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.
A ready to use VM can be downloaded from:
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
# Build STAR
$ make STAR
# Copy the STAR exe to the PATH
$ cp STAR /usr/bin
# Install samtools
$ sudo apt-get install samtools
$ 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
This is a introduction to how computational biology can help us to better understand molecular biology.
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.
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.
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.
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.
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.
In this section you can work with microarray data to understand how the differential expression analysis can be performed.
Now, you will be working with RNAseq data to perform differential expression analysis.
In this section you will learn how to create graphics to show your results from genomics data analysis.
In this section you will be able to use informations from transcriptome and genomic data to infer their influence on an entire biological system.
In this section you will be asked to search from public available online data. The topic must be on cancer, in special solid tumours. We have to create a proposal and do your own analysis. Finally, you have to create a report.