class: center, middle, inverse, title-slide # Cancer Bioinformatics course ## Introduction ### Mikhail Dozmorov ### Virginia Commonwealth University ### 01-25-2021 --- ## Computational Biology <center><img src="img/computational_biology.png" height="300px" /></center> &nbsp; &nbsp; Markowetz, Florian. "[All Biology Is Computational Biology](http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.2002050)" _PLoS Biology_ March 9, 2017 --- ## Welcome! The primary goal of the course is to provide theory and practice of computational genomics, and empower you to conduct independent genomic analyses - We will study the leading computational approaches for analyzing genomes starting from raw sequencing data - The course will focus on cancer genomics and human medical applications, but the techniques will be broadly applicable - The topics will include genomic technologies, alignment algorithms, genome assembly, gene- and miRNA expression, methylation and epigenomic analysis, single nucleotide polymorphism and copy number variant analysis, metagenomics, single-cell analysis, 3D genomics --- ## Logistics - Course Webpage: https://bios691-cancer-bioinformatics.netlify.app/ - Lecture notes, exercises R code, references will be posted there - Course announcements, assignments, homework submission and grading, class video recordings - Blackboard, https://blackboard.vcu.edu - Class Hours: Monday/Wednesday, 9:00 am to 10:20 am - Attendance is encouraged but not required. You can watch lectures later, but be mindful of homework deadlines - Office Hours: by appointment --- ## Virtual format - Let's maintain a welcoming and supportive environment - Video on is encouraged - it increases _your_ attention - Ask as many questions during the class as necessary - this is what live attendance is for - Use Chat functionality - If anyone knows answers, post them there --- ## Slack For the off-class questions/announcements, we'll use [Slack](https://slack.com/). See [Slack 101](https://slack.com/resources/slack-101) if you are new to it - I will send invites using your VCU e-mail addresses - use them to join the [#bios691_cancer_bioinformatics channel](https://dozmorovlab.slack.com/archives/C01J4NKL1B7) - Omit formalities, but be respectful and polite - Questions and answers posted there will be available for everyone - answer them if you can, or I'll address them - Direct messages are possible - use them to ask private questions or schedule a meeting - I'll try to answer your questions asap, typically within 24 hours --- ## Prerequisites - No formal course requirements, but basic knowledge of the following will help - Basic statistics knowledge: descriptive statistics, estimators, (linear) modeling (e.g., BIOS 544 or 554 courses) - Basic programming skills in R, familiarity with command line (e.g., BIOS 524 course) - Hardware - A laptop, Mac or Linux OSs are recommended --- ## Computational resources - We will be working within **Linux environment** - If you are on Windows, you may install [Windows Subsystem for Linux](https://docs.microsoft.com/en-us/windows/wsl/install-win10). Alternatively, [Cygwin](https://www.cygwin.com/). It should enable a Linux command line environment (bash) on your local machine - Alternatively, contact Helen Wang (huwang at vcu.edu) to get an account on our computer cluster, `merlot.bis.vcu.edu` - VCU Biostatistics cluster information, https://wiki.vcu.edu/display/biosit/Home - We will program in [R](https://www.r-project.org/) and use [RStudio](https://rstudio.com/products/rstudio/download/) - Install R and RStudio on your machine --- ## Class Resources - Primary Texts: We will be studying research papers and notes - Lecture slides will contain links. Make good use of them, explore the references on your own! - Other Resources: Google, SEQanswers, Biostars, StackOverflow --- ## Grading Policies - Homework assignments practicing methods and tools learned in class: 50% of the total grade - Due two weeks after the assignment - Final Project developing a genomics analysis: 50% - Pre-proposal and due dates are to be discussed - Collaborations are allowed, no more than two per group - Duplicate or nearly identical homeworks will receive a score of zero --- ## Course evaluation - At the end of the course, you will be asked to evaluate it - Assess what you learned during the course - Take notes on what you like in the course, what you want to be improved - Your evaluation is anonymous