Giorgia Giacomini (University of Siena)
Oct 16, 2019 – 11:00 AM
DIISM, Artificial Intelligence laboratory (room 201), Siena SI
Over the past decades, the introduction of massively parallel sequencing platforms, known as Next Generation Sequencing (NGS), changed the landscape of the genetics studies in cancer genomics. NGS technologies allow for detection and quantification of DNA and RNA, providing us an instrument to fill the gap between gene sequences and their biological and molecular functions.
Several methods have been developed to analyze and quantify the transcriptome, but RNA-Sequencing (RNA-seq) provides a more accurate measurement of levels of transcripts than hybridization-based approaches.
Computational analysis of RNA-seq data allows us to investigate the biological functions and the dynamics of RNAs in different conditions.
RNA-Seq is not limited to detecting transcripts that correspond to existing genomic sequences. In addition, RNA-seq approach is focused on small RNA, noncoding RNA, microRNAs and also reveals sequence variations such as single nucleotide polymorphisms (SNPs) in the transcribed regions.
Even if the transcriptomic measurement of mRNA level by RNA-Seq is helpful, is not sufficient to adequately profile the expression of mRNAs. Indeed the mRNAs produced during transcription are not necessarily expressed during translation.
Thanks to the development of a recent technology, named Ribosome Profiling, we can monitor translation at high resolution. To investigate the translation status of different transcripts in cancer and healthy samples, we will compare and integrate multiple data sources (RNA-seq data and Ribo-seq data).
Evaluation of global changes in translation efficiency in human tumors has thus the potential to identify the mRNAs translated and to understand which of them can be used as biomarkers and therapeutic targets.