Pertea Lab

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.

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.