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140.688.01
Statistics for Genomics

Location
East Baltimore
Term
4th Term
Department
Biostatistics
Credit(s)
3
Academic Year
2015 - 2016
Instruction Method
TBD
Class Time(s)
Tu, Th, 1:30 - 2:50pm
Auditors Allowed
Yes, with instructor consent
Available to Undergraduate
No
Grading Restriction
Letter Grade or Pass/Fail
Course Instructor(s)
Contact Name
Frequency Schedule
Every Year
Prerequisite

Some familarity with the R statistical language will be assumed; a student without any experience in this language can still take the class but will need to set aside additional time to learn R. A suitable background class is 140.776.01 – Statistical Computing

Description
Introduces statistical genomics with an emphasis on next generation sequencing and microarrays. Covers the key capabilities of the Bioconductor project (a widely used open source software project for the analysis of high-throughput experiments in genomics and molecular biology and rooted in the open source statistical computing environment R). Also introduces statistical concepts and tools necessary to interpret and critically evaluate the bioinformatics and computational biology literature. Includes an overview of preprocessing and normalization, batch effects, statistical inference, multiple comparisons. Intended for students with a background in statistics or biology, but not necessarily both. Assumes some familarity with the R statistical language (a student without any experience in this language can still take the class but will need to set aside additional time to learn R).
Learning Objectives
Upon successfully completing this course, students will be able to:
  1. Describe the basics of how various high-throughput assays works, including microarrays and next generation sequencing
  2. Critique existing methodology for the analysis of high-throughput biological data
  3. Write R code to import and analyze microarray and next generation sequencing data