Mapping with STAR

Objectives

In this lab, you will explore a faster splice aware mapper called STAR. Simulated RNA-seq data will be provided to you; the data contains 75 bp paired-end reads that have been generated in silico to replicate real gene count data from Drosophila. The data simulates two biological groups with three biological replicates per group (6 samples total). The objectives of this lab is mainly to:

Learn how STAR works and how to use it.

Introduction

STAR '‘Spliced Transcripts Alignment to a Reference" is a faster alternative for splice-aware read alignment. STAR can align the non-contiguous sequences directly to the genome. The STAR algorithm consists of two major steps: seed searching step and clustering/stitching/scoring step. STAR is more memory intensive (30 gb of RAM required for human genome as compared to ~5 gb required by hisat2), but it is fast.

Get your data

Six raw data files have been provided for all our further RNA-seq analysis:

c1_r1, c1_r2, c1_r3 from the first biological condition
c2_r1, c2_r2, and c2_r3 from the second biological condition

Get set up for the exercises

cdh
cd my_rnaseq_course/partB
cd star_exercise

Run STAR

#the below command will show you documentation about how to run STAR
STAR --help

Part 1. Create a index of your reference

NO NEED TO RUN THIS NOW- YOUR INDEX HAS ALREADY BEEN BUILT!

STAR --runMode genomeGenerate --genomeDir STAR_genome/ --genomeFastaFiles genome.fa --sjdbGTFfile genes.formatted.gtf --sjdbOverhang 74 --genomeChrBinNbits 14

Part 2. Align the samples to reference

We aren’t going to run this right now! Create a commands file.

If you wanted to run this, you’d execute the commands file.

chmod +x commands.star #change permissions to make it executable

nohup ./commands.star &

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