Short Courses and Workshops
Hopkins faculty, staff, fellows and graduate students, as well as those outside the Hopkins community, are encouraged to join.
Workshops are being scheduled for Fall 2014; please check the list below for dates and times.
To register for courses, please send an email to email@example.com containing your name, interests and experience pertaining to the course(s) you are registering.
- Course Listing
- Subclonal Deconvolution and Phylogeny
- Statistics and Data Analysis using R -Course Package
- Introduction to Unix
- sed, AWK, and Bash Scripting
- Computational analysis of sequencing data
- Gene Expression Analysis
- High Throughput Biology
- Introduction to Python
Subclonal Deconvolution and Phylogeny
April 10, 12, and 14 3PM to 4PM
Locations: CRB1 3M42
Instructor: Rumen Kostadinov
Requirements: Laptopfor the hands-on session
In this course, Dr. Kostadinov will demonstrate clonal evolutionary dynamics within tumors using computer simulations  (see my example videos at http://www.rkostadi.org/wpress/?p=87). I will discuss how growth, mutation, selection, and tissue structure affect the evolution of subclones within a tumor. Next, I will discuss existing subclonal deconvolution methods and apply some of them on simulated data above. I will then discuss phylogeny inference (the so-called field of PhyloOncology ) in cancer data sets  and briefly demonstrate phylogeny inference using Phylip and PAUP.
1. Kostadinov, R., Maley, C. C., & Kuhner, M. K. (2016). Bulk Genotyping of Biopsies Can Create Spurious Evidence for Hetereogeneity in Mutation Content. PLoS Computational Biology, 12(4), e1004413. http://doi.org/10.1371/journal.pcbi.100441.
2. Somarelli, J. A., Ware, K. E., Kostadinov, et. al. (2016). PhyloOncology: Understanding cancer through phylogenetic analysis. Biochimica et Biophysica Acta (BBA) - Reviews on Cancer. http://doi.org/10.1016/j.bbcan.2016.10.006
3. Kostadinov, R., et. al. (2013). NSAIDs modulate clonal evolution in Barrett’s esophagus. PLoS Genetics, 9(6), e1003553. http://dx.doi.org/10.1371/journal.pgen.1003553.
To register, email: firstname.lastname@example.org.
Statistics and Data Analysis using R -Course: 510.707
10 sessions, on Wednesday and Friday of each week beginning Wednesday, 11/09/2016 and ending Friday, 12/16/2016
10AM to Noon
Location: PCTB 113 Conference Rm, on Friday, Nov. 11th ONLY class will be in PCTB 115 STILE
Instructors: Leslie Cope, Elana Fertig, and Chenguang Wang
Cost: JHU SOM tuition (Fall Semester)
Statistics and Data Analysis Using R is a hands-on introduction to the R statistical software suite for biomedical scientists. It is assumed that the student is familiar with the plots and statistical summaries that are most commonly used in biomedical papers, but no formal background in statistics or programming is necessary. The primary objective is learning to use R, but the course also emphasizes the standards of practice that programmers and data analysts have implemented to ensure transparency, accuracy and accountability. Students are required to have a laptop.
Please Contact the Johns Hopkins University School of Medicine Registrar's office to Register.
Understanding the Unix environment and interface is critical to using modern bioinformatics programs. This course will cover the basics of using Unix, including how to find help with any Unix command.
sed, AWK, and Bash Scripting
Prerequisite: Working experience with all the material covered in the 'Introduction to Unix' workshop"
One, 3 hour session
An introduction to the classic and essential Unix tools.
**Prior to attending the course, please download VirtualBox and our pre-built Ubuntu instance onto your laptop.
Computational analysis of sequencing data
Instructor: Ben Langmead
This two-hour lecture is an introduction to the array of computational methods, many new but some old, that underlie popular software used today. We will cover the computational ideas behind these tools, describe what makes them different from or similar to each other, and address questions on how to interpret their output.
Gene Expression Analysis
Requirements: Laptop and successful completion of the "Statistics and Data Analysis Using R" course Package.
This course will cover the basic concepts of genomic
analysis, and is designed for students with a background in biology and/or biostatistics, and interest in basic or clinical/translational research. The goal is to provide a general orientation and pointers to simple and effective methodologies for analyzing genomic data in these
Specific topics will include:
Part 1: Read and explore gene expression data
a) measurement technologies, preprocessing, and quality control;
Part 2: Differential gene expression analysis
a) gene annotation;
b) identification of features associated with phenotypes;
c) analysis by gene sets and pathway
High Throughput Biology
Instructors: Sarah Wheelan & Srinivasan (Vasan) Yegnasubramanian
As high throughput technologies (sequencing, microarrays, and more) grow in popularity, researchers are increasingly interested in what is available and how they can utilize these technologies in their own work. The class will briefly discuss the available technologies and some typical experimental designs, and will open the class to questions about the way technologies are used, how to design experiments, and how the technology may be used to address particular experimental questions.