HUM-MOLGEN events: Bioinformatics for Cancer Genomics

With the introduction of next-generation sequencing platforms, it is now feasible to use high-throughput approaches to address many research questions. This is especially true in cancer research, which has rapidly embraced high-throughput technologies. Dedicated compute clouds such as the Cancer Genome Collaboratory also facilitate complex analyses on large cancer data sets from projects such as the International Cancer Genome Consortium (ICGC) and the Pan-Cancer Analysis of Whole Genomes (PCAWG). Now more than ever, it is crucial to know what bioinformatic tools and resources are available in cancer research, and it is necessary to develop informatic skills to analyze high-throughput data using those tools.

The Canadian Bioinformatics Workshops (CBW), in collaboration with Cold Spring Harbor Laboratory, has developed a comprehensive six-day course covering key bioinformatics concepts and tools required to analyze cancer genomic data sets. This course combines the material and concepts from established CBW workshops; see a full outline here. In this course, participants will gain practical experience and skills to:

visualize genomic data
analyze cancer data sets for gene expression, genome rearrangement, somatic mutation, and copy-number variation
analyze and conduct pathway analysis on candidate gene lists
integrate -omic and clinical data
launch, configure, customize, and scale virtual machines (VMs)
navigate cloud repositories and work with data sets in them
follow best practices in data and workflow management

Target Audience: Graduate students, postdoctoral scholars, clinical scientists, and principal investigators currently working with cancer genomics data, or about to embark on projects that require analysis of such data.

Prerequisites: You must have basic familiarity with UNIX/Linux and R to participate in this course. Specifically, you will need to understand material in the following tutorials before attending the course: UNIX for Beginners (tutorials 1-3) and Introductory R Tutorial (chapters 1-5). You can also review material from the prior CBW workshop on Exploratory Analysis of Biological Data Using R. A useful hands-on tool for getting started in R is Swirl. Pre-work and pre-course readings can be found on GitHub.

Computer requirements: If selected for the course, you will need to bring your own laptop computer. The minimum specifications are as follows: 1024x768 screen resolution, 1.5GHz CPU, 2GB RAM, 10GB free disk space, and a recent version of the Windows, Mac OS X or Linux operating systems. Most laptops purchased in the past 3-4 years likely meet these requirements. If you do not have access to your own laptop, you may be able to borrow one from CSHL.