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Humboldt-Universität zu Berlin - Mathematisch-Naturwissenschaftliche Fakultät - Institut für Informatik

Probevortrag Dissertation: Michael Piechotta

Wann 15.02.2019 ab 09:15 (Europe/Berlin / UTC100) iCal
Wo Rudower Chaussee 25, Raum 4.410
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Herr Michael Piechotta wird in Kürze seine Dissertation zum Thema
Computational methods for transcriptome-wide identification and characterization of RNA editing sites in paired sequencing samples
an unserer Fakultät einreichen. Die Arbeit ist insbesondere am MDC entstanden und wird von Prof. Ohler als Erstgutachter betreut.

Sein Vortrag findet am Freitag, den 15.2.2019 ab 9.00 c.t. im R. 4.410 statt.

Abstract

RNA editing refers to co-transcriptional RNA base modifications that increase transcript sequence diversity without changing the underlying genome. Site-specific RNA editing events may lead to amino acid substitutions by changing codons in coding sequences. Apart from its role in coding regions, RNA editing may also influence transcript splicing and structure and could have an effect on mRNA stability and nuclear export.
The availability of deep next-generation sequencing data enabled the transcriptome-wide discovery of RNA editing events. However, these early attempts suffered from the inherent artifacts of short read and ambiguities in read mapping leading to high false positive rate (FPR 90%) among called RNA editing sites.
Characterization of the nucleotide frequency spectrum of RNA-DNA differences (RDDs) at a given location is the most direct way of identifying editing sites, whereas RNA-RNA comparison may pinpoint differential editing events across samples and conditions (RNA-RNA differences, in short RRDs).
Computational methods for the identification of single nucleotide variants (SNVs) are developed and presented. Difficulties in calling specifically RDDs and RRDs are examined and solutions are presented. Computational methods are calibrated and tested on in silico benchmarks and their performance is compared against other popular variants callers. Finally, the performance is evaluated in a controlled biological setting using sequencing data from ADAR knockdown experiments with human embryonic kidney (HEK-293) cells.

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