BCH709 Introduction to Bioinformatics: RNA-SEq

HPC Clusters

This exercise focuses on using HPC clusters for large-scale data analysis (e.g., Next Generation Sequencing, genome annotation, evolutionary studies). These clusters contain multiple processors with large amounts of RAM, making them ideal for computationally intensive tasks. The operating system is primarily UNIX, accessed via the command line. All the commands you’ve learned in previous exercises can be used here.

Pronghorn High Performance Computing offers shared infrastructure for researchers and students at UNR. You can find available resources here. Request access through your department or advisor. All attendees of this workshop will have their accounts set up on the HPC class education cluster using their UNR NetID and password. You should have received a confirmation email with connection instructions. This exercise covers connecting to a remote HPC server, transferring files, and running programs by requesting resources.

To log into the HPC front-end/job-submission system (pronghorn.rc.unr.edu), use your UNR NetID and password. Windows users will need an SSH client, while Mac/Linux users have SSH built-in.

ssh <YOUR_NET_ID>@pronghorn.rc.unr.edu
## First login will prompt for key confirmation. Choose 'yes.'

Pronghorn HPC Cluster Overview

Pronghorn is UNR’s new GPU-accelerated HPC cluster, supporting general research across NSHE. Comprising CPU, GPU, and storage subsystems, Pronghorn’s main features include:

Pronghorn is located at Switch Citadel Campus, 25 miles east of UNR. Switch is renowned for sustainable data center operations.

Pronghorn system map

Customizing the Bash Prompt for Pronghorn

echo '###BCH709' >> ~/.bashrc
echo 'tty -s && export PS1="\[\033[38;5;164m\]\u\[$(tput sgr0)\]\[\033[38;5;15m\] \[$(tput sgr0)\]\[\033[38;5;231m\]@\[$(tput sgr0)\]\[\033[38;5;15m\] \[$(tput sgr0)\]\[\033[38;5;2m\]\h\[$(tput sgr0)\]\[\033[38;5;15m\] \[$(tput sgr0)\]\[\033[38;5;172m\]\t\[$(tput sgr0)\]\[\033[38;5;15m\] \[$(tput sgr0)\]\[\033[38;5;2m\]\w\[$(tput sgr0)\]\[\033[38;5;15m\]\n \[$(tput sgr0)\]"' >> ~/.bashrc
echo "alias ls='ls --color=auto'" >> ~/.bashrc
source ~/.bashrc

File Transfer Methods

Several methods exist for transferring files to/from HPC clusters, including command-line tools (scp, rsync) and graphical clients (SCP, SFTP). For secure copying:

Transferring from Local to Remote System

scp <source_file> <username>@pronghorn.rc.unr.edu:<target_location>

Example:

mkdir ~/bch709
cd ~/bch709
echo "hello world" > test_uploading_file.txt
scp test_uploading_file.txt <username>@pronghorn.rc.unr.edu:~/

Transferring from Remote to Local System

scp <username>@pronghorn.rc.unr.edu:<source_file> <destination_file>

Example:

scp <username>@pronghorn.rc.unr.edu:~/test_downloading_file.txt ~/

Recursive Directory Transfer (Local to Remote)

scp -r <source_directory> <username>@pronghorn.rc.unr.edu:<target_directory>

Example:

scp -r ../bch709 <username>@pronghorn.rc.unr.edu:~/

Opening Location

Rsync Usage

Conda Installation on Pronghorn

Install Miniconda3 using the following commands:

curl -O https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh
~/miniconda3/bin/conda init

Follow the on-screen instructions to complete the installation.

Using Conda Environment

conda install mamba

mamba create -y -n RNASEQ_bch709 -c bioconda -c conda-forge sra-tools=3.1.1 minimap2 star trim-galore gffread seqkit samtools multiqc subread tree
conda activate RNASEQ_bch709

Environment create and installation in Pronghorn

# Create a new conda environment named "rnaseq".
# Add two channels to fetch the required packages:
# - bioconda: A channel specializing in bioinformatics software
# - conda-forge: A community-maintained collection of conda packages
-c bioconda -c conda-forge 

# List of packages/software to be installed in the "rnaseq" environment:
# - fastqc: A tool for quality control checks on raw sequence data
# - trim-galore: A wrapper tool around Cutadapt and FastQC to consistently apply adapter and quality trimming
# - hisat2: A fast and sensitive alignment program for mapping next-generation sequencing reads to a population of genomes
# - samtools: A suite of programs for interacting with high-throughput sequencing data
# - subread: A toolkit for processing next-gen sequencing read data, including feature counting
# - bioconductor-deseq2: A package for differential expression analysis based on the negative binomial distribution
# - bc: An arbitrary precision calculator language
# The "-y" flag allows the command to proceed without asking for user confirmation.



## Connecting Scratch Disk

```bash
mkdir /data/gpfs/assoc/bch709-5/students/$(whoami)
cd /data/gpfs/assoc/bch709-5/students/$(whoami)
ln -s /data/gpfs/assoc/bch709-5/students/$(whoami) ~/scratch

Job Submission with SBATCH

Create a job submission script named submit.sh:

nano submit.sh

Add the following content:

#!/bin/bash
#SBATCH --job-name=test
#SBATCH --mail-type=ALL
#SBATCH --mail-user=<YOUR_EMAIL>
#SBATCH --ntasks=1
#SBATCH --mem=1g
#SBATCH --time=8:10:00
#SBATCH --account=cpu-s5-bch709-5
#SBATCH --partition=cpu-core-0

for i in {1..1000}; do
  echo $i;
  sleep 1;
done

Submit the job:

chmod 775 submit.sh
sbatch submit.sh

To cancel the job:

scancel <JOB_ID>

SRA

Sequence Read Archive (SRA) data, available through multiple cloud providers and NCBI servers, is the largest publicly available repository of high throughput sequencing data. The archive accepts data from all branches of life as well as metagenomic and environmental surveys.

Searching the SRA: Searching the SRA can be complicated. Often a paper or reference will specify the accession number(s) connected to a dataset. You can search flexibly using a number of terms (such as the organism name) or the filters (e.g. DNA vs. RNA). The SRA Help Manual provides several useful explanations. It is important to know is that projects are organized and related at several levels, and some important terms include:

Bioproject: A BioProject is a collection of biological data related to a single initiative, originating from a single organization or from a consortium of coordinating organizations; see for example Bio Project 272719 Bio Sample: A description of the source materials for a project Run: These are the actual sequencing runs (usually starting with SRR); see for example SRR1761506

Publication (Arabidopsis)

Kim JS et al., “ROS1-Dependent DNA Demethylation Is Required for ABA-Inducible NIC3 Expression.”, Plant Physiol, 2019 Apr;179(4):1810-1821

SRA Bioproject site

https://www.ncbi.nlm.nih.gov/bioproject/PRJNA272719

Runinfo

Run ReleaseDate LoadDate spots bases spots_with_mates avgLength size_MB AssemblyName download_path Experiment LibraryName LibraryStrategy LibrarySelection LibrarySource LibraryLayout InsertSize InsertDev Platform Model SRAStudy BioProject Study_Pubmed_id ProjectID Sample BioSample SampleType TaxID ScientificName SampleName g1k_pop_code source g1k_analysis_group Subject_ID Sex Disease Tumor Affection_Status Analyte_Type Histological_Type Body_Site CenterName Submission dbgap_study_accession Consent RunHash ReadHash
SRR1761506 1/15/2016 15:51 1/15/2015 12:43 7379945 1490748890 7379945 202 899   https://sra-downloadb.be-md.ncbi.nlm.nih.gov/sos1/sra-pub-run-5/SRR1761506/SRR1761506.1 SRX844600   RNA-Seq cDNA TRANSCRIPTOMIC PAIRED 0 0 ILLUMINA Illumina HiSeq 2500 SRP052302 PRJNA272719 3 272719 SRS820503 SAMN03285048 simple 3702 Arabidopsis thaliana GSM1585887             no         GEO SRA232612   public F335FB96DDD730AC6D3AE4F6683BF234 12818EB5275BCB7BCB815E147BFD0619
SRR1761507 1/15/2016 15:51 1/15/2015 12:43 9182965 1854958930 9182965 202 1123   https://sra-downloadb.be-md.ncbi.nlm.nih.gov/sos1/sra-pub-run-5/SRR1761507/SRR1761507.1 SRX844601   RNA-Seq cDNA TRANSCRIPTOMIC PAIRED 0 0 ILLUMINA Illumina HiSeq 2500 SRP052302 PRJNA272719 3 272719 SRS820504 SAMN03285045 simple 3702 Arabidopsis thaliana GSM1585888             no         GEO SRA232612   public 00FD62759BF7BBAEF123BF5960B2A616 A61DCD3B96AB0796AB5E969F24F81B76
SRR1761508 1/15/2016 15:51 1/15/2015 12:47 19060611 3850243422 19060611 202 2324   https://sra-downloadb.be-md.ncbi.nlm.nih.gov/sos1/sra-pub-run-5/SRR1761508/SRR1761508.1 SRX844602   RNA-Seq cDNA TRANSCRIPTOMIC PAIRED 0 0 ILLUMINA Illumina HiSeq 2500 SRP052302 PRJNA272719 3 272719 SRS820505 SAMN03285046 simple 3702 Arabidopsis thaliana GSM1585889             no         GEO SRA232612   public B75A3E64E88B1900102264522D2281CB 657987ABC8043768E99BD82947608CAC
SRR1761509 1/15/2016 15:51 1/15/2015 12:51 16555739 3344259278 16555739 202 2016   https://sra-downloadb.be-md.ncbi.nlm.nih.gov/sos1/sra-pub-run-5/SRR1761509/SRR1761509.1 SRX844603   RNA-Seq cDNA TRANSCRIPTOMIC PAIRED 0 0 ILLUMINA Illumina HiSeq 2500 SRP052302 PRJNA272719 3 272719 SRS820506 SAMN03285049 simple 3702 Arabidopsis thaliana GSM1585890             no         GEO SRA232612   public 27CA2B82B69EEF56EAF53D3F464EEB7B 2B56CA09F3655F4BBB412FD2EE8D956C
SRR1761510 1/15/2016 15:51 1/15/2015 12:46 12700942 2565590284 12700942 202 1552   https://sra-downloadb.be-md.ncbi.nlm.nih.gov/sos1/sra-pub-run-5/SRR1761510/SRR1761510.1 SRX844604   RNA-Seq cDNA TRANSCRIPTOMIC PAIRED 0 0 ILLUMINA Illumina HiSeq 2500 SRP052302 PRJNA272719 3 272719 SRS820508 SAMN03285050 simple 3702 Arabidopsis thaliana GSM1585891             no         GEO SRA232612   public D3901795C7ED74B8850480132F4688DA 476A9484DCFCF9FFFDAADAAF4CE5D0EA
SRR1761511 1/15/2016 15:51 1/15/2015 12:44 13353992 2697506384 13353992 202 1639   https://sra-downloadb.be-md.ncbi.nlm.nih.gov/sos1/sra-pub-run-5/SRR1761511/SRR1761511.1 SRX844605   RNA-Seq cDNA TRANSCRIPTOMIC PAIRED 0 0 ILLUMINA Illumina HiSeq 2500 SRP052302 PRJNA272719 3 272719 SRS820507 SAMN03285047 simple 3702 Arabidopsis thaliana GSM1585892             no         GEO SRA232612   public 5078379601081319FCBF67C7465C404A E3B4195AFEA115ACDA6DEF6E4AA7D8DF
SRR1761512 1/15/2016 15:51 1/15/2015 12:44 8134575 1643184150 8134575 202 1067   https://sra-downloadb.be-md.ncbi.nlm.nih.gov/sos1/sra-pub-run-5/SRR1761512/SRR1761512.1 SRX844606   RNA-Seq cDNA TRANSCRIPTOMIC PAIRED 0 0 ILLUMINA Illumina HiSeq 2500 SRP052302 PRJNA272719 3 272719 SRS820509 SAMN03285051 simple 3702 Arabidopsis thaliana GSM1585893             no         GEO SRA232612   public DDB8F763B71B1E29CC9C1F4C53D88D07 8F31604D3A4120A50B2E49329A786FA6
SRR1761513 1/15/2016 15:51 1/15/2015 12:43 7333641 1481395482 7333641 202 960   https://sra-downloadb.be-md.ncbi.nlm.nih.gov/sos1/sra-pub-run-5/SRR1761513/SRR1761513.1 SRX844607   RNA-Seq cDNA TRANSCRIPTOMIC PAIRED 0 0 ILLUMINA Illumina HiSeq 2500 SRP052302 PRJNA272719 3 272719 SRS820510 SAMN03285053 simple 3702 Arabidopsis thaliana GSM1585894             no         GEO SRA232612   public 4068AE245EB0A81DFF02889D35864AF2 8E05C4BC316FBDFEBAA3099C54E7517B
SRR1761514 1/15/2016 15:51 1/15/2015 12:44 6160111 1244342422 6160111 202 807   https://sra-downloadb.be-md.ncbi.nlm.nih.gov/sos1/sra-pub-run-5/SRR1761514/SRR1761514.1 SRX844608   RNA-Seq cDNA TRANSCRIPTOMIC PAIRED 0 0 ILLUMINA Illumina HiSeq 2500 SRP052302 PRJNA272719 3 272719 SRS820511 SAMN03285059 simple 3702 Arabidopsis thaliana GSM1585895             no         GEO SRA232612   public 0A1F3E9192E7F9F4B3758B1CE514D264 81BFDB94C797624B34AFFEB554CE4D98
SRR1761515 1/15/2016 15:51 1/15/2015 12:44 7988876 1613752952 7988876 202 1048   https://sra-downloadb.be-md.ncbi.nlm.nih.gov/sos1/sra-pub-run-5/SRR1761515/SRR1761515.1 SRX844609   RNA-Seq cDNA TRANSCRIPTOMIC PAIRED 0 0 ILLUMINA Illumina HiSeq 2500 SRP052302 PRJNA272719 3 272719 SRS820512 SAMN03285054 simple 3702 Arabidopsis thaliana GSM1585896             no         GEO SRA232612   public 39B37A0BD484C736616C5B0A45194525 85B031D74DF90AD1815AA1BBBF1F12BD
SRR1761516 1/15/2016 15:51 1/15/2015 12:44 8770090 1771558180 8770090 202 1152   https://sra-downloadb.be-md.ncbi.nlm.nih.gov/sos1/sra-pub-run-5/SRR1761516/SRR1761516.1 SRX844610   RNA-Seq cDNA TRANSCRIPTOMIC PAIRED 0 0 ILLUMINA Illumina HiSeq 2500 SRP052302 PRJNA272719 3 272719 SRS820514 SAMN03285055 simple 3702 Arabidopsis thaliana GSM1585897             no         GEO SRA232612   public E4728DFBF0F9F04B89A5B041FA570EB3 B96545CB9C4C3EE1C9F1E8B3D4CE9D24
SRR1761517 1/15/2016 15:51 1/15/2015 12:44 8229157 1662289714 8229157 202 1075   https://sra-downloadb.be-md.ncbi.nlm.nih.gov/sos1/sra-pub-run-5/SRR1761517/SRR1761517.1 SRX844611   RNA-Seq cDNA TRANSCRIPTOMIC PAIRED 0 0 ILLUMINA Illumina HiSeq 2500 SRP052302 PRJNA272719 3 272719 SRS820513 SAMN03285058 simple 3702 Arabidopsis thaliana GSM1585898             no         GEO SRA232612   public C05BC519960B075038834458514473EB 4EF7877FC59FF5214DBF2E2FE36D67C5
SRR1761518 1/15/2016 15:51 1/15/2015 12:44 8760931 1769708062 8760931 202 1072   https://sra-downloadb.be-md.ncbi.nlm.nih.gov/sos1/sra-pub-run-5/SRR1761518/SRR1761518.1 SRX844612   RNA-Seq cDNA TRANSCRIPTOMIC PAIRED 0 0 ILLUMINA Illumina HiSeq 2500 SRP052302 PRJNA272719 3 272719 SRS820515 SAMN03285052 simple 3702 Arabidopsis thaliana GSM1585899             no         GEO SRA232612   public 7D8333182062545CECD5308A222FF506 382F586C4BF74E474D8F9282E36BE4EC
SRR1761519 1/15/2016 15:51 1/15/2015 12:44 6643107 1341907614 6643107 202 811   https://sra-downloadb.be-md.ncbi.nlm.nih.gov/sos1/sra-pub-run-5/SRR1761519/SRR1761519.1 SRX844613   RNA-Seq cDNA TRANSCRIPTOMIC PAIRED 0 0 ILLUMINA Illumina HiSeq 2500 SRP052302 PRJNA272719 3 272719 SRS820516 SAMN03285056 simple 3702 Arabidopsis thaliana GSM1585900             no         GEO SRA232612   public 163BD8073D7E128D8AD1B253A722DD08 DFBCC891EB5FA97490E32935E54C9E14
SRR1761520 1/15/2016 15:51 1/15/2015 12:44 8506472 1718307344 8506472 202 1040   https://sra-downloadb.be-md.ncbi.nlm.nih.gov/sos1/sra-pub-run-5/SRR1761520/SRR1761520.1 SRX844614   RNA-Seq cDNA TRANSCRIPTOMIC PAIRED 0 0 ILLUMINA Illumina HiSeq 2500 SRP052302 PRJNA272719 3 272719 SRS820517 SAMN03285062 simple 3702 Arabidopsis thaliana GSM1585901             no         GEO SRA232612   public 791BD0D8840AA5F1D74E396668638DA1 AF4694425D34F84095F6CFD6F4A09936
SRR1761521 1/15/2016 15:51 1/15/2015 12:46 13166085 2659549170 13166085 202 1609   https://sra-downloadb.be-md.ncbi.nlm.nih.gov/sos1/sra-pub-run-5/SRR1761521/SRR1761521.1 SRX844615   RNA-Seq cDNA TRANSCRIPTOMIC PAIRED 0 0 ILLUMINA Illumina HiSeq 2500 SRP052302 PRJNA272719 3 272719 SRS820518 SAMN03285057 simple 3702 Arabidopsis thaliana GSM1585902             no         GEO SRA232612   public 47C40480E9B7DB62B4BEE0F2193D16B3 1443C58A943C07D3275AB12DC31644A9
SRR1761522 1/15/2016 15:51 1/15/2015 12:49 9496483 1918289566 9496483 202 1162   https://sra-downloadb.be-md.ncbi.nlm.nih.gov/sos1/sra-pub-run-5/SRR1761522/SRR1761522.1 SRX844616   RNA-Seq cDNA TRANSCRIPTOMIC PAIRED 0 0 ILLUMINA Illumina HiSeq 2500 SRP052302 PRJNA272719 3 272719 SRS820519 SAMN03285061 simple 3702 Arabidopsis thaliana GSM1585903             no         GEO SRA232612   public BB05DF11E1F95427530D69DB5E0FA667 7706862FB2DF957E4041D2064A691CF6
SRR1761523 1/15/2016 15:51 1/15/2015 12:46 14999315 3029861630 14999315 202 1832   https://sra-downloadb.be-md.ncbi.nlm.nih.gov/sos1/sra-pub-run-5/SRR1761523/SRR1761523.1 SRX844617   RNA-Seq cDNA TRANSCRIPTOMIC PAIRED 0 0 ILLUMINA Illumina HiSeq 2500 SRP052302 PRJNA272719 3 272719 SRS820520 SAMN03285060 simple 3702 Arabidopsis thaliana GSM1585904             no         GEO SRA232612   public 101D3A151E632224C09A702BD2F59CF5 0AC99FAA6B8941F89FFCBB8B1910696E

Subset of data

Sample information Run
WT_rep1 SRR1761506
WT_rep2 SRR1761507
WT_rep3 SRR1761508
ABA_rep1 SRR1761509
ABA_rep2 SRR1761510
ABA_rep3 SRR1761511

SRA Data Access

SRA (Sequence Read Archive) is a repository of high-throughput sequencing data. To download sequencing data from SRA, use fastq-dump.

preparing Fastq-Dump Job

Create and submit a script fastq-dump.sh to download RNA-Seq data:

mkdir ~/scratch/raw_data
cd ~/scratch/raw_data
nano fastq-dump.sh

#!/bin/bash
#SBATCH --job-name=fastqdump_ATH
#SBATCH --cpus-per-task=2
#SBATCH --time=2-15:00:00
#SBATCH --mem=16g
#SBATCH --mail-type=ALL
#SBATCH --mail-user=<YOUR_EMAIL>
#SBATCH -o fastq-dump.out
#SBATCH --account=cpu-s5-bch709-5
#SBATCH --partition=cpu-core-0

fastq-dump SRR1761506 --split-3 --outdir /data/gpfs/assoc/bch709-5/students/$(whoami)/raw_data --gzip
fastq-dump SRR1761507 --split-3 --outdir /data/gpfs/assoc/bch709-5/students/$(whoami)/raw_data --gzip
fastq-dump SRR1761508 --split-3 --outdir /data/gpfs/assoc/bch709-5/students/$(whoami)/raw_data --gzip
fastq-dump SRR1761509 --split-3 --outdir /data/gpfs/assoc/bch709-5/students/$(whoami)/raw_data --gzip
fastq-dump SRR1761510 --split-3 --outdir /data/gpfs/assoc/bch709-5/students/$(whoami)/raw_data --gzip
fastq-dump SRR1761511 --split-3 --outdir /data/gpfs/assoc/bch709-5/students/$(whoami)/raw_data --gzip

submit Fastq-Dump Job

sbatch fastq-dump.sh
squeue

Explain the command

This command uses the `fastq-dump` tool from the SRA (Sequence Read Archive) Toolkit to download and process sequencing data associated with the accession number **SRR1761510**. Let’s break it down:

1. **`fastq-dump SRR1761510`**: This part tells `fastq-dump` to retrieve the sequencing data associated with the accession **SRR1761510**. Accession numbers like these correspond to specific datasets available in the NCBI SRA database.

2. **`--split-3`**: This option splits paired-end reads into separate files (for example, `_1` for the first read in a pair and `_2` for the second read). If there are also unpaired reads, they’ll be stored in a separate file as well.

3. **`--outdir /data/gpfs/assoc/bch709-5/students/$(whoami)/raw_data`**: The `--outdir` option specifies the output directory for the downloaded FASTQ files. The path:

/data/gpfs/assoc/bch709-5/students/$(whoami)/raw_data

   
   includes `$(whoami)`, which dynamically inserts the current username. This makes sure each user has their own dedicated output directory under `raw_data`.

4. **`--gzip`**: This option compresses the output FASTQ files in **GZIP** format, saving storage space and making downstream data handling more efficient.

In summary, this command will download the sequencing data for **SRR1761510** from SRA, split the paired-end reads, and save them in a specific directory with GZIP compression.

Trimming Reads with Trim-Galore

Submit a trimming job:

mkdir  -p ~/scratch/RNA-Seq_example/ATH/trim
cd  ~/scratch/RNA-Seq_example/ATH
nano trim.sh

#!/bin/bash
#SBATCH --job-name=trim_ATH
#SBATCH --cpus-per-task=2
#SBATCH --time=2-15:00:00
#SBATCH --mem=16g
#SBATCH --mail-type=ALL
#SBATCH --mail-user=<YOUR_EMAIL>
#SBATCH -o trim.out
#SBATCH --account=cpu-s5-bch709-5
#SBATCH --partition=cpu-core-0

trim_galore --paired   --three_prime_clip_R1 5 --three_prime_clip_R2 5 --cores 2  --max_n 40  --gzip -o  ~/scratch/RNA-Seq_example/ATH/trim --basename SRR1761506 raw_data/SRR1761506_1.fastq.gz raw_data/SRR1761506_2.fastq.gz --fastqc
trim_galore --paired   --three_prime_clip_R1 5 --three_prime_clip_R2 5 --cores 2  --max_n 40  --gzip -o  ~/scratch/RNA-Seq_example/ATH/trim --basename SRR1761507  raw_data/SRR1761507_1.fastq.gz raw_data/SRR1761507_2.fastq.gz --fastqc
trim_galore --paired   --three_prime_clip_R1 5 --three_prime_clip_R2 5 --cores 2  --max_n 40  --gzip -o  ~/scratch/RNA-Seq_example/ATH/trim --basename SRR1761508 raw_data/SRR1761508_1.fastq.gz raw_data/SRR1761508_2.fastq.gz --fastqc
trim_galore --paired   --three_prime_clip_R1 5 --three_prime_clip_R2 5 --cores 2  --max_n 40  --gzip -o  ~/scratch/RNA-Seq_example/ATH/trim --basename SRR1761509 raw_data/SRR1761509_1.fastq.gz raw_data/SRR1761509_2.fastq.gz --fastqc
trim_galore --paired   --three_prime_clip_R1 5 --three_prime_clip_R2 5 --cores 2  --max_n 40  --gzip -o  ~/scratch/RNA-Seq_example/ATH/trim --basename SRR1761510 raw_data/SRR1761510_1.fastq.gz raw_data/SRR1761510_2.fastq.gz --fastqc
trim_galore --paired   --three_prime_clip_R1 5 --three_prime_clip_R2 5 --cores 2  --max_n 40  --gzip -o  ~/scratch/RNA-Seq_example/ATH/trim --basename SRR1761511 raw_data/SRR1761511_1.fastq.gz raw_data/SRR1761511_2.fastq.gz --fastqc

Submit trimming

squeue -u $(whoami)
sbatch --dependency=afterok:######## trim.sh

Explain the command

This command runs **Trim Galore**, a tool used for trimming adapters and low-quality sequences from high-throughput sequencing data, with specific parameters for paired-end RNA-Seq reads. Here’s a detailed breakdown:

1. **`trim_galore --paired`**: This specifies that the input data consists of paired-end reads. Trim Galore will process both forward and reverse reads together, ensuring paired-end compatibility after trimming.

2. **`--three_prime_clip_R1 5` and `--three_prime_clip_R2 5`**: These options clip (remove) 5 bases from the 3' end of both reads in the pair. **`_R1`** applies to the first read and **`_R2`** applies to the second read, which can help remove low-quality or unwanted bases at the end of each read.

3. **`--cores 2`**: This specifies the number of cores (CPUs) to use, allowing Trim Galore to run with 2 parallel threads for faster processing.

4. **`--max_n 40`**: This sets the maximum number of ambiguous bases (`N`) allowed per read. Reads with more than 40 `N` bases will be discarded, helping improve the quality of the data.

5. **`--gzip`**: This option compresses the output files in **GZIP** format, saving space and making downstream handling more efficient.

6. **`-o ~/scratch/RNA-Seq_example/ATH/trim`**: This specifies the output directory for the processed files. Here, the results will be saved in `~/scratch/RNA-Seq_example/ATH/trim`.

7. **`--basename SRR1761511`**: This sets the base name for the output files, which will start with **SRR1761511**. This is useful for organizing results by sample name.

8. **`raw_data/SRR1761511_1.fastq.gz raw_data/SRR1761511_2.fastq.gz`**: These are the input files: the paired-end FASTQ files (forward and reverse) that will be trimmed.

9. **`--fastqc`**: This tells Trim Galore to run **FastQC** on the trimmed reads, generating a quality control report. FastQC provides information on read quality, adapter content, and other metrics.

### In summary:

This command will trim adapters and low-quality bases from paired-end RNA-Seq reads in **SRR1761511**, remove excess ambiguous bases, clip 5 bases from the 3' ends of both reads, and save the results (compressed) to the specified output directory with **FastQC** quality reports. This helps prepare high-quality reads for further analysis.

Trim the reads

Trimming

Cutadapt
fastp
Skewer
Prinseq
Trimmomatics
Trim Galore

Download reference file

cd ~/scratch/RNA-Seq_example/ATH

mkdir bam
mkdir reference
cd reference

wget https://www.arabidopsis.org/api/download-files/download?filePath=Genes/TAIR10_genome_release/TAIR10_chromosome_files/TAIR10_chr_all.fas.gz -O TAIR10_chr_all.fas.gz --no-check-certificate

wget https://www.arabidopsis.org/api/download-files/download?filePath=Genes/TAIR10_genome_release/TAIR10_gff3/TAIR10_GFF3_genes.gff  -O TAIR10_GFF3_genes.gff --no-check-certificate

seqkit stats TAIR10_chr_all.fas.gz

Explain the command

This command uses **wget** to download a GFF3 file from the Arabidopsis Information Resource (TAIR) website. Here’s what each part does:

1. **`wget https://www.arabidopsis.org/api/download-files/download?filePath=Genes/TAIR10_genome_release/TAIR10_gff3/TAIR10_GFF3_genes.gff`**: This is the download command with the full URL of the TAIR10 GFF3 file. **GFF3** (General Feature Format, version 3) files are used for genome annotations and contain information about gene locations, exons, coding regions, and other genomic features.

2. **`-O TAIR10_GFF3_genes.gff`**: This option specifies the output filename. Without this option, `wget` would use the default name provided by the URL, which might be complex or include additional characters. **TAIR10_GFF3_genes.gff** is set as the filename to make it easier to reference.

3. **`--no-check-certificate`**: This option tells `wget` to ignore SSL certificate verification errors. It can be useful when a website’s SSL certificate is expired or not properly recognized. Here, it ensures the download proceeds without interruptions.

### Summary:

This command downloads the **TAIR10_GFF3_genes.gff** file from the TAIR database without SSL verification, saving it as **TAIR10_GFF3_genes.gff**. This GFF3 file will contain essential genome annotation data for Arabidopsis, commonly used in genomic analysis.

Convert GFF to GTF

cd ~/scratch/RNA-Seq_example/ATH/reference
gffread TAIR10_GFF3_genes.gff -T -F --keep-exon-attrs -o TAIR10_GFF3_genes.gtf

Explain the command

This command uses **gffread** to convert a **GFF3** file into a **GTF** file format. **GTF** (Gene Transfer Format) is similar to **GFF** but often preferred in certain bioinformatics tools, especially for transcript-based analyses. Here’s a breakdown of each component:

1. **`gffread TAIR10_GFF3_genes.gff`**: This specifies the input file in **GFF3** format, here **TAIR10_GFF3_genes.gff**.

2. **`-T`**: This option tells `gffread` to output the file in **GTF** format instead of **GFF3**.

3. **`-F`**: This forces the inclusion of features that might be incomplete or missing certain attributes (e.g., lacking start or stop codons). It ensures that all exons are included in the output.

4. **`--keep-exon-attrs`**: This option retains additional attributes associated with exon features, which are often discarded in standard conversions. Keeping exon attributes can be valuable for certain analyses where more detailed annotation is needed.

5. **`-o TAIR10_GFF3_genes.gtf`**: This specifies the output filename. Here, the GTF-formatted file will be saved as **TAIR10_GFF3_genes.gtf**.

### Summary:

This command converts the **TAIR10** genome annotation file from **GFF3** to **GTF** format, ensuring that all exons are retained along with their attributes, even if they lack some standard annotations. This output file, **TAIR10_GFF3_genes.gtf**, will be useful for downstream transcript-based analysis in pipelines or tools that prefer GTF format.

Create reference index

cd  ~/scratch/RNA-Seq_example/ATH/reference
ls -algh
gunzip TAIR10_chr_all.fas.gz

nano index.sh

#!/bin/bash
#SBATCH --job-name=index_ATH
#SBATCH --cpus-per-task=12
#SBATCH --time=2-15:00:00
#SBATCH --mem=48g
#SBATCH --mail-type=all
#SBATCH --mail-user=<PLEASE CHANGE THIS TO YOUR EMAIL>
#SBATCH -o index.out # STDOUT & STDERR
#SBATCH --account=cpu-s5-bch709-5
#SBATCH --partition=cpu-core-0

STAR  --runThreadN 48g --runMode genomeGenerate --genomeDir . --genomeFastaFiles  ~/scratch/RNA-Seq_example/ATH/reference/TAIR10_chr_all.fas --sjdbGTFfile ~/scratch/RNA-Seq_example/ATH/reference/TAIR10_GFF3_genes.gtf --sjdbOverhang 99   --genomeSAindexNbases 12

Explain the command

This command uses **STAR** (Spliced Transcripts Alignment to a Reference) to generate a genome index, which is a critical step for efficiently aligning RNA-Seq reads to a reference genome. Here’s a detailed breakdown of each parameter:

1. **`STAR`**: This calls the STAR program, which is an RNA-Seq read aligner optimized for high accuracy and speed.

2. **`--runThreadN 48g`**: This option specifies the number of threads (CPUs) STAR should use. However, the argument should be an integer (like `48`) rather than `48g`. Assuming you intended to use 48 threads, the correct syntax would be `--runThreadN 48`.

3. **`--runMode genomeGenerate`**: This tells STAR to run in genome generation mode, which creates an index for the reference genome. This index is needed for subsequent alignment steps.

4. **`--genomeDir .`**: This sets the directory where the generated genome index files will be stored. Using `.` specifies the current directory.

5. **`--genomeFastaFiles ~/scratch/RNA-Seq_example/ATH/reference/TAIR10_chr_all.fas`**: This provides the path to the reference genome FASTA file. Here, **TAIR10_chr_all.fas** contains the reference sequences for **Arabidopsis thaliana**.

6. **`--sjdbGTFfile ~/scratch/RNA-Seq_example/ATH/reference/TAIR10_GFF3_genes.gtf`**: This specifies the path to the annotation file in **GTF** format (TAIR10_GFF3_genes.gtf). STAR uses this file to incorporate known splice junctions into the index, which improves alignment accuracy, especially for spliced reads in RNA-Seq data.

7. **`--sjdbOverhang 99`**: This defines the length of the sequence to be used for junctions. Ideally, it should be set to the length of the read minus 1. For instance, if the RNA-Seq reads are 100 bp, `sjdbOverhang` should be 99. This value helps STAR optimize the alignment of reads that span splice junctions.

8. **`--genomeSAindexNbases 12`**: This parameter controls the size of the suffix array index used by STAR. **12** is typical for a smaller genome (such as Arabidopsis) to balance between memory usage and indexing speed. Larger values reduce memory requirements but can slow down the alignment step slightly.

### Summary:

This command sets STAR to generate a genome index for **Arabidopsis thaliana** using 48 threads, based on the reference FASTA and GTF files. The generated index will be saved in the current directory and includes splice junction information, which is crucial for accurately mapping RNA-Seq reads that contain introns. This index will be used in future alignment steps to align RNA-Seq reads quickly and accurately to the genome.

Mapping the reads to genome index

cd  ~/scratch/RNA-Seq_example/ATH/
ls -algh
nano align.sh

#!/bin/bash
#SBATCH --job-name=align_ATH
#SBATCH --cpus-per-task=8
#SBATCH --time=2-15:00:00
#SBATCH --mem=32g
#SBATCH --mail-type=all
#SBATCH --mail-user=<PLEASE CHANGE THIS TO YOUR EMAIL>
#SBATCH -o align.out # STDOUT & STDERR
#SBATCH --account=cpu-s5-bch709-5
#SBATCH --partition=cpu-core-0

STAR --runMode alignReads --runThreadN 8 --readFilesCommand zcat --outFilterMultimapNmax 10 --alignIntronMin 25 --alignIntronMax 10000 --genomeDir ~/scratch/RNA-Seq_example/ATH/reference/ --readFilesIn ~/scratch/RNA-Seq_example/ATH/trim/SRR1761506_val_1.fq.gz ~/scratch/RNA-Seq_example/ATH/trim/SRR1761506_val_2.fq.gz --outSAMtype BAM SortedByCoordinate --outFileNamePrefix ~/scratch/RNA-Seq_example/ATH/bam/SRR1761506.bam

STAR --runMode alignReads --runThreadN 8 --readFilesCommand zcat --outFilterMultimapNmax 10 --alignIntronMin 25 --alignIntronMax 10000 --genomeDir ~/scratch/RNA-Seq_example/ATH/reference/ --readFilesIn ~/scratch/RNA-Seq_example/ATH/trim/SRR1761507_val_1.fq.gz ~/scratch/RNA-Seq_example/ATH/trim/SRR1761507_val_2.fq.gz --outSAMtype BAM SortedByCoordinate --outFileNamePrefix ~/scratch/RNA-Seq_example/ATH/bam/SRR1761507.bam

STAR --runMode alignReads --runThreadN 8 --readFilesCommand zcat --outFilterMultimapNmax 10 --alignIntronMin 25 --alignIntronMax 10000 --genomeDir ~/scratch/RNA-Seq_example/ATH/reference/ --readFilesIn ~/scratch/RNA-Seq_example/ATH/trim/SRR1761508_val_1.fq.gz ~/scratch/RNA-Seq_example/ATH/trim/SRR1761508_val_2.fq.gz --outSAMtype BAM SortedByCoordinate --outFileNamePrefix ~/scratch/RNA-Seq_example/ATH/bam/SRR1761508.bam

STAR --runMode alignReads --runThreadN 8 --readFilesCommand zcat --outFilterMultimapNmax 10 --alignIntronMin 25 --alignIntronMax 10000 --genomeDir ~/scratch/RNA-Seq_example/ATH/reference/ --readFilesIn ~/scratch/RNA-Seq_example/ATH/trim/SRR1761509_val_1.fq.gz ~/scratch/RNA-Seq_example/ATH/trim/SRR1761509_val_2.fq.gz --outSAMtype BAM SortedByCoordinate --outFileNamePrefix ~/scratch/RNA-Seq_example/ATH/bam/SRR1761509.bam

STAR --runMode alignReads --runThreadN 8 --readFilesCommand zcat --outFilterMultimapNmax 10 --alignIntronMin 25 --alignIntronMax 10000 --genomeDir ~/scratch/RNA-Seq_example/ATH/reference/ --readFilesIn ~/scratch/RNA-Seq_example/ATH/trim/SRR1761510_val_1.fq.gz ~/scratch/RNA-Seq_example/ATH/trim/SRR1761510_val_2.fq.gz --outSAMtype BAM SortedByCoordinate --outFileNamePrefix ~/scratch/RNA-Seq_example/ATH/bam/SRR1761510.bam

STAR --runMode alignReads --runThreadN 8 --readFilesCommand zcat --outFilterMultimapNmax 10 --alignIntronMin 25 --alignIntronMax 10000 --genomeDir ~/scratch/RNA-Seq_example/ATH/reference/ --readFilesIn ~/scratch/RNA-Seq

Submit mapping

squeue -u $(whoami)
sbatch --dependency=afterok:########:####### align.sh

Explain the command

This command uses **STAR** in **alignment mode** to align paired-end RNA-Seq reads to a pre-built genome index. Here’s a detailed breakdown:

1. **`STAR --runMode alignReads`**: Specifies that STAR should run in **alignment mode**, which aligns RNA-Seq reads to the reference genome.

2. **`--runThreadN 8`**: Sets the number of threads to use (in this case, 8), which will speed up the alignment process by utilizing multiple CPU cores.

3. **`--readFilesCommand zcat`**: Instructs STAR to use `zcat` to decompress the input files since they are **GZIP** compressed (`.fq.gz` format).

4. **`--outFilterMultimapNmax 10`**: Sets the maximum number of loci a read can map to. If a read maps to more than 10 locations, it will be discarded. This helps control the level of ambiguity in alignment, especially in repetitive regions.

5. **`--alignIntronMin 25`**: Specifies the minimum allowed length for introns. STAR will ignore introns shorter than 25 bp, which reduces false alignments in smaller repetitive regions.

6. **`--alignIntronMax 10000`**: Sets the maximum allowed intron length to 10,000 bp, accommodating typical intron lengths found in **Arabidopsis**. This helps STAR avoid spurious alignments that would involve unusually long gaps.

7. **`--genomeDir ~/scratch/RNA-Seq_example/ATH/reference/`**: Specifies the directory containing the STAR genome index, created in the previous genome generation step.

8. **`--readFilesIn ~/scratch/RNA-Seq_example/ATH/trim/SRR1761506_val_1.fq.gz ~/scratch/RNA-Seq_example/ATH/trim/SRR1761506_val_2.fq.gz`**: These are the paths to the input FASTQ files for paired-end reads (forward and reverse), which have been trimmed and compressed.

9. **`--outSAMtype BAM SortedByCoordinate`**: Specifies that the output should be in **BAM** format and sorted by genomic coordinates. BAM is a binary, compressed format for alignment data, commonly used for downstream analysis.

10. **`--outFileNamePrefix ~/scratch/RNA-Seq_example/ATH/bam/SRR1761506.bam`**: Sets the output filename prefix, so the results will be saved with the prefix `SRR1761506.bam` in the specified BAM directory. STAR will automatically append additional information as needed.

### Summary:

This command aligns the trimmed, paired-end RNA-Seq reads for **SRR1761506** to the **Arabidopsis** reference genome, utilizing 8 threads. It decompresses the input files, filters out highly multimapping reads, and limits intron size for optimized mapping. The output is saved in sorted **BAM** format, ready for downstream analysis.

BW algorithm

algorithm banana banana

####################################

Mouse RNA-Seq

https://www.sciencedirect.com/science/article/pii/S2211124722011111

Benraiss A et al., “A TCF7L2-responsive suppression of both homeostatic and compensatory remyelination in Huntington disease mice.”, Cell Rep, 2022 Aug 30;40(9):111291

Working directory (Pronghorn)

echo $USER

cd /data/gpfs/assoc/bch709-5/students/${USER}

mkdir mouse
mkdir /data/gpfs/assoc/bch709-5/students/${USER}/mouse/fastq
mkdir /data/gpfs/assoc/bch709-5/students/${USER}/mouse/ref 
mkdir /data/gpfs/assoc/bch709-5/students/${USER}/mouse/trim
mkdir /data/gpfs/assoc/bch709-5/students/${USER}/mouse/bam
mkdir /data/gpfs/assoc/bch709-5/students/${USER}/mouse/readcount
mkdir /data/gpfs/assoc/bch709-5/students/${USER}/mouse/DEG

Reference Download

https://hgdownload.soe.ucsc.edu/downloads.html

### change working directory
cd /data/gpfs/assoc/bch709-5/students/${USER}/mouse/ref 

### download
wget https://hgdownload.soe.ucsc.edu/goldenPath/mm39/bigZips/mm39.fa.gz
wget https://hgdownload.soe.ucsc.edu/goldenPath/mm39/bigZips/genes/refGene.gtf.gz

### decompress
gunzip mm39.fa.gz
gunzip refGene.gtf.gz


### index
nano index.sh

#!/bin/bash
#SBATCH --job-name=index_mouse
#SBATCH --cpus-per-task=12
#SBATCH --time=2-15:00:00
#SBATCH --mem=48g
#SBATCH --mail-type=all
#SBATCH --mail-user=<PLEASE CHANGE THIS TO YOUR EMAIL>
#SBATCH -o index.out # STDOUT & STDERR
#SBATCH --account=cpu-s5-bch709-5
#SBATCH --partition=cpu-core-0

STAR  --runThreadN 48g --runMode genomeGenerate --genomeDir . --genomeFastaFiles  /data/gpfs/assoc/bch709-5/students/${USER}/mouse/ref/mm39.fa  --sjdbGTFfile /data/gpfs/assoc/bch709-5/students/${USER}/mouse/ref/refGene.gtf --sjdbOverhang 99   --genomeSAindexNbases 12

FASTQ file

cd /data/gpfs/assoc/bch709-5/students/${USER}/mouse/

### Link file (without copy)
ln -s /data/gpfs/assoc/bch709-5/students/Course_materials/mouse/fastq/* /data/gpfs/assoc/bch709-5/students/${USER}/mouse/fastq

ls /data/gpfs/assoc/bch709-5/students/${USER}/mouse/fastq

Create file list

cd /data/gpfs/assoc/bch709-5/students/${USER}/mouse/fastq

ls -1 *.gz | sed 's/_R.\.fastq\.gz//g' | sort -u > /data/gpfs/assoc/bch709-5/students/${USER}/mouse/filelist

cat /data/gpfs/assoc/bch709-5/students/${USER}/mouse/filelist

Regular expression

https://regex101.com/

Trim reads

trim_galore --paired  --three_prime_clip_R1 [integer] --three_prime_clip_R2 [integer]  --cores [integer]   --max_n [integer]   --fastqc --gzip -o /data/gpfs/assoc/bch709-5/students/${USER}/mouse/trim {READ_R1} {READ_R2}

Trim reads example

trim_galore --paired  --three_prime_clip_R1 5 --three_prime_clip_R2 5 --cores 2  --max_n 40  --fastqc --gzip -o /data/gpfs/assoc/bch709-5/students/${USER}/mouse/trim {READ_R1} {READ_R2}

Prepare templet

cat /data/gpfs/assoc/bch709-5/students/Course_materials/mouse/run.sh | sed "s/\-\-cpus\-per\-task\=2/\-\-cpus\-per\-task\=4/g; s/\[NAME\]/Trim/g; s/\[youremail\]/${USER}\@unr.edu\,${USER}\@nevada.unr.edu/g"  > /data/gpfs/assoc/bch709-5/students/${USER}/mouse/fastq/trim.sh

Edit templet

nano /data/gpfs/assoc/bch709-5/students/${USER}/mouse/fastq/trim.sh

Batch submission

# Check file list
cat ../filelist


# Loop file list
### Add Forward read to variable
### Add reverse read from forward read name substitution

for i in `cat ../filelist`
    do

    read1=${i}_R1.fastq.gz
        read2=${read1//_R1.fastq.gz/_R2.fastq.gz}
        echo $read1 $read2
done


# Loop file list
### add file name from variable to trim-galore

### merge trim-galore command and trim.sh
for i in `cat ../filelist`
    do
        read1=${i}_R1.fastq.gz
        read2=${read1//_R1.fastq.gz/_R2.fastq.gz}
        echo $read1 $read2
        echo "trim_galore --paired  --three_prime_clip_R1 5 --three_prime_clip_R2 5 --cores 2  --max_n 40  --fastqc --gzip -o /data/gpfs/assoc/bch709-5/students/${USER}/mouse/trim $read1 $read2" | cat trim.sh - > ${i}_trim.sh
done

### Batch submission

ls *.sh
ls -1 *.sh

### Loop *.sh printing
for i in `ls -1 *.sh`
do
    echo $i
done

### Loop *.sh submission
for i in `ls -1 *.sh`
do
    sbatch $i
done

Check submission

squeue -u ${USER}

Environment activation

conda activate BCH709_RNASeq

Copy files

cp /data/gpfs/assoc/bch709-5/students/Course_materials/mouse/ref/* /data/gpfs/assoc/bch709-5/students/${USER}/mouse/ref/

cp /data/gpfs/assoc/bch709-5/students/Course_materials/mouse/trim/* /data/gpfs/assoc/bch709-5/students/${USER}/mouse/trim/

RNA-Seq Alignment

#### Move to trim folder
cd /data/gpfs/assoc/bch709-5/students/${USER}/mouse/trim

#### Copy templet
cat /data/gpfs/assoc/bch709-5/students/Course_materials/mouse/run.sh | sed "s/16g/64g/g; s/\-\-cpus\-per\-task\=2/\-\-cpus\-per\-task\=4/g; s/\[NAME\]/Trim/g; s/\[youremail\]/${USER}\@unr.edu\,${USER}\@nevada.unr.edu/g" > /data/gpfs/assoc/bch709-5/students/${USER}/mouse/trim/mapping.sh

#### Edit templet
nano mapping.sh

Check output

ls -algh /data/gpfs/assoc/bch709-5/students/${USER}/mouse/trim

Output example

[FILENAME]_R1_val_1.fq.gz [FILENAME]_R2_val_2.fq.gz

STAR RNA-Seq alignment batch file test

for i in `cat ../filelist`
    do
        read1=${i}_R1_val_1.fq.gz
        read2=${read1//_R1_val_1.fq.gz/_R2_val_2.fq.gz}
        echo $read1 $read2
        echo "STAR --runMode alignReads --runThreadN 4 --outFilterMultimapNmax 100 --alignIntronMin 25 --alignIntronMax 50000 --genomeDir /data/gpfs/assoc/bch709-5/students/${USER}/mouse/ref  --readFilesCommand gunzip -c --readFilesIn /data/gpfs/assoc/bch709-5/students/${USER}/mouse/trim/${read1} /data/gpfs/assoc/bch709-5/students/${USER}/mouse/trim/${read2} --outSAMtype BAM SortedByCoordinate --outFileNamePrefix /data/gpfs/assoc/bch709-5/students/${USER}/mouse/bam/${i}.bam" | cat mapping.sh - > ${i}_mapping.sh
    done

Job submission dependency on Mapping


for i in `ls -1 *_mapping.sh`
do
    sbatch  $i 
done

Reads count

In the case of RNA-Seq, the features are typically genes, where each gene is considered here as the union of all its exons. Counting RNA-seq reads is complex because of the need to accommodate exon splicing. The common approach is to summarize counts at the gene level, by counting all reads that overlap any exon for each gene. In this method, gene annotation file from RefSeq or Ensembl is often used for this purpose. So far there are two major feature counting tools: featureCounts (Liao et al.) and htseq-count (Anders et al.)

featurecount

Copy templet

mkdir /data/gpfs/assoc/bch709-5/students/${USER}/RNA-Seq_example/ATH/readcount cd /data/gpfs/assoc/bch709-5/students/${USER}/RNA-Seq_example/ATH

cat /data/gpfs/assoc/bch709-5/students/Course_materials/mouse/run.sh sed “s/16g/64g/g; s/--cpus-per-task=2/--cpus-per-task=4/g; s/[NAME]/Count/g; s/[youremail]/${USER}\@unr.edu\,${USER}\@nevada.unr.edu/g” > /data/gpfs/assoc/bch709-5/students/${USER}/RNA-Seq_example/ATH/count.sh

FeatureCounts read bam file

mkdir /data/gpfs/assoc/bch709-5/students/${USER}/RNA-Seq_example/ATH/readcount/
cd /data/gpfs/assoc/bch709-5/students/${USER}/RNA-Seq_example/ATH/raw_data
ls -1 *.gz | sed 's/_.*//g' | sort -u > /data/gpfs/assoc/bch709-5/students/${USER}/RNA-Seq_example/ATH/filelist
cd  /data/gpfs/assoc/bch709-5/students/${USER}/RNA-Seq_example/ATH/bam
ls -1 *.sortedByCoord.out.bam
ls -1 *.sortedByCoord.out.bam| tr '\n' ' '
cp /data/gpfs/assoc/bch709-5/students/Course_materials/ATH/reference/*  /data/gpfs/assoc/bch709-5/students/${USER}/RNA-Seq_example/ATH/reference/TAIR10_GFF3_genes.gtf

###########

featureCounts -o [output] -T [threads] -Q 1 -p -M  -g gene_id -a [GTF] [BAMs]

Edit file

#paste this to count.sh
featureCounts -o /data/gpfs/assoc/bch709-5/students/${USER}/RNA-Seq_example/ATH/readcount/ATH_featucount -T 4 -Q 1 -p -M  -g gene_id -a /data/gpfs/assoc/bch709-5/students/${USER}/RNA-Seq_example/ATH/reference/TAIR10_GFF3_genes.gtf $(for i in `cat  /data/gpfs/assoc/bch709-5/students/${USER}/RNA-Seq_example/ATH/filelist`; do echo ${i}.bamAligned.sortedByCoord.out.bam| tr '\n' ' ';done)

nano count.sh

RNA-Seq report

How to make a report?

MultiQC MultiQC

MultiQC

cd /data/gpfs/assoc/bch709-5/students/${USER}/RNA-Seq_example/ATH
 multiqc --pdf -n test .

Slurm provides resource management for the processors allocated to a job, so that multiple job steps can be simultaneously submitted and queued until there are available resources within the job’s allocation. ############

FeatureCounts execute location

#### Move to trim folder
cd /data/gpfs/assoc/bch709-5/students/${USER}/mouse/bam 

ls -1 *.bam

#### Copy templet
cp /data/gpfs/assoc/bch709-5/students/Course_materials/mouse/run.sh /data/gpfs/assoc/bch709-5/students/${USER}/mouse/bam/count.sh
sed -i "s/16g/64g/g; s/\-\-cpus\-per\-task\=2/\-\-cpus\-per\-task\=4/g; s/\[NAME\]/Count/g; s/\[youremail\]/${USER}\@unr.edu\,${USER}\@nevada.unr.edu/g" /data/gpfs/assoc/bch709-5/students/${USER}/mouse/bam/count.sh

FeatureCounts read bam file

cd /data/gpfs/assoc/bch709-5/students/${USER}/mouse/bam 
ls -1 *.sortedByCoord.out.bam
ls -1 *.sortedByCoord.out.bam| tr '\n' ' '

Edit templet

nano count.sh

#paste this to count.sh
featureCounts -o /data/gpfs/assoc/bch709-5/students/${USER}//mouse/readcount/featucount -T 4 -Q 1 -p -M  -g gene_id -a /data/gpfs/assoc/bch709-5/students/${USER}/mouse/ref/refGene.gtf $(for i in `cat /data/gpfs/assoc/bch709-5/students/${USER}/mouse/filelist`; do echo ${i}.bamAligned.sortedByCoord.out.bam| tr '\n' ' ';done)

Job submission dependency

squeue --noheader --format %i --user ${USER}

Submit

# Get the list of job IDs as a colon-separated string
jobid=$(squeue --noheader --format %i --user ${USER} | tr '\n' ':' | sed 's/:$//')

# Check if the jobid variable is empty
if [[ -n "$jobid" ]]; then
    # If not empty, schedule the job with dependencies
    sbatch --dependency=afterany:${jobid} count.sh
else
    # If empty, schedule the job without dependencies
    sbatch count.sh
fi

local Mac to Downloads

echo "setopt nonomatch" >> ~/.zshrc

Conda environment


mamba create -n RNASeq_postanalysis -y -c bioconda -c conda-forge -c r r trinity multiqc=1.9 samtools r-fastcluster=1.1.25  bioconductor-ctc  bioconductor-deseq2 bioconductor-biobase=2.40.0  bioconductor-qvalue  bioconductor-limma bioconductor-edger  bioconductor-genomeinfodb bioconductor-deseq2 bioconductor-genomeinfodbdata r-rcurl

conda activate RNASeq_postanalysis

WORKTING PATH

cd /data/gpfs/assoc/bch709-5/students/${USER}/RNA-Seq_example/ATH/readcount

Reads count

In the case of RNA-Seq, the features are typically genes, where each gene is considered here as the union of all its exons. Counting RNA-seq reads is complex because of the need to accommodate exon splicing. The common approach is to summarize counts at the gene level, by counting all reads that overlap any exon for each gene. In this method, gene annotation file from RefSeq or Ensembl is often used for this purpose. So far there are two major feature counting tools: featureCounts (Liao et al.) and htseq-count (Anders et al.)

featurecount

head ATH_featucount

cut -f1,7-  ATH_featucount |  egrep -v "#" | sed 's/\Aligned\.sortedByCoord\.out\.bam//g; s/\.bam//g' >> ATH.featureCount_count_only.cnt

Go to DEG

head ATH_featucount
head ATH.featureCount_count_only.cnt
mkdir /data/gpfs/assoc/bch709-5/students/${USER}/RNA-Seq_example/ATH/DEG
cp ATH.featureCount* ../DEG
cd /data/gpfs/assoc/bch709-5/students/${USER}/RNA-Seq_example/ATH/DEG
ls

Data list

Sample information Run
WT_rep1 SRR1761506
WT_rep2 SRR1761507
WT_rep3 SRR1761508
ABA_rep1 SRR1761509
ABA_rep2 SRR1761510
ABA_rep3 SRR1761511

sample files

nano samples.txt

WT<TAB>SRR1761506
WT<TAB>SRR1761507
WT<TAB>SRR1761508
ABA<TAB>SRR1761509
ABA<TAB>SRR1761510
ABA<TAB>SRR1761511

sed -i 's/<TAB>/\t/g' samples.txt

PtR (Quality Check Your Samples and Biological Replicates)

Once you’ve performed transcript quantification for each of your biological replicates, it’s good to examine the data to ensure that your biological replicates are well correlated, and also to investigate relationships among your samples. If there are any obvious discrepancies among your sample and replicate relationships such as due to accidental mis-labeling of sample replicates, or strong outliers or batch effects, you’ll want to identify them before proceeding to subsequent data analyses (such as differential expression).

PtR  --matrix ATH.featureCount_count_only.cnt  --samples samples.txt --CPM  --log2 --min_rowSums 10   --sample_cor_matrix --compare_replicates


WT.rep_compare.pdf
ABA.rep_compare.pdf

DEG calculation

conda activate RNASeq_postanalysis
mamba install -c conda-forge -c anaconda pandas

cd /data/gpfs/assoc/bch709-5/students/${USER}/RNA-Seq_example/ATH/DEG

run_DE_analysis.pl --matrix ATH.featureCount_count_only.cnt --method DESeq2 --samples_file samples.txt --output rnaseq

DEG output

cd rnaseq

ATH.featureCount_count_only.cnt.ABA_vs_WT.DESeq2.count_matrix
ATH.featureCount_count_only.cnt.ABA_vs_WT.DESeq2.DE_results
ATH.featureCount_count_only.cnt.ABA_vs_WT.DESeq2.DE_results.MA_n_Volcano.pdf
ATH.featureCount_count_only.cnt.ABA_vs_WT.DESeq2.Rscript

TPM and FPKM calculation


cd /data/gpfs/assoc/bch709-5/students/${USER}/RNA-Seq_example/ATH/readcount

cut -f1,6-  ATH_featucount |  egrep -v "#" | sed 's/\Aligned\.sortedByCoord\.out\.bam//g; s/\.bam//g' > ATH.featureCount_count_length.cnt

cp ATH.featureCount* ../DEG
cd /data/gpfs/assoc/bch709-5/students/${USER}/RNA-Seq_example/ATH/DEG

cp /data/gpfs/assoc/bch709-5/students/Course_materials/script/tpm_raw_exp_calculator.py .

python tpm_raw_exp_calculator.py -count ATH.featureCount_count_length.cnt

TPM and FPKM calculation output

ATH.featureCount_count_length.cnt.fpkm.xls
ATH.featureCount_count_length.cnt.fpkm.tab
ATH.featureCount_count_length.cnt.tpm.xls
ATH.featureCount_count_length.cnt.tpm.tab

DEG subset

cd /data/gpfs/assoc/bch709-5/students/wyim/RNA-Seq_example/ATH/DEG/rnaseq

analyze_diff_expr.pl --samples /data/gpfs/assoc/bch709-5/students/${USER}/RNA-Seq_example/ATH/DEG/samples.txt  --matrix /data/gpfs/assoc/bch709-5/students/${USER}/RNA-Seq_example/ATH/DEG/ATH.featureCount_count_length.cnt.tpm.tab -P 0.01 -C 2 --output ATH

analyze_diff_expr.pl --samples /data/gpfs/assoc/bch709-5/students/${USER}/RNA-Seq_example/ATH/DEG/samples.txt  --matrix /data/gpfs/assoc/bch709-5/students/${USER}/RNA-Seq_example/ATH/DEG/ATH.featureCount_count_length.cnt.tpm.tab -P 0.05 -C 1 --output ATH

DEG output

ATH.matrix.log2.centered.sample_cor_matrix.pdf
ATH.matrix.log2.centered.genes_vs_samples_heatmap.pdf

ATH.featureCount_count_only.cnt.ABA_vs_WT.DESeq2.DE_results.P0.01_C2.ABA-UP.subset
ATH.featureCount_count_only.cnt.ABA_vs_WT.DESeq2.DE_results.P0.01_C2.WT-UP.subset
ATH.featureCount_count_only.cnt.ABA_vs_WT.DESeq2.DE_results.P0.01_C2.DE.subset

ATH.featureCount_count_only.cnt.ABA_vs_WT.DESeq2.DE_results.P0.01_C1.ABA-UP.subset
ATH.featureCount_count_only.cnt.ABA_vs_WT.DESeq2.DE_results.P0.01_C1.WT-UP.subset
ATH.featureCount_count_only.cnt.ABA_vs_WT.DESeq2.DE_results.P0.01_C1.DE.subset

Mouse DEG

Activate environment

conda activate RNASeq_postanalysis

WORKTING PATH

cd /data/gpfs/assoc/bch709-5/students/${USER}/mouse/readcount

Clean sample name

### ls /data/gpfs/assoc/bch709-5/students/${USER}/mouse/readcount

cut -f1,7-  mouse_featurecount |  egrep -v "#" | sed 's/\Aligned\.sortedByCoord\.out\.bam//g; s/\.bam//g' > mouse_featurecount_only.cnt
cut -f1,6-  mouse_featurecount |  egrep -v "#" | sed 's/\Aligned\.sortedByCoord\.out\.bam//g; s/\.bam//g' > mouse_featurecount_length.cnt

Copy read count to DEG folder

cp /data/gpfs/assoc/bch709-5/students/${USER}/mouse/readcount/mouse_* /data/gpfs/assoc/bch709-5/students/${USER}/mouse/DEG/

Go to DEG folder

cd /data/gpfs/assoc/bch709-5/students/${USER}/mouse/DEG/

sample files

nano samples.txt

WT<TAB>R62_WT_Rep2
WT<TAB>R62_WT_Rep3
WT<TAB>R62_WT_Rep4
WT<TAB>R62_WT_Rep5
Glial<TAB>R62_Glial_Rep1
Glial<TAB>R62_Glial_Rep2
Glial<TAB>R62_Glial_Rep3
Glial<TAB>R62_Glial_Rep4
Glial<TAB>R62_Glial_Rep5

sed -i 's/<TAB>/\t/g' samples.txt

PtR (Quality Check Your Samples and Biological Replicates)

cd /data/gpfs/assoc/bch709-5/students/${USER}/mouse/DEG/
PtR  --matrix mouse_featurecount_only.cnt  --samples samples.txt --CPM  --log2 --min_rowSums 10   --sample_cor_matrix --compare_replicates

PtR download on local

scp [YOURID]@pronghorn.rc.unr.edu:/data/gpfs/assoc/bch709-5/students/${USER}/mouse/DEG/*.pdf .

DEG calculation

cd /data/gpfs/assoc/bch709-5/students/${USER}/mouse/DEG

run_DE_analysis.pl --matrix mouse_featurecount_only.cnt --method DESeq2 --samples_file samples.txt --output rnaseq

DEG output

cd rnaseq

mouse_featurecount_only.cnt.Glial_vs_WT.DESeq2.count_matrix
mouse_featurecount_only.cnt.Glial_vs_WT.DESeq2.DE_results
mouse_featurecount_only.cnt.Glial_vs_WT.DESeq2.DE_results.MA_n_Volcano.pdf
mouse_featurecount_only.cnt.Glial_vs_WT.DESeq2.Rscript

TPM and FPKM calculation

TPM/FPKM calculation

cp /data/gpfs/assoc/bch709-5/students/Course_materials/script/tpm_raw_exp_calculator.py .

python tpm_raw_exp_calculator.py -count mouse_featurecount_length.cnt

TPM and FPKM calculation output

mouse_featurecount_length.cnt.fpkm.xls
mouse_featurecount_length.cnt.fpkm.tab
mouse_featurecount_length.cnt.tpm.xls
mouse_featurecount_length.cnt.tpm.tab

DEG subset

cd /data/gpfs/assoc/bch709-5/students/wyim/mouse/DEG/rnaseq

analyze_diff_expr.pl --samples /data/gpfs/assoc/bch709-5/students/wyim/mouse/DEG/samples.txt  --matrix /data/gpfs/assoc/bch709-5/students/wyim/mouse/DEG/mouse_featurecount_length.cnt.tpm.tab  -P 0.01 -C 2 --output mouse

analyze_diff_expr.pl --samples /data/gpfs/assoc/bch709-5/students/wyim/mouse/DEG/samples.txt  --matrix /data/gpfs/assoc/bch709-5/students/wyim/mouse/DEG/mouse_featurecount_length.cnt.tpm.tab  -P 0.01 -C 2 --output mouse

DEG output

mouse_featurecount_only.cnt.Glial_vs_WT.DESeq2.count_matrix
mouse_featurecount_only.cnt.Glial_vs_WT.DESeq2.DE_results
mouse_featurecount_only.cnt.Glial_vs_WT.DESeq2.DE_results.MA_n_Volcano.pdf
mouse_featurecount_only.cnt.Glial_vs_WT.DESeq2.DE_results.P0.01_C2.DE.subset
mouse_featurecount_only.cnt.Glial_vs_WT.DESeq2.DE_results.P0.01_C2.Glial-UP.subset
mouse_featurecount_only.cnt.Glial_vs_WT.DESeq2.DE_results.P0.01_C2.WT-UP.subset
mouse_featurecount_only.cnt.Glial_vs_WT.DESeq2.DE_results.samples
mouse_featurecount_only.cnt.Glial_vs_WT.DESeq2.Rscript

Expression normalization based on transcript length

FPKM

Fragments per Kilobase of transcript per million mapped reads

FPKM

X = mapped reads count N = number of reads L = Length of transcripts

cd /data/gpfs/assoc/bch709-5/students/wyim/mouse/DEG/

head mouse_featurecount_length.cnt

awk 'NR > 1 {sum += $3} END {print sum}' mouse_featurecount_length.cnt

egrep Xkr4 /data/gpfs/assoc/bch709-5/students/wyim/mouse/DEG/rnaseq/rnaseqmouse_featurecount_length.cnt

X = 718
Number_Reads_mapped = 87203201
Length = 3634
fpkm= X*(1000/Length)*(1000000/Number_Reads_mapped)
fpkm
quit()

ten to the ninth power = 10**9

fpkm=X/(Number_Reads_mapped*Length)*10**9
fpkm
quit()

check th FPKM file

cd /data/gpfs/assoc/bch709-5/students/wyim/mouse/DEG/

egrep Xkr4 mouse_featurecount_length.cnt.fpkm.tab

TPM

Transcripts Per Million

TPM

TPM2

TPM calculation from reads count

cd /data/gpfs/assoc/bch709-5/students/wyim/mouse/DEG

awk 'NR > 1 {sum += $3/$2} END {print sum}' mouse_featurecount_length.cnt

sum_count_per_length = 32811.8
Length = 3634
X = 718
TPM = (X/Length)*(1/sum_count_per_length )*10**6

TPM calculation from FPKM

awk ‘NR > 1 {sum += $2} END {print sum}’ mouse_featurecount_length.cnt.fpkm.tab

FPKM = 2.265724465514574
SUM_FPKM = 376268
TPM=(FPKM/SUM_FPKM)*10**6
TPM
quit()

Paper read

Li et al., 2010, RSEM Dillies et al., 2013

DEG subset

cd /data/gpfs/assoc/bch709-5/students/wyim/mouse/DEG/rnaseq

analyze_diff_expr.pl --samples /data/gpfs/assoc/bch709-5/students/${USER}/mouse/DEG/samples.txt  --matrix /data/gpfs/assoc/bch709-5/students/${USER}/mouse/DEG/mouse_featurecount_length.cnt.tpm.tab -P 0.01 -C 2 --output mouse_RNASEQ_P001_C2


analyze_diff_expr.pl --samples /data/gpfs/assoc/bch709-5/students/${USER}/mouse/DEG/samples.txt  --matrix /data/gpfs/assoc/bch709-5/students/${USER}/mouse/DEG/mouse_featurecount_length.cnt.tpm.tab -P 0.05 -C 1 --output mouse_RNASEQ_P001_C2

DEG Subset to list

for i in *.subset; do
  NAME=$(basename "${i}" .subset)
  egrep -v sample "${i}" | cut -f 1 > "${NAME}.txt"
done
#This will generate .txt files for each .subset file in the directory, excluding lines with "sample" and retaining only the first column.

DEG download

scp ..

Functional analysis • GO

Gene enrichment analysis (Hypergeometric test) Gene set enrichment analysis (GSEA) Gene ontology / Reactome databases

Gene Ontology

Gene Ontology project is a major bioinformatics initiative Gene ontology is an annotation system The project provides the controlled and consistent vocabulary of terms and gene product annotations, i.e. terms occur only once, and there is a dictionary of allowed words GO describes how gene products behave in a cellular context A consistent description of gene products attributes in terms of their associated biological processes, cellular components and molecular functions in a species-independent manner Each GO term consists of a unique alphanumerical identifier, a common name, synonyms (if applicable), and a definition Each term is assigned to one of the three ontologies Terms have a textual definition When a term has multiple meanings depending on species, the GO uses a “sensu” tag to differentiate among them (trichome differentiation (sensu Magnoliophyta)

GO

kegg

hypergeometric test

The hypergeometric distribution is the lesser-known cousin of the binomial distribution, which describes the probability of k successes in n draws with replacement. The hypergeometric distribution describes probabilities of drawing marbles from the jar without putting them back in the jar after each draw. The hypergeometric probability mass function is given by (using the original variable convention)

hyper_geo combination FWER

FWER

The FWER for the other tests is computed in the same way: the gene-associated variables (scores or counts) are permuted while the annotations of genes to GO-categories stay fixed. Then the statistical tests are evaluated again for every GO-category.

Hypergeometric Test Example 1

Suppose we randomly select 2 cards without replacement from an ordinary deck of playing cards. What is the probability of getting exactly 2 cards you want (i.e., Ace or 10)?

Solution: This is a hypergeometric experiment in which we know the following:

N = 52; since there are 52 cards in a deck. k = 16; since there are 16 Ace or 10 cards in a deck. n = 2; since we randomly select cards from the deck. x = 2; since 2 of the cards we select are red. We plug these values into the hypergeometric formula as follows:

h(x; N, n, k) = [ kCx ] [ N-kCn-x ] / [ NCn ]

h(2; 52, 2, 16) = [ 16C2 ] [ 48C1 ] / [ 52C2 ]

h(2; 52, 2, 16) = [ 325 ] [ 1 ] / [ 1,326 ]

h(2; 52, 2, 16) = 0.0904977

Thus, the probability of randomly selecting 2 Ace or 10 cards is 9%

category probability
probability mass f 0.09049773755656108597285
lower cumulative P 1
upper cumulative Q 0.09049773755656108597285
Expectation 0.6153846153846153846154

Hypergeometric Test Example 2

Suppose we have 30 DEGs in human genome (200). What is the probability of getting 10 oncogene?

An oncogene is a gene that has the potential to cause cancer.

Solution: This is a hypergeometric experiment in which we know the following:

N = 200; since there are 200 genes in human genome k = 10; since there are 10 oncogenes in human n = 30; since 30 DEGs x = 5; since 5 of the oncogenes in DEGs.

We plug these values into the hypergeometric formula as follows:

h(x; N, n, k) = [ kCx ] [ N-kCn-x ] / [ NCn ]

h(5; 200, 30, 10) = [ 10C5 ] [ 190C25 ] / [ 200C30 ]

h(5; 200, 30, 10) = [ 252 ] [ 11506192278177947613740456466942 ] / [ 409681705022127773530866523638950880 ]

h(5; 200, 30, 10) = 0.007078

Thus, the probability of oncogene 0.7%.

hypergeometry.png

hypergeometric distribution value

category probability
probability mass f 0.0070775932109153651831923063371216961166297
lower cumulative P 0.99903494867072865323201131115533112651846
upper cumulative Q 0.0080426445401867119511809951817905695981658
Expectation 1.5

False Discovery Rate (FDR) q-value

The false discovery rate (FDR) is a method of conceptualizing the rate of type I errors in null hypothesis testing when conducting multiple comparisons. FDR-controlling procedures are designed to control the expected proportion of “discoveries” (rejected null hypotheses) that are false (incorrect rejections).

MetaScape

http://metascape.org/gp/index.html

REViGO

http://revigo.irb.hr/revigo.jsp

cleverGO

http://www.tartaglialab.com/GO_analyser/tutorial

DAVID

https://david.ncifcrf.gov/

Araport

http://araport.org

Paper read

Fu, Yu, et al. “Elimination of PCR duplicates in RNA-seq and small RNA-seq using unique molecular identifiers.” BMC genomics 19.1 (2018): 531 Parekh, Swati, et al. “The impact of amplification on differential expression analyses by RNA-seq.” Scientific reports 6 (2016): 25533 Klepikova, Anna V., et al. “Effect of method of deduplication on estimation of differential gene expression using RNA-seq.” PeerJ 5 (2017): e3091

Human RNA-Seq

Transcriptome alterations in myotonic dystrophy frontal cortex

Environment activation

conda activate BCH709_RNASeq

Working directory (Pronghorn)

echo $USER

cd /data/gpfs/assoc/bch709-5/students/${USER}

mkdir human
mkdir /data/gpfs/assoc/bch709-5/students/${USER}/human/fastq
mkdir /data/gpfs/assoc/bch709-5/students/${USER}/human/ref 
mkdir /data/gpfs/assoc/bch709-5/students/${USER}/human/trim
mkdir /data/gpfs/assoc/bch709-5/students/${USER}/human/bam
mkdir /data/gpfs/assoc/bch709-5/students/${USER}/human/readcount
mkdir /data/gpfs/assoc/bch709-5/students/${USER}/human/DEG

Reference Download

https://www.ncbi.nlm.nih.gov/genome/guide/human/

### change working directory
cd /data/gpfs/assoc/bch709-5/students/${USER}/human/ref 


### download
wget https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/genes/hg38.refGene.gtf.gz

wget https://ftp.ncbi.nlm.nih.gov/refseq/H_sapiens/annotation/GRCh38_latest/refseq_identifiers/GRCh38_latest_genomic.fna.gz


### decompress
gunzip GRCh38_latest_genomic.fna.gz
gunzip hg38.refGene.gtf.gz

STAR reference build

STAR aligner reference build on Pronghorn

### Copy templet
cp /data/gpfs/assoc/bch709-5/students/Course_materials/human/run.sh /data/gpfs/assoc/bch709-5/students/${USER}/human/ref/ref_build.sh

#open text editor
### PLEASE RENAME EMAIL AND JOB NAME
sed -i "s/16g/64g/g; s/\-\-cpus\-per\-task\=2/\-\-cpus\-per\-task\=4/g; s/\[NAME\]/ref_build/g; s/\[youremail\]/${USER}\@unr.edu\,${USER}\@nevada.unr.edu/g" /data/gpfs/assoc/bch709-5/students/${USER}/human/ref/ref_build.sh
nano ref_build.sh

# Add below command to ref_build.sh

STAR  --runThreadN 4 --runMode genomeGenerate --genomeDir . --genomeFastaFiles GRCh38_latest_genomic.fna --sjdbGTFfile GRCh38_latest_genomic.gtf  --sjdbOverhang 99   --genomeSAindexNbases 12

Submit job to HPC

#submit job
sbach ref_build.sh

#check job
squeue -u ${USER}

FASTQ file

cd /data/gpfs/assoc/bch709-5/students/${USER}/human/

### Link file (without copy)
ln -s /data/gpfs/assoc/bch709-5/students/Course_materials/human/fastq/* /data/gpfs/assoc/bch709-5/students/${USER}/human/fastq

ls /data/gpfs/assoc/bch709-5/students/${USER}/human/fastq

Create file list

cd /data/gpfs/assoc/bch709-5/students/${USER}/human/fastq

ls -1 *.gz 

ls -1 *.gz | sed 's/_R.\.fastq\.gz//g'

ls -1 *.gz | sed 's/_R.\.fastq\.gz//g' | sort -u

ls -1 *.gz | sed 's/_R.\.fastq\.gz//g' | sort -u > /data/gpfs/assoc/bch709-5/students/${USER}/human/filelist

cat /data/gpfs/assoc/bch709-5/students/${USER}/human/filelist

Regular expression

https://regex101.com/

Trim reads

Prepare templet

cp /data/gpfs/assoc/bch709-5/students/Course_materials/human/run.sh /data/gpfs/assoc/bch709-5/students/${USER}/human/fastq/trim.sh
sed -i "s/16g/64g/g; s/\-\-cpus\-per\-task\=2/\-\-cpus\-per\-task\=4/g; s/\[NAME\]/Trim/g; s/\[youremail\]/${USER}\@unr.edu\,${USER}\@nevada.unr.edu/g" /data/gpfs/assoc/bch709-5/students/${USER}/human/fastq/trim.sh

Edit templet

nano /data/gpfs/assoc/bch709-5/students/${USER}/human/fastq/trim.sh

Batch submission

# Check file list
cat ../filelist
nano trim.sh

# Loop file list
### Add Forward read to variable
### Add reverse read from forward read name substitution
### add file name from variable to trim-galore
### merge trim-galore command and trim.sh
### add trim-galore command and trim.sh to new file
for i in `cat ../filelist`
    do
        read1=${i}_R1.fastq.gz
        read2=${read1//_R1.fastq.gz/_R2.fastq.gz}
        
        echo "trim_galore --paired  --three_prime_clip_R1 5 --three_prime_clip_R2 5 --cores 2  --max_n 40  --fastqc --gzip -o /data/gpfs/assoc/bch709-5/students/${USER}/human/trim $read1 $read2" | cat trim.sh - > ${i}_trim.sh
        echo "$read1 $read2 trim file has been created."
done

Batch submission

ls -1 *_trim.sh
### Loop *.sh submission
for i in `ls -1 *_trim.sh`
do
    sbatch $i
done

Check submission

squeue -u ${USER}

RNA-Seq Alignment

#### Move to trim folder
cd /data/gpfs/assoc/bch709-5/students/${USER}/human/trim

#### Copy templet
cp /data/gpfs/assoc/bch709-5/students/Course_materials/human/run.sh /data/gpfs/assoc/bch709-5/students/${USER}/human/trim/mapping.sh
sed -i "s/16g/64g/g; s/\-\-cpus\-per\-task\=2/\-\-cpus\-per\-task\=4/g; s/\[NAME\]/Mapping/g; s/\[youremail\]/${USER}\@unr.edu\,${USER}\@nevada.unr.edu/g" /data/gpfs/assoc/bch709-5/students/${USER}/human/trim/mapping.sh


#### Edit templet
nano mapping.sh

Check output

ls -algh /data/gpfs/assoc/bch709-5/students/${USER}/human/trim

Output example

[FILENAME]_R1_val_1.fq.gz [FILENAME]_R2_val_2.fq.gz

STAR RNA-Seq alignment

STAR --runMode alignReads --runThreadN 4 --outFilterMultimapNmax 100 --alignIntronMin 25 --alignIntronMax 50000 --quantMode TranscriptomeSAM GeneCounts --genomeDir /data/gpfs/assoc/bch709-5/students/${USER}/human/ref  --readFilesCommand gunzip -c --readFilesIn /data/gpfs/assoc/bch709-5/students/${USER}/human/trim/[FILENAME]_R1_val_1.fq.gz  /data/gpfs/assoc/bch709-5/students/${USER}/human/trim/[FILENAME]_R2_val_2.fq.gz --outSAMtype BAM SortedByCoordinate --outFileNamePrefix /data/gpfs/assoc/bch709-5/students/${USER}/human/bam/[FILENAME].bam

STAR RNA-Seq alignment batch file

cd /data/gpfs/assoc/bch709-5/students/${USER}/human/trim
for i in `cat ../filelist`
    do
        read1=${i}_R1_val_1.fq.gz
        read2=${read1//_R1_val_1.fq.gz/_R2_val_2.fq.gz}
        echo $read1 $read2
        echo "STAR --runMode alignReads --runThreadN 4 --outFilterMultimapNmax 100 --alignIntronMin 25 --alignIntronMax 50000 --genomeDir /data/gpfs/assoc/bch709-5/students/${USER}/human/ref --outSAMtype BAM SortedByCoordinate --readFilesCommand gunzip -c --readFilesIn /data/gpfs/assoc/bch709-5/students/${USER}/human/trim/${read1} /data/gpfs/assoc/bch709-5/students/${USER}/human/trim/${read2} --outFileNamePrefix /data/gpfs/assoc/bch709-5/students/${USER}/human/bam/${i}.bam" | cat mapping.sh - > ${i}_mapping.sh
    done

Job submission dependency

squeue --noheader --format %i --user ${USER}  
squeue --noheader --format %i --user ${USER} | tr '\n'  ':'

Job submission dependency on Trim


jobid=$(squeue --noheader --format %i --user ${USER} | tr '\n'  ':')1
for i in `ls -1 *_mapping.sh`
do
    sbatch  --dependency=afterany:${jobid} $i 
done

FeatureCounts

Bioinformatics, Volume 30, Issue 7, 1 April 2014, Pages 923–930 

featureCounts -o [output] -T [threads] -Q 1 -p -M  -g gene_id -a [GTF] [BAMs]

FeatureCounts location

#### Move to trim folder
cd /data/gpfs/assoc/bch709-5/students/${USER}/human/bam 

#### Copy templet
cp /data/gpfs/assoc/bch709-5/students/Course_materials/human/run.sh /data/gpfs/assoc/bch709-5/students/${USER}/human/bam/count.sh

sed -i "s/16g/64g/g; s/\-\-cpus\-per\-task\=2/\-\-cpus\-per\-task\=4/g; s/\[NAME\]/Count/g; s/\[youremail\]/${USER}\@unr.edu\,${USER}\@nevada.unr.edu/g"  /data/gpfs/assoc/bch709-5/students/${USER}/human/bam/count.sh

FeatureCounts command to count.sh

LOOP example

cd /data/gpfs/assoc/bch709-5/students/${USER}/human/bam 

ls -1 *.bam 

for i in `cat /data/gpfs/assoc/bch709-5/students/${USER}/human/filelist`
do 
echo ${i}.bamAligned.sortedByCoord.out.bam | tr '\n' ' '
done

FeatureCount


echo "featureCounts -o /data/gpfs/assoc/bch709-5/students/${USER}//mouse/readcount/featucount -T 4 -Q 1 -p -M  -g gene_id -a /data/gpfs/assoc/bch709-5/students/${USER}/human/ref/GRCh38_latest_genomic.gtf $(for i in `cat /data/gpfs/assoc/bch709-5/students/${USER}/human/filelist`; do echo ${i}.bamAligned.sortedByCoord.out.bam| tr '\n' ' ';done)" >> count.sh

Job submission dependency

squeue --noheader --format %i --user ${USER} 
squeue --noheader --format %i --user ${USER} | tr '\n'  ':'

Job submission dependency on Align

cd /data/gpfs/assoc/bch709-5/students/${USER}/human/bam 

jobid=$(squeue --noheader --format %i --user ${USER} | tr '\n'  ':')1
sbatch  --dependency=afterany:${jobid} count.sh

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Slurm

CheatSheet