Learn Bioinformatics Now

Our Focus

What makes this course unique?

No prior coding experience necessary

We assume no prior coding / computer science knowledge in this course. We go over everything “computer science-y” in a user friendly terminology

Realistic Lesson plans

If everyone could finish a course in one week then we would not need this course. We give you enough time to practice what you learn so that you retain what you learn


Content is customized based on the needs of our students. If students would rather study ChIP-Seq as opposed to Variant Calling, we do that.

Individual Attention

Keeping our class size under 15 students helps us give personalized attention to all students. You also get free tutoring after

About the Course

What you can expct to learn
We teach everything necessary to do your sequencing analysis

We start our course by talking about various sequencing platforms and go into detail about experiment design and sequencing technology selection.

In the CS module we introduce students to Linux and the Shell. Students learn about servers and job submission. We talk about sequencing data access and quality control. Students learn programming in R and basic scripting in Linux

Statistics module covers Significance testing (p-value), normalization methods and data distributions. We will discuss in detail about various normalization methods and their applicability for different types of data.

Bioinformatics module is the essence of this course. Having learnt basic computer science and statistics skills we will learn about open source tools (including R packages available in "BioConductor") that help us with specific analyses like ChIP/RNA-seq, Variant/SNP calling and Graphics.

  • Platforms, Merits of one vs. another, Sequencing background

  • Linux, Programming in R, Functions, Variables, Data Structures, File Systems, Scripting, Parallel Job Submission, FTP etc.

  • P-Value, Normalization techniques, Data distributions in the NGS context, Statistical Power

  • Read Alignment algorithms, Bioconductor packages in R, ChIP/RNA-Seq, Variant / SNP Calling, Genome Browsers, Graphics / Heatmaps, GSEA & Pathway Analysis



We've put together the best team to deliver the best instruction

Vishal Thapar, Ph.D.

Staff Scientist, Mass General Hospital Cancer Center, Department of Pathology, MGH
Dr. Thapar has a Ph.D. in Computer Science and a post doc in Computational Biology from Cold Spring Harbor Lab. He has worked on Cancer Genetics in Dr. Scott Lowe’s Lab at MSKCC and currently works with the Cancer Center and Department of pathology at MGH

Dr. David T. Ting

Assistant Physician Mass General Cancer Center Assistant Professor in Medicine Harvard Medical School
Ting Laboratory has been utilizing innovative microfluidic chip technologies to capture circulating tumor cells (CTCs) in the blood of pancreatic cancer patients as a means to understand why pancreatic cancers spread so quickly and as a potential non-invasive tool to diagnose our patients earlier.

Martin Aryee, Ph.D.

Assistant Professor, Department of Pathology, Massachusetts General Hospital & Harvard Medical School, Assistant Professor in the Department of Biostatistics, Harvard TH Chan School of Public Health, Associate Member, Broad Institute of Harvard and MIT
Martin received his PhD in Biostatistics from the Harvard School of Public Health in 2008, and completed a post-doctoral fellowship at the Johns Hopkins Sidney Kimmel Comprehensive Cancer Center.

Ben Wittner, Ph.D.

Staff Scientist, Mass General Hospital, Cancer Center
Dr. Wittner is a Staff Scientist in the Massachusetts General Hospital Center for Cancer Research and an Instructor in Medicine at the Harvard Medical School. His package on Clustering and Heatmaps has been used in many top tier journal publications
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