Harvard University Free Online Course on Case Studies in Functional Genomics

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Harvard University is offering free online course on Case Studies in Functional Genomics. The course will end with a brief description of the basic steps for analyzing ChIP-seq datasets, from read alignment, to peak calling, and assessing differential binding patterns across multiple samples.

In this four week course applicants will explore data analysis of several experimental protocols, using open source software, including R and Bioconductor. This course will start on September 7, 2017.

Course At A Glance 

Length: 4 weeks
Effort: 2-4 hours pw
Subject: Biology & Life Sciences
Institution: Harvard University and edx
Languages: English
Price: Free
Certificate Available: Yes, Add a Verified Certificate for $49
Session: Course Starts on September 7, 2017

Providers’ Details

Harvard University is devoted to excellence in teaching, learning, and research, and to developing leaders in many disciplines who make a difference globally. Harvard faculty is engaged with teaching and research to push the boundaries of human knowledge.

About This Course

We will explain how to start with raw data, and perform the standard processing and normalization steps to get to the point where one can investigate relevant biological questions. Throughout the case studies, we will make use of exploratory plots to get a general overview of the shape of the data and the result of the experiment. We start with RNA-seq data analysis covering basic concepts of RNA-seq and a first look at FASTQ files.

Why Take This Course?

We will also go over quality control of FASTQ files; aligning RNA-seq reads; visualizing alignments and move on to analyzing RNA-seq at the gene-level: counting reads in genes; Exploratory Data Analysis and variance stabilization for counts; count-based differential expression; normalization and batch effects. Finally, we cover RNA-seq at the transcript-level: inferring expression of transcripts (i.e. alternative isoforms); differential exon usage. We will learn the basic steps in analyzing DNA methylation data, including reading the raw data, normalization, and finding regions of differential methylation across multiple samples.

 Learning Outcomes

  • Mapping reads
  • Quality assessment of Next Generation Data
  • Analyzing RNA-seq data
  • Analyzing DNA methylation data
  • Analyzing ChIP Seq data

Instructors

Michael Love

Michael Love is a postdoctoral fellow with Dr. Irizarry in the Department of Biostatistics at the Dana Farber Cancer Institute and Harvard T.H. Chan School of Public Health. Dr. Love received his bachelor’s in mathematics in 2005 from Stanford University, his master’s in statistics in 2010 from Stanford University, and his Ph.D.

Rafael Irizarry

Rafael Irizarry is a Professor of Biostatistics at the Harvard T.H. Chan School of Public Health and a Professor of Biostatistics and Computational Biology at the Dana Farber Cancer Institute.

Requirements

PH525.3xPH525.4x

How To Join This Course

  • Go to the course website link
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