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INFO-B 574 Next-Generation Sequencing Data Analysis

3 credits

  • Prerequisites: None
  • Delivery: On-Campus
  • This course covers concepts of genomic sequencing datasets from several sequencing platforms, including how the data motivates computational needs and tasks for analysis. Students learn how to devise approaches for analyzing massive clinical and biomedical sequencing datasets and for developing sound hypotheses and making predictions.

    Learning Outcomes

    1. Analyze genomic data appropriately considering the sequencing technique and molecular biology.
    2. Determine 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 and CLIP sequence reads.
    4. 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.