Classroom
mpertea [at] jhu.edu
@elapertea
EN 580.458/658 Computing the Transcriptome.
Description: This course introduces computational tools used in the field
of transcriptomics to analyze the genes and transcripts expressed in a
living cell. Lectures cover different practical ways to analyze large
data sets generated by high-throughput RNA sequencing (RNA-Seq) experiments,
including alignment, assembly, and quantification. The students will learn
how to use RNA-seq to answer questions such as: what is the complete set
of human genes? How do we reconstruct the splice variants that are
transcribed in different cell types and conditions? How do we compute
which genes are differentially expressed between different RNA-seq datasets?
Offered: Spring 2022-2025.
ME 710.736 Introduction to programming for DNA sequence analysis.
Description: This course teaches participants the basic concepts of
bioinformatics programming, and introduce them to computational tools
that deal with large amounts of data. The goals of the class is to
enable students to write basic programs in Perl, to adapt existing
programs to their needs, and to interface Perl programs with standard
bioinformatics packages such as BLAST and next-generation sequence
programs.
Offered: Spring 2012, Spring 2013.
Online instruction
Python for Genomic Data Science
Description: This course is part of the MOOC Genomic Big Data Science
Specialization offered by JHU through Coursera
(coursera.org). It provides an introduction to the Python
programming language to people who are not computer scientists but who
want to analyze genomic data. The course teaches participants basic
concepts of bioinformatics programming, and introduce them to
computational tools that deal with large amounts of data. The goals of
the class are to enable students to write basic programs in Python, to
adapt existing programs to their needs, and to interface Python programs
with various bioinformatics packages through the use of Biopython.
