Myers E., Tomancak P. (2014) Efficient Bayesian-based multiview deconvolution. Nature Methods 11(6), 645-648. Link to Paper

[4] Pietzsch T., Preibisch S., Tomancak P., Saalfeld S. (2012) ImgLib2–generic image processing in Java. Bioinformatics 28(22), 3009-3011. Link to Paper

[5] Pietzsch T., Saalfeld S., Preibisch S., Tomancak P. (2015) BigDataViewer: visualization and processing for large image data sets. Nature Methods 12(6), 481-483. Link to Paper