To meet the growing need for computational and bioinformatics skills for trainees, the CCBB sponsors two peer-led working groups geared towards (1st year) graduate students and postdocs, who are just starting new research projects that will involve programming and statistics, but who have little or no background in the area. The working groups provide informal weekly sessions that facilitate the transfer and sharing of knowledge. Each session has a period devoted to solving problems contributed by the participants and provide a cheat sheet (posted on the CCBB wiki) that can serve as a future reference.
Programming is becoming an ever-more fundamental part of biological research. To help foster the growing Biocomputing community here at UT, The Center for Computational Biology and Bioinformatics will again sponsor a Peer-led Working Group on Computing in Biology during the spring semester.
What it is: A no-pressure learning environment where graduate students and post-docs can learn the basics of programming. This is not a for-credit course. Its purpose is not to replace any existing courses, but to bolster the programming community here and provide support for those who want to learn but don’t know where to begin.
Who it’s for: The course is geared towards graduate students and post-docs, who are just starting new research projects that will involve programming, but who have little or no background in the area. More experienced programmers are welcome to attend! We would especially like to welcome experienced researchers to attend our open-coding hour (see below) and be part of the growing bio-computing community.
Who is in charge: Stephanie Spielman, Becca Tarvin, and Sean Leonard (see bios below)
This spring, the course will have two components and meet twice a week:
The first component will be a teaching session that will begin with several weeks of introduction to the command line and Unix/Linux, and then cover beginning Python programming. Like last year, the second half of the semester will then feature graduate students, post-docs, and other researchers teaching more specific topics in Biocomputing. This session will be held every Wednesday from 4-5pm in FNT 1.104, starting January 20th, 2016.
The second component will be an Open Coding Hour where attendees can work on their own projects or projects related to the course. We encourage students with any kind of biocomputing experience to attend this section, whether or not you attend the teaching sessions, with the goal of building an interactive and hands-on bio-computing community. Open coding hour will be held on Tuesdays from 5-6 in the CCBB conference room (GDC 7.514), starting January 26th, 2016.
Whatever your background or experience, we encourage you to attend one or both of these components in the spring. Please visit the Biocomputing course page from Spring 2015 to see what we covered last year: https://CCBBatUT.github.io/Biocomputing_Spring2015
If you have any questions, please contact:
I am a 5th year Ph.D. candidate in Claus Wilke’s lab through the EEB program, and my research focuses on computational molecular evolution. Our lab is a computational lab, and I conduct my research using python, R, UNIX, and C++. I’m broadly interested in “best practices” in evolutionary data analysis - in other words, what are the best approaches/ methodologies that we can use to extract meaningful biological information from genetic data? In particular, I study the properties and limitations of different statistical modeling frameworks used to discern strength and direction of natural selection in protein-coding sequences.
I’m a 5th year PhD student in EEB; my studies focus on poison frog evolution. Over the past three years I’ve learned how to use python, R, unix, and TACC to analyze RNAseq data and speed up things like renaming files, blasting huge amounts of sequences and sorting results, and dealing with genomic data in general. For me biocomputing has opened new avenues of research and potential careers. My interest in teaching this course is to help you all get introduced and involved in something that will certainly save you time and preserve your sanity in the future.
I'm a 2nd year CMB student co-advised by Drs. Jeffrey Barrick and Nancy Moran, and I study the microbiome of bees. My work is mainly wet-lab experimental, and I'm self taught in computational methods for biology and handling uncomfortably-sized data sets. I prefer to code in R and bash, but can get by in Python. Next generation sequencing (RNAseq, Tnseq, genome variant analysis), reproducible research, and data visualization are the things I like to think about the most. I feel strongly about making computation accessible to everyone, and I love to help others learn to use computation to make their research *easier* and more powerful (ultimately to learn more about biology). twitter: @spleonard1
Practicum course for beginners on tools used in modern biology. In hands-on exercises students will build their own Linux virtual machine, learn how to process data on the command line, and learn the fundamentals of programming in Python. This class is for students without prior knowledge of command-line software tools or programming. Students must bring their own laptops, which must be powerful enough to run a virtual machine; the class requires 20 GB of hard drive space on the student laptops.
Instructor: James Derry (jderry[at]austin.utexas.edu)
Enrollment limit: 30 students
Time: MWF, 4:00PM - 5:00PM,
Dates: Jan 26 – Apr 24, 2015
Location: CBA 4.324
Enrollment limit: 30 students
Time: TTH, 3:30PM - 5:00PM
Dates: Jan 27 – Apr 23, 2014
Location: PAR 203
Integrative Biology graduate students will be hosting a set of peer-led workshops in Biological Statistics over the Fall 2015 semester. The goal of this workshop is to provide graduate students early in their studies with a broad set of statistical tools which can be applied to future research problems.
The course will take place weekly on Fridays from 2-3:30 PM (GDC 7.514), starting Friday September 4th, and is kindly hosted by the Center for Computational Biology and Bioinformatics.
This course will provide a broad overview and workable examples of the types of statistical analyses commonly used by graduate students in the department, using the R programming language. This course is not meant to replace formal course work in statistics. Instead, it will provide participants with a foundation of knowledge that can be built upon by future study. The first few weeks, we will teach basic R functionality and discuss basic topics in probability and statistics. Subsequent weeks will be devoted to more specialized and advanced topics.
We invite you to take the following survey (click here for survey) to help us shape the course to best meet the needs of Integrative Biology students.
We'd like to recruit graduate students and post-docs with experience in data analysis to lead sections of this workshop. This is a great opportunity to get additional teaching experience! Indicate your interest in teaching and/or taking sessions of the workshop in the survey.