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INFO-B 474 Next Generation Sequencing Data Analysis

3 credits

Prerequisties: None

This course covers basic concepts of genomic sequencing datasets from several sequencing platforms, including how the data motivates computational needs and methods for analysis. Students learn how to devise approaches for analyzing massive clinical and biomedical sequencing datasets and for developing sound hypotheses and predictions from them.

Learning Outcomes

  1. Summarize genomic data appropriately with respect to the sequencing technique and considering molecular biology.
  2. Perform sequence alignment and genome assembling.
  3. Align and quantitate (a) DNA sequence reads of various platforms; (b) RNA sequence reads of various platforms; (c) microbial DNA and RNA sequence reads; and (d) ChIP-seq and CLIP-seq reads.
  4. Process and analyze microbial genomics, metagenomics, metatranscriptomics, operons, and transcription units taxonomic mapping, microbial abundance, interactions, and pathways.
  5. Compare and contrast computational methods for performing peak calling and benchmarking and for analyzing ChIP-seq, CLIP-seq, and post-transcriptional regulation.
  6. Analyze diverse datasets, including small RNA sequencing, polyA sequencing, and protein occupancy profiling.
  7. Evaluate genetic and somatic variation, differences among variant calling approaches, expression quantitative trait loci identification, and related issues and considerations.
  8. Evaluate personalized sequencing projects with respect to ethical considerations.
  9. Write a report and give an oral presentation grounded in an appropriate review of the literature.

Course Delivery

  • On-Campus

Syllabi