Analytical Function to Score LC-WGS Results from the Discrimination of Low-Grade Tumor Samples

Analytical Function to Score LC-WGS Results from the Discrimination of Low-Grade Tumor Samples Analytical Function to Score LC-WGS Results for the Discrimination of Low-Grade from High-Grade Tumor Samples (Follow on funding) Analytical Function to Score LC-WGS Results for the Discrimination of Low-Grade from High-Grade Tumor Samples (Follow on funding) Analytical Function to Score LC-WGS Results for the Discrimination of Low-Grade from High-Grade Tumor Samples (Klim Drobnyh Spring 2019) Analytical Function to Score LC-WGS Results for the Discrimination of Low-Grade from High-Grade Tumor Samples (Follow on funding) Analytical Function to Score LC-WGS Results for the Discrimination of Low-Grade from High-Grade Tumor Samples (Follow on funding) Analysis of genomics data The Bioinformatics team at Mayo has developed workflows to process and visualize genomics results for Low Coverage Whole Genome Sequencing (LC-WGS). LC-WGS assay is mainly used to study Aneuploidy and Copy Number Variants (CNV) events in normal or tumor samples. To be useful, LC-WGS sequencing data have first to be properly processed. The changes of sequencing coverage that informed of the presence of CNV events have to be converted into segments spanning the amplified or deleted regions of the genome. The ASU student will work in close collaboration with Bioinformaticians at Mayo to improve the segmentation algorithm currently used. He will also compare CNVs reported by Oncoscan (a clinical platform used to call CNVs) with results of LC-WGS. Since our preliminary results highlight the potential of LC-WGS to improve CNV calling, we also plan to perform specific studies aiming at associating CNV patterns with tumor grade. This association studies might require the use of machine learning or AI technics. Analysis of clinical data Dr. Kocher has developed a repository of truly de-identified clinical data that can serve as a base for several studies using machine learning or AI technics to identify patterns associated to disease or disease progressions. These patterns can be derived from tokenized diagnosis, procedure, medication and lab test data. The student will work on projects aiming at quality controlling these data and also at building models to predict risk or the health trajectory of patients. Analytical Function to Score LC-WGS Results for the Discrimination of Low-Grade from High-Grade Tumor Samples (Follow on funding) The Bioinformatics team at Mayo has developed workflows to process and visualize genomics results for Low Coverage Whole Genome Sequencing (LC-WGS). LC-WGS assay is mainly used to study Aneuploidy and Copy Number Variants (CNV) events in normal or tumor samples. To be useful, LC-WGS sequencing data have first to be properly processed. The changes of sequencing coverage that informed of the presence of CNV events have to be converted into segments spanning the amplified or deleted regions of the genome. The ASU student will work in close collaboration with Bioinformaticians at Mayo to improve the segmentation algorithm currently used. He will also compare CNVs reported by Oncoscan (a clinical platform used to call CNVs) with results of LC-WGS. Since our preliminary results highlight the potential of LC-WGS to improve CNV calling, we also plan to perform specific studies aiming at associating CNV patterns with tumor grade. This association studies might require the use of machine learning or AI technics. Analysis of clinical data Dr. Kocher has developed a repository of truly de-identified clinical data that can serve as a base for several studies using machine learning or AI technics to identify patterns associated to disease or disease progressions. These patterns can be derived from tokenized diagnosis, procedure, medication and lab test data. The student will work on projects aiming at quality controlling these data and also at building models to predict risk or the health trajectory of patients. Spring 2018: Analytical Function to Score LC-WGS Results for the Discrimination of Low-Grade from high-grade tumor samples