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Genetic aberrations in hepatocellular carcinoma (HCC) are well known, but the functional consequences of such aberrations remain poorly understood.
Large-scale RNAi screening has become an important technology for identifying genes involved in biological processes of interest. However, the quality of large-scale RNAi screening is often deteriorated by off-targets effects. In order to find statistically significant effector genes for pathogen entry, we systematically analyzed entry pathways in human host cells for eight pathogens using image-based kinome-wide siRNA screens with siRNAs from three vendors. We propose a Parallel Mixed Model (PMM) approach that simultaneously analyzes several non-identical screens performed with the same RNAi libraries.
Chimeric read-through RNAs are transcripts originating from two directly adjacent genes (<10 kb) on the same DNA strand. Although they are found in next-generation whole transcriptome sequencing (RNA-Seq) data on a regular basis, investigating them further has usually been refrained from. Therefore, their expression patterns or functions in general, and in oncogenesis in particular, are poorly understood.
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