Course Description

Save a bundle and receive 30% off the individual course rate when you register for BIOF 521 and BIOF 549!

Bundled Courses: 
BIOF 521 | Bioinformatics for Analysis of Data Generated by Next Generation Sequencing
BIOF 549 | Introduction to Omics and Data Integration

In BIOF 521,  learn to analyze data generated by a variety of sequencing techniques (such as DNAseq, RNAseq and CHIP-seq) particularly in relation to biomedical applications (such as analysis of gene expression and identification of medically relevant sequence variation). While recorded lectures and readings will provide necessary background, the course emphasizes hands-on, self-paced lessons featuring real-world data sets to give the learner experience with all major steps of sequencing analyses, from filtering of raw data to creating polished figures. As the course progresses, students will work on a term project in which they design a sequencing project based on their own research interests. To make this course accessible to all students, we will focus on the use of publicly available resources, such as the NCBI SRA and the Galaxy platform, that can be accessed from anywhere.

In BIOF 549, look at strategies for integrating Transcriptomic, Genetic, and Epigenetic data sets  to decode the underlying biology of interest.  A little over two decades since the first draft of the human genome sequence was published, high-throughput sequencing data is now routinely being generated at single cell level. These high-throughput data sets are being generated for multiple modalities, including transcriptomics, epigenomics, genomics, and proteomics. Significant advances have been made in the primary processing of these data sets that include mapping the data to reference genome and quantifying the data. However, the downstream analysis remains a major challenge, particularly in integrating data originating from multiple modalities.
Systematic integration of data originating from multiple modalities has the potential to decode novel cellular events that orchestrate gene regulation. Such integrative analyses can help elucidate molecular mechanisms that underlie the development or disease states of a cell or tissue. 

Learner Outcomes

• Compare and contrast a variety of modern sequencing techniques and their applications.
• Utilize and compare several platforms for the analysis of sequencing data.
• Carry out bioinformatics analyses on biomedically relevant sequencing data sets.
• Interpret the results of these analyses by generating figures and written summaries.
• Develop a sequencing and analysis plan for a project relevant to their own research interests.
• Locate public repositories that store quantified next-generation sequencing data for all three major modalities.
• Apply data science strategies to filter outliers or low-quality data for a given a quantified data set.
• Formulate integration strategies for data sets originating from at-least two distinct modalities.

Microcredential(s)

These courses apply toward the Bioinformatics Endeavor digital badge.

Textbook Information