Bioinformatics Core Summer Workshops 2022

Four courses that can be attended in-person at the University of South Carolina in Columbia, SC or virtual (synchronous). Courses are free for all affiliate members of SC INBRE. Download shareable flyer (PDF).

REGISTER HERE

    • Beginning Linux for Bioinformatics, Week of June 20

    • Bioinformatics Analysis in R, Week of July 11

    • Bioinformatics Analysis in Python3, Week of July 18

    • Data Analytics and ML in Python3, Week of July 25

Contact

Dr. Homay Valafar
Bioinformatics
Core Director

Email

What is Bioinformatics?

NIH describes bioinformatics as the field “that deals with the application of computers to the collection, organization, analysis, manipulation, presentation, and sharing of biologic data. A central component of bioinformatics is the study of the best ways to design and operate biologic databases. This is in contrast with the field of computational biology, where specific research questions are the primary focus."

SC INBRE Bioinformatics Core recognizes the importance of bioinformatics to biomedical research in our State. We have a commitment to assist South Carolina’s biomedical research community. As a Core, we provide bioinformatics support to faculty, postdocs, grad and undergrad students of SC INBRE’s lead and partner institutions and also offer two competitive grants programs: Bioinformatics Pilot Project Program and Student-Initiated Research Program. Our Core also hosts free workshops and recordings of workshops are on our website under the Learning tab.

Beginning Linux for Bioinformatics
Week of June 20

This course will provide an introduction on how to use Linux to perform some pre and postprocessing of genomic data and other data used in Bioinformatics analyses. These topics will include file system navigation and maintenance, search for data and patterns, extraction of data and manipulation of FASTA files, working with CSV files, and remote access to systems.

Course is designed for investigators who have little to no familiarity with the Linux environment, but are familiar with the use of personal computers to accomplish scientific data analytics. Participants will need to be familiar with software installation and know how to work around their system.

Each session will begin at 10 am with an hour of presentation and related demonstrations on a given topic. Sessions will conclude with a set of assigned work to be completed by the next session with office hours held each day from 3-4s.

Attendance can be in-person or synchronized remotely. The location of in-person attendance will be announced at a later time and will be on the campus of UofSC, Columbia.

You only need to have a functional computer. All of the pedagogical material in this course are not compute-intensive and can be easily accomplished on a typical personal computer.

Bioinformatics Analysis in R
Week of July 11

This course will provide an introduction to a few typical Bioinformatics analyses in the R environment. More specifically, introductory R, quality control, sequence alignment, phylogeny trees, data visualization, DGE, SNP, and PCA analyses will be discussed in this course.

Course is designed for investigators who have little to no familiarity with the R environment but are familiar with the biological principles of the course. Participants will need to be familiar with software installation and know how to work around their system.

Each session will start at 10 am with an hour of presentation and related demonstrations on a given topic. Sessions will conclude with a set of assigned work to be completed by the next session with office hours held on each day from 3-4s.

Attendance can be in-person or synchronized remotely. The location of in-person attendance will be announced at a later time and will be on the campus of UofSC, Columbia.

You only need to have a functional computer. We recommend the use of R-Studio in this course. We also encourage the participants to use their own data during the last day of the workshop.

Bioinformatics Analysis in Python3
Week of July 18

This course will provide an introduction to a few typical Bioinformatics analyses in the Python environment. More specifically, introductory Python programming, introduction to BioPython, quality control, sequence alignment, phylogeny trees, PCA, and data visualization will be discussed in this course.

Course is designed for investigators who have little to no familiarity with the Python environment but are familiar with the biological principles of the course. Participants will need to be familiar with software installation and know how to work around their system.

Each session will start at 10 am with an hour of presentation and related demonstrations on a given topic. Sessions will conclude with a set of assigned work to be completed by the next session with office hours held on each day from 3-4s.

Attendance can be in-person or synchronized remotely. The location of in-person attendance will be announced at a later time and will be on the campus of UofSC, Columbia.

You only need to have a functional computer. We recommend the use of PyCharm as the preferred Python programming environment. We also encourage the participants to use their own data during the last day of the workshop.

Data Analysis and ML in Python3
Week of July 25

This course will provide an introduction to a few typical Bioinformatics analyses in the Python environment. More specifically, introductory Python programming, introduction to BioPython, quality control, sequence alignment, phylogeny trees, PCA, and data visualization will be discussed in this course.

This course will provide an introduction to cluster analysis, decision trees, and regression analysis in Python. The course will start with understanding the dataset and its preparation for analysis and will be followed by hands-on examples. The participants will learn how to understand the dataset, apply the algorithms, and understand the result.

Each session will start at 10 AM with an hour of presentation and related demonstrations on a given topic. Each session will conclude with a set of assigned work to be completed by the next session with office hours held on each day from 3-4s.

Attendance can be in-person or synchronized remotely. The location of in-person attendance will be announced at a later time and will be on the campus of UofSC, Columbia.

You only need to have a functional computer. We recommend the use of PyCharm as the preferred Python programming environment. We also encourage the participants to use their own data during the last day of the workshop.