## Programação

• ### Installing the linux virtual machine (VM) or installing softwares on your own Linux machine

In order to follow this course, it's necessary to have a Unix based computer running specific softwares to manipulate raw sequencing data. Since you have the raw data processed and exported to a data frame in an table separated value file for example, you can go on using a R software installed on any operational system. So, to achieve your goals trough sections 1 to 3 is necessary to have this all sorted.

This is a Mint distribution from Linux, a Debian derivative, and all the following command lines were used to install all the features needed for this course. So, if you prefer to use your own Debian derivative system, you are able to install the same softwares versions.

# Get STAR source from git

$git clone https://github.com/alexdobin/STAR.git cd /STAR/source # Build STAR$ make STAR

# Copy the STAR exe to the PATH

$cp STAR /usr/bin # Install samtools$ sudo apt-get install samtools

#Install FastQC

$wget https://www.bioinformatics.babraham.ac.uojects/fastqc/fastqc_v0.11.5.zip$ unzip fastqc_v0.11.5.zip

$cd FastQC$ chmod 755 fastqc

\$ cp fasted /usr/bin

• ### Introduction to genomics and computational biology

This is a introduction to how computational biology can help us to better understand molecular biology.

• ### Basic training on Linux command line

In this section you will be learning the basic shell commands for Linux. Most command lines are compatible with other Unix based OS like Mas OS.

• ### Raw sequencing data

In this section we are talking about the raw data output from sequencer machines (fastq files), how to visualize them on your screen and how to perform quality control preparing files for alignment.

• ### Basic in RNA and DNA sequencing alignment

In this section you will have some hands-on in RNAseq and WES alignment using the command line in Linux. All the softwares may be installed in Mac OS and the command lines are quite similar.

• ### Basic training in R command line

In this section you will be learning basic functions in R. Here we show how to install packages and demonstrate the basic command line for manipulation variables, vectors, matrix and data frames. Additionally, you will be introduced to basic statistic functions.

• ### Building a SummarizedExperiement

In this section we are demonstrating how to build a SummarizedExperiment object. It is suitable for storing processed data particularly from high-throughout sequencing assay, and will be used for differential expression analysis.

• ### Differential expression analysis - microarray data

In this section you can work with microarray data to understand how the differential expression analysis can be performed.

• ### Differential expression analysis - RNAseq

Now, you will be working with RNAseq data to perform differential expression analysis.