Jun Prof Neva Caliskan

Many bacterial and viral pathogens and also their eukaryotic host cells employ non-canonical translation strategies in order to express hidden genes from alternative open reading frames (Caliskan et al., 2015). RNA is a versatile molecule that acts as a key regulator of non-canonical translation events. RNA can exist in various shapes and interact with other regulatory elements such as ncRNAs, small molecules and proteins to alter the meaning of the message encoded in the primary sequence of the mRNA. How RNA structure and regulatory elements drive alternative translation events is currently not fully understood. In addition, it is largely unclear to what extend these translation events are used by the pathogen and the host cell during infections. We use cutting-edge RNA analytics, such as ribosome profiling and deep sequencing combined with single molecule and computational tools to understand dynamics of translation and the functions of RNA regulators during infections. Ultimately, we want to better understand the interplay between the host’s and pathogen’s gene expression and harness our knowledge to develop novel therapeutic strategies to combat infectious diseases.

The POTATO TOOL: DATA ANALYSIS MAde easy

The single-molecule technique known as optical tweezers allows probing of intra- and intermolecular interactions that govern complex biological processes. Recent developments have made it easier to collect data, yet it remains difficult to analyze it. To enable high-throughput data analysis, we developed an automated Python-based analysis pipeline called POTATO (practical optical tweezers analysis tool). POTATO uses predefined parameters to automatically process the high-frequency raw data generated by force-ramp experiments and to identify (un)folding events. Our research was published in the Biophysical Journal.

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