Category: BINF 6203

Genome Comparison with Mauve

There are many ways to compare genomes, and these comparisons provide different kinds of information about evolutionary history and shared function. First, how do you decide which genes are “the same” across multiple genomes,...

Access and use genome track data

Genome browsers are designed to get different types of genome track data together using the common reference system of genomic coordinates. Often you’re more interested in manipulating whole sets of data, rather than just...

Sequence Read QC

DNA Sequencing is a continually evolving technology, with new platforms becoming available each year, all designed with the aim of reducing the cost and increasing the speed of large sequencing projects. As you can...

Prepare Your Computer

Prepare Your Computer

Many of the assignments in BINF 6203 are scaled so that you can complete them on a reasonably powerful laptop without using the University Research Computing cluster. That said, in order to do the...

BINF 6203: Read Mapping

Mapping short sequence reads to a reference sequence is a common task in genomics. Many different results can be extracted from a mapped sequence, depending on the original experimental design that produced the sequence...

Sidebar: DE with Tuxedo pipeline #usegalaxy

Sidebar: DE with Tuxedo pipeline #usegalaxy

To get around the problems with the software installs in the lab (and that some of you are having installing corset on your own) here is another way to get a full DE gene...

BINF 6203: Lab 10 — expression statistics

BINF 6203: Lab 10 — expression statistics

Last week you used transcriptome and read mapping tools to get read counts for your Vibrio vulnificus expression data, with two different pipelines. Now let’s analyze the data and get the top differentially expressed...

BINF 6203: Lab 9 — RNA-Seq read summarization

BINF 6203: Lab 9 — RNA-Seq read summarization

In this lab and the next, we are going to use two different methods to calculate differential expression for the same RNASeq dataset. In a nutshell, we have measured gene expression under two conditions...