Bioinformatics Core Summer Workshops 2023

This course will provide an introduction on how to spin up a Cloud based server and 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. The 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. 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 USC 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.

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. The 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. 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 USC Columbia.

You only need to have a functional computer. You 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. 

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. The 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. 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 USC 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. 

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 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 USC 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.