Computational Genomics Shared Resource (CG-SR) Project Summary The Computational Genomics Shared Resource (CG-SR) incorporates both bioinformatics and genomics expertise for the Purdue Center for Cancer Research (PCCR). The greatest utility of the CG-SR is in data analysis. Since 2015, the CG-SR has analyzed dozens of projects, of which the majority are bulk and single-cell RNA sequencing (RNA-seq), whole genome sequencing (WGS), chromatin immunoprecipitation sequencing (ChIP-seq), whole genome CRISPR-Cas 9 screens, and bisulphite sequencing. Since its inception in 2015, the bioinformatics services provided by the CG-SR has supported 45 PCCR investigators' research projects (23 in the last year) from all three of PCCR's Research Programs. The analyses performed by the CG-SR are highly customized and tailored to individual groups, providing PCCR members with fast, reliable, and high-quality data analysis. The incorporated genomics consulting services provide equal access to all PCCR members and have served 53 PCCR members in the last year. The CG-SR aids in experimental design, ensuring that the experiments carried out by PCCR members are feasible, provide adequate power, and follow experimental guidelines suggested in the scientific literature. The CG-SR regularly participates in writing grants and manuscripts with PCCR members. In this way, the PCCR members are supported by the CG-SR from the initial stages of a project, all the way through publication. Finally, the CG-SR provides both formal and informal bioinformatics training and support for PCCR members. The CG-SR currently mentors four trainees (three graduate students and one undergraduate student) and prepares both computationally and biologically inclined graduate students to bridge the gap between computational and biological disciplines. The CG-SR aided in designing and teaching a course to encourage life scientists to use high-performance computing resources on campus and to teach them the basic skills that are necessary for such work. The CG-SR regularly gives talks and workshops on campus, and provides group and one-on-one training for labs, students, postdocs, and faculty who wish to learn to perform their own data analysis.
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