that may not have been available to them otherwise. CREEDS: Community Research Education and Engagement for Data Science Patricia Kovatch; Andrew Sharp, Ph.D., Luz Claudio, Ph.D. mssm.edu/CREEDS CREEDS represents our commitment to the overall goal of fostering practical skills for a national, diverse and interdisciplinary community of early career researchers. This program is funded through the NIH Big Data to Knowledge (BD2K), award #1R25EB020393. Skills Developed and Activities Program Introduction Summer School for Computational Genomics 2016 – Year 1 Outcomes We will personally engage 150 graduate students through an intensive, self-tailored, two-week summer school in NYC that will showcase interesting, current, collaborative case studies in activities at schools throughout NYC. Participants will employ active learning techniques to develop their skills of specific new methods and tools through both individual and group tasks on real-life large data sets. The training will raise the skills of students of varied backgrounds to a sufficient level for additional graduate research and will not require any prior computing experience. Students will also receive experience and materials to help them teach others when they return to their home institutions. Additionally, we will mentor another 30 NYC-based graduate students for team participation in four month long DREAM challenges. We will reach more individuals by placing the summer school on Coursera. Feedback from Students: “I thought this was an excellent course. Some modules were taught quicker than others and were more difficult to follow, but I suppose it is difficult to cover all of the topics and teach them at a leisurely pace. Nevertheless, I greatly appreciate the powerpoints and lectures as resources that I can use and refer to even after the course ended. Very much recommend this course to colleagues.”  “Overall though, I really did enjoy this program, I loved the field trips, the professors were very knowledgeable and I'm glad I got to connect with them. Thanks for having the course!”  Our goal for the summer school is to provide students with an initial familiarity and context from which students will be able to select and use genomics data tools and approaches. Skills Developed Activity Team problem solving DREAM challenges, Trivia Night, RCR, select Summer school activities Peer mentoring Presentation Forum Communication Poster session, Trivia Night, PathoMap, DREAM challenges Understanding of bioethics issues and reproducibility especially related to genetic testing Summer school segments: Responsible Conduct Research (RCR) and Genomics in the Clinic Develop relationships and a broader community Summer school, Kipin Hall, DREAM challenges, tours to other NYC-based labs and institutions Understanding of computing and data architectures Summer school with individualized training Basic Unix, job submission, scripting, Python, data movement and management Understanding of NGS and the human genome Summer School Practical use of genome browser and data analysis tools for RNA and DNA sequencing using real-life data sets including: UCSC genome browser, Galaxy Toolkit, geWorkbench, PlinkSeq, XHMM, DAPPLE, DNENRICH, voom, STAR, IGV, GATK, 1000 Genomes, GWAS and WES data Summer School, PathoMap activity, DREAM challenges with individualized training Algorithm development, programming and problem solving for real-life biological problems DREAM challenges Figure 2 Participants of the 2016 Summer School for Computational Genomics Course Evaluations: A thorough evaluation was conducted before, during and after the summer school program. The aims of the evaluation were: 1) to determine if the program improved capacity in key aspects of computational genomics among participants 2) to assess what components of the program were best received by the students 3) to capture students’ input in order to make improvements for future programs Most students (81%) stated that their motivation to participate in the Mount Sinai program was that they needed to learn computational genomic for their own research. Many (41%) said that they did not have other opportunities to learn these skills at their institutions.  Students were asked to self-rate their confidence (CRAI scale from 0 = no confidence, to 10 = total confidence) in performing research tasks related to computational genomics before their participation, and three weeks after they completed the program. Figure 1 New York City Skyline Skills Before Program Rating After Program Rating Level of confidence (17 skills including Python, Unix) 3.89 6.35 Download and analyze genome sequence data 4.56 7.09 Use of GWAS in research 2.66 5.32 DREAM Challenges DREAM Challenges Table 2 Skills Developed & Activities The goal of the DREAM Challenges is to teach interdisciplinary collaboration and advances programming skills. In small groups, faculty will mentor teams to develop computational solutions to real-life biological questions over a four-month period. The 2016 DREAM Challenge (Resilience to Infectious Diseases DREAM Challenge) was focused on assessing the capabilities of developing early stage predictors of viral resilience based on peripheral blood expression patterns of host immune response. Participants were challenged to build predictors from time-series expression data that can distinguish people who do not get sick following exposure to flu and other respiratory viruses. 2017 Summer School for Computational Genomics Table 1 Skill Development Comparisons Therefore, the Mount Sinai program represented a unique opportunity for these students to advance their research skills that may not have been available to them otherwise. June 12th through June 23rd, 2017 For more information and to apply, visit mssm.edu/CREEDS