Allele-Specific Gene Expression Analysis In Patients With Invasive Breast Carcinoma
Open AccessNext-generation sequencing (NGS) has enlightened up new ways to understand and fight against cancer through the identification of the cancer genome and epigenome. With the enhancements in computational biology and bioinformatics, whole-genome and whole-exome sequencing has been used a lot in the search for identification of genomic differences in various cancer types. Although there are many tools claiming to achieve success in predicting events related to cancer, there is still no tool claiming to produce sufficient algorithms to predict genomic positions associated with various genetic events, while assigning functionality to these genetic variants in both individual and high-throughput settings at the same time.While working with Dr. Anelia Horvath, one of my projects was to improve a novel computational RNA-DNA software called “RNA2DNAlign” that our lab has been developing. The software is being developed by using pysam module of Python and is capable of comparative quantitative assessment of allele counts across paired RNA and DNA sequencing datasets. We are using RNA2DNAlign to compute the likelihood for association of the variant that can be strongly associated to tumorigenesis such as: RNA editing, variant-specific expression/loss, somatic mutagenesis and loss-of-heterozygosity. I used this software to estimate allele specific gene expression events throughout multiple transcriptome and exome datasets simultaneously. We applied RNA2DNAlign on three hundred sixty matching normal and tumor exomes and transcriptomes from ninety breast cancer samples downloaded from the TCGA (The Cancer Genome Atlas) public data portal ( https://tcga-data.nci.nih.gov/tcga/). The software revealed 17577 unique variations associated with asymmetric allele distribution. The performance assessment of RNA2DNAlign shows very high specificity and sensitivity, due to the corroboration of signals across multiple matching dataset; we are currently applying RNA2DNAlign on datasets of different types of cancers. I am confident that in addition to my thesis, the outcomes of the rest of this project will help the scientific community to better understand the molecular mechanisms involved in the onset and development of different kinds of cancer, and will facilitate the development of improved tools for diagnosis and treatment.
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