BCH709 Introduction to Bioinformatics: HPC

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:

• CPU Partition: 93 nodes, 2,976 CPU cores, 21TiB of memory.
• GPU Partition: 44 NVIDIA Tesla P100 GPUs, 352 CPU cores, 2.75TiB of memory.
• Storage: 1PB high-performance storage using IBM SpectrumScale.

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

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

• Windows (WSL)

  cd ~/bch709
  explorer.exe .
  

• Mac

  cd ~/bch709
  open .
  

Rsync Usage

• Sync local directory to remote server:

  rsync -avhP <source_directory> <username>@pronghorn.rc.unr.edu:<target_directory>
  

• Sync remote directory to local:

  rsync -avhP <username>@pronghorn.rc.unr.edu:<source_directory> <target_directory>
  

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 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

Subset of data

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

• Trim IF necessary
  • Synthetic bases can be an issue for SNP calling
  • Insert size distribution may be more important for assemblers
• Trim/Clip/Filter reads
• Remove adapter sequences
• Trim reads by quality
• Sliding window trimming
• Filter by min/max read length
• Remove reads less than ~18nt
• Demultiplexing/Splitting

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-bch709-2
#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-bch709-2
#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

Paper reading

Please read this paper https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0881-8

RNA Sequencing

1. The transcriptome is spatially and temporally dynamic
2. Data comes from functional units (coding regions)
3. Only a tiny fraction of the genome

Introduction

Sequence based assays of transcriptomes (RNA-seq) are in wide use because of their favorable properties for quantification, transcript discovery and splice isoform identification, as well as adaptability for numerous more specialized measurements. RNA-Seq studies present some challenges that are shared with prior methods such as microarrays and SAGE tagging, and they also present new ones that are specific to high-throughput sequencing platforms and the data they produce. This document is part of an ongoing effort to provide the community with standards and guidelines that will be updated as RNASeq matures and to highlight unmet challenges. The intent is to revise this document periodically to capture new advances and increasingly consolidate standards and best practices.

RNA-Seq experiments are diverse in their aims and design goals, currently including multiple types of RNA isolated from whole cells or from specific sub-cellular compartments or biochemical classes, such as total polyA+ RNA, polysomal RNA, nuclear ribosome-depleted RNA, various size fractions of RNA and a host of others. The goals of individual experiments range from major transcriptome “discovery” that seeks to define and quantify all RNA species in a starting RNA sample to experiments that simply need to detect significant changes in the more abundant RNA classes across many samples.

Seven stages to data science

1. Define the question of interest
2. Get the data
3. Clean the data
4. Explore the data
5. Fit statistical models
6. Communicate the results
7. Make your analysis reproducible

What do we need to prepare ?

Sample Information

a. What kind of material it is should be noted: Tissue, cell line, primary cell type, etc… b. It’s ontology term (a DCC wrangler will work with you to obtain this) c. If any treatments or genetic modifications (TALENs, CRISPR, etc…) were done to the sample prior to RNA isolation. d. If it’s a subcellular fraction or derived from another sample. If derived from another sample, that relationship should be noted. e. Some sense of sample abundance: RNA-Seq data from “bulk” vs. 10,000 cell equivalents can give very different results, with lower input samples typically being less reproducible. Having a sense of the amount of starting material here is useful. f. If you received a batch of primary or immortalized cells, the lot #, cat # and supplier should be noted. g. If cells were cultured out, the protocol and methods used to propagate the cells should be noted. h. If any cell phenotyping or other characterizations were done to confirm it’s identify, purity, etc.. those methods should be noted.

RNA Information:

RNAs come in all shapes and sizes. Some of the key properties to report are: a. Total RNA, Poly-A(+) RNA, Poly-A(-) RNA b. Size of the RNA fraction: we typically have a + 200 and – 200 cutoff, but there is a wide range, i.e. microRNA-sized, etc… c. If the RNA was treated with Ribosomal RNA depletion kits (RiboMinus, RiboZero): please note the kit used.

Protocols:

There are several methods used to isolate RNAs with that work fine for the purposes of RNA-Seq. For all the ENCODE libraries that we make, we provide a document that lists in detail: a. The RNA isolation methods, b. Methods of size selections c. Methods of rRNA removal d. Methods of oligo-dT selections e. Methods of DNAse I treatments

Experimental Design

• Balanced design
• Technical replicates not necessary (Marioni et al., 2008)
• Biological replicates: 6 - 12 (Schurch et al., 2016)
• Power analysis

Reading materials

Paul L. Auer and R. W. Doerge “Statistical Design and Analysis of RNA Sequencing Data” Genetics June 1, 2010 vol. 185 no.2 405-416
Busby, Michele A., et al. “Scotty: a web tool for designing RNA-Seq experiments to measure differential gene expression.” Bioinformatics 29.5 (2013): 656-657
Marioni, John C., et al. “RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays.” Genome research (2008)
Schurch, Nicholas J., et al. “How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use?.” Rna (2016)
Zhao, Shilin, et al. “RnaSeqSampleSize: real data based sample size estimation for RNA sequencing.” BMC bioinformatics 19.1 (2018): 191

Replicate number

In all cases, experiments should be performed with two or more biological replicates, unless there is a compelling reason why this is impractical or wasteful (e.g. overlapping time points with high temporal resolution). A biological replicate is defined as an independent growth of cells/tissue and subsequent analysis. Technical replicates from the same RNA library are not required, except to evaluate cases where biological variability is abnormally high. In such instances, separating technical and biological variation is critical. In general, detecting and quantifying low prevalence RNAs is inherently more variable than high abundance RNAs. As part of the ENCODE pipeline, annotated transcript and genes are quantified using RSEM and the values are made available for downstream correlation analysis. Replicate concordance: the gene level quantification should have a Spearman correlation of >0.9 between isogenic replicates and >0.8 between anisogenic replicates.

RNA extraction

• Sample processing and storage
• Total RNA/mRNA/small RNA
• DNAse treatment
• Quantity & quality
• RIN values (Strong effect)
• Batch effect
• Extraction method bias (GC bias)

Reading materials

Romero, Irene Gallego, et al. “RNA-seq: impact of RNA degradation on transcript quantification.” BMC biology 12.1 (2014): 42
Kim, Young-Kook, et al. “Short structured RNAs with low GC content are selectively lost during extraction from a small number of cells.” Molecular cell 46.6 (2012): 893-89500481-9).

RNA Quantification and Quality Control: When working with bulk samples, throughout the various steps we periodically assess the quality and quantity of the RNA. This is typically done on a BioAnalyzer. Points to check are: a. Total RNA b. After oligo-dT size selections c. After rRNA-depletions d. After library construction

Library prep

• PolyA selection
• rRNA depletion
• Size selection
• PCR amplification (See section PCR duplicates)
• Stranded (directional) libraries
  • Accurately identify sense/antisense transcript
  • Resolve overlapping genes
• Exome capture
• Library normalisation
• Batch effect

Sequencing:

There are several sequencing platforms and technologies out there being used. It is important to provide the following pieces of information: a. Platform: Illumina, PacBio, Oxford Nanopore, etc… b. Format: Single-end, Pair-end, c. Read Length: 101 bases, 125 bases, etc… d. Unusual barcode placement and sequence: Some protocols introduce barcodes in noncustomary places. If you are going to deliver a FASTQ file that will contain the barcode sequences in it or other molecular markers – you will need to report both the position in the read(s) where they are and their sequence(s). e. Please provide the sequence of any custom primers that were used to sequence the library

Sequencing depth.

The amount of sequencing needed for a given sample is determined by the goals of the experiment and the nature of the RNA sample. Experiments whose purpose is to evaluate the similarity between the transcriptional profiles of two polyA+ samples may require only modest depths of sequencing. Experiments whose purpose is discovery of novel transcribed elements and strong quantification of known transcript isoforms requires more extensive sequencing.
• Each Long RNA-Seq library must have a minimum of 30 million aligned reads/mate-pairs.
• Each RAMPAGE library must have a minimum of 20 million aligned reads/mate-pairs.
• Each small RNA-Seq library must have a minimum of 30 million aligned reads/mate-pairs.

Quantitative Standards (spike-ins).

It is highly desirable to include a ladder of RNA spike-ins to calibrate quantification, sensitivity, coverage and linearity. Information about the spikes should include the stage of sample preparation that the spiked controls were added, as the point of entry affects use of spike data in the output. In general, introducing spike-ins as early in the process as possible is the goal, with more elaborate uses of different spikes at different steps being optional (e.g. before poly A+ selection, at the time of cDNA synthesis, or just prior to sequencing). Different spike-in controls are needed for each of the RNA types being analyzed (e.g. long RNAs require different quantitative controls from short RNAs). Such standards are not yet available for all RNA types. Information about quantified standards should also include: a) A FASTA (or other standard format) file containing the sequences of each spike in. b) Source of the spike-ins (home-made, Ambion, etc..) c) The concentration of each of the spike-ins in the pool used.

Hong et al., 2016, Principles of metadata organization at the ENCODE data coordination center.

QC FAIL?

https://sequencing.qcfail.com/

Fastq format

FASTQ format is a text-based format for storing both a biological sequence (usually nucleotide sequence) and its corresponding quality scores.

The format is similar to fasta though there are differences in syntax as well as integration of quality scores. Each sequence requires at least 4 lines:

1. The first line is the sequence header which starts with an ‘@’ (not a ‘>’!). Everything from the leading ‘@’ to the first whitespace character is considered the sequence identifier. Everything after the first space is considered the sequence description
2. The second line is the sequence.
3. The third line starts with ‘+’ and can have the same sequence identifier appended (but usually doesn’t anymore).
4. The fourth line are the quality scores

The FastQ sequence identifier generally adheres to a particular format, all of which is information related to the sequencer and its position on the flowcell. The sequence description also follows a particular format and holds information regarding sample information.

@A00261:180:HL7GCDSXX:2:1101:30572:1047/2
AAAATACATTGATGACCATCTAAAGTCTACGGCGTATGCGACTGATGAAGTATATTGCACCACCTGAGGGTGATGCTAATACTACTGTTGACGATAATGCTGATCTTCTTGCTAAGCTTAATATTGTTGGTGTTGAACCTAATGTTGGTG
+
FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF:FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF:FFFFFFFFF
@A00261:180:HL7GCDSXX:2:1101:21088:1094/2
ATCTCACATCGTTCCCTCAAGATTCTGAATTTTGGCAGCTCATTGCATTCTGTGCCGGCACTGGTGGTTCGATGCTTGTCATTGGTTCTGCTGCTGGTGTAGCCTTCATGGGGATGGAGAAAGTCGATTTCTTTTGGTATTTCCGAAAGG
+
FFFFFFFFFFFFFFFFFFFFFF:FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF:FFFFFFFF:FFFFFFFFFFFFFFFFFFFFFFFFFFFFF:FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF
@A00261:180:HL7GCDSXX:2:1101:21251:1125/2
CGGTGGAAAAGGAAACAGCTTTGGAAGGTTGATTCCATTACAGATTCGATTCGAAACTATGGTTCAGATTTCCGATCTTCCACGGGATTTGACAGAGGAGGTGCTCTCTAGGATTCCGGTGACATCTATGAGAGCAGTGAGATTTACTTG

• A00261 : Instrument name
• 180 : run ID
• HL7GCDSXX : Flowcell ID
• 2 : Flowcell lane
• 1101 : tile number within the flowcell lane
• 30572 : X-coordinate of the cluster within tile
• 1047 : Y-coordinate of the cluster within tile
• /2 : member of a pair 1 or 2 (Paired end reads only)

Quality Scores

Quality scores are a way to assign confidence to a particular base within a read. Some sequencers have their own proprietary quality encoding but most have adopted Phred-33 encoding. Each quality score represents the probability of an incorrect basecall at that position.

Phred Quality Score Encoding

Quality scores started as numbers (0-40) but have since changed to an ASCII encoding to reduce filesize and make working with this format a bit easier, however they still hold the same information. ASCII codes are assigned based on the formula found below. This table can serve as a lookup as you progress through your analysis.

Quality Score Interpretation

Once you know what each quality score represents you can then use this chart to understand the confidence in a particular base.

BW algorithm

Reads QC

• Number of reads
• Per base sequence quality
• Per sequence quality score
• Per base sequence content
• Per sequence GC content
• Per base N content
• Sequence length distribution
• Sequence duplication levels
• Overrepresented sequences
• Adapter content
• Kmer content

FastQC

How to make a report?

MultiQC