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Bioinformatics:Sequence QC

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NGS: Sequence QC

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GCATemplates available: grace terra

 module spider FastQC

After running FastQC via the command line, you can ssh to an HPRC cluster enabling X11 forwarding by using the -X option and view the images using the eog tool.

From your desktop:

 ssh -X username@grace.hprc.tamu.edu

From your FastQC working directory on Grace unzip the .zip results file then use eog to view the results in the Images directory:

 eog sample_fastqc/Images/per_sequence_gc_content.png

You can also run FastQC interactively using the FastQC GUI by logging in using X11 forwarding and running the command:



GCATemplates available: ada (w/bwa) ada

RNA-SeQC homepage

 module spider RNA-SeQC

RNA-SeQC is a java program which computes a series of quality control metrics for RNA-seq data.

To run RNA-SeQC after loading the module:

 java -jar $EBROOTRNASEQC/RNA-SeQC_v1.1.8.jar


GCATemplates available: ada

KmerGenie homepage

 module spider KmerGenie

KmerGenie estimates the best k-mer length for genome de novo assembly.


GCATemplates available: no

Qualimap homepage

  • fast analysis across the reference genome of mapping coverage and nucleotide distribution;
  • easy-to-interpret summary of the main properties of the alignment data;
  • analysis of the reads mapped inside/outside of the regions defined in an annotation reference;
  • computation and analysis of read counts obtained from intersting of read alignments with genomic features;
  • analysis of the adequacy of the sequencing depth in RNA-seq experiments;
  • support for multi-sample comparison for alignment data and counts data;
  • clustering of epigenomic profiles.
module spider Qualimap

Enter the following command to see the command line options

qualimap -h

Qualimap will use more than one core so you will need to specify the number of cores using the qualimap -nt option.

You can capture the number of cores you specify in the Slurm parameters with the environment variable $SLURM_CPUS_PER_TASK

For example if you have these Slurm parameters:

#SBATCH --ntasks-per-node=1
#SBATCH --cpus-per-task=28

Then you can use the -n value with the environment variable $SLURM_CPUS_PER_TASK to specify the number of cores for qualimap to use

qualimap -nt $SLURM_CPUS_PER_TASK

If you run qualimap without options, it will start the GUI version. The GUI version works best with the HPRC Portal.

If you would like to use the GUI version, you can login to the OnDemand portal at portal.hprc.tamu.edu and select VNC in the 'Interactive Apps' tab. You might start with all cores and all memory initially until you get an idea of how much memory is required for Qualimap.

When the VNC loads after clicking the blue launch button, you will reach a terminal where you can start the GUI version of Qualimap with the following commands

module purge
module spider Qualimap
# load the latest version using the appropriate module load command then run qualimap

Screen Reads


GCATemplates available: no


FastQ Screen allows you to screen a library of sequences in FastQ format against a set of sequence databases so you can see if the composition of the library matches with what you expect.

module load FastQScreen/0.11.4-intel-2015B-Perl-5.20.0

Version 0.11.4 requires the full path to the alignment binary. Use any of the following in your config file

BOWTIE   /software/easybuild/software/Bowtie/1.1.2-intel-2015B/bin/bowtie
BOWTIE2  /software/easybuild/software/Bowtie2/2.2.9-intel-2015B/bin/bowtie2
BWA      /software/easybuild/software/BWA/0.7.15-intel-2015B/bin/bwa
BISMARK  /software/easybuild/software/Bismark/0.17.0-intel-2015B/bismark

There are some databases already available on Ada. Your config file will look like the following for screening the PhiX and UniVec databases using Bowtie2.

BOWTIE2  /software/easybuild/software/Bowtie2/2.2.9-intel-2015B/bin/bowtie2

DATABASE  PhiX   /scratch/datasets/genome_indexes/ncbi/PhiX/bowtie2/NC_001422.1
DATABASE  UniVec /scratch/datasets/genome_indexes/univec/UniVec_Core/bowtie2/UniVec_Core.fa


GCATemplates available: grace

Kraken2 is a system for assigning taxonomic labels to short DNA sequences, usually obtained through metagenomic studies.

Kraken2 genomic sequence databases for their respective clusters are found in the following Grace directory:


Use a protein database (directories ending in _protein) if you have RNA-seq data

The standard database includes bacterial, archaeal, and viral domains, along with the human genome and a collection of known vectors (UniVec_Core).

Send a request to the HPRC helpdesk if you need other Kraken2 databases.


GCATemplates available: ada

Kraken homepage

Kraken is a system for assigning taxonomic labels to short DNA sequences, usually obtained through metagenomic studies.

You will need to use at least the 256GB which is recommended for the bacteria database.

 module load Kraken/1.1-GCCcore-6.3.0-Perl-5.24.0

After you load the modules, you will need to set the KRAKEN_DB_PATH environment variable

 export KRAKEN_DB_PATH=/scratch/datasets/kraken

Currently only the bacteria database is available, If you need other Kraken databases advise the HPRC helpdesk which db you need.

Sample one-line command for single end reads file on a 256GB node for a fastq zipped file

kraken --preload --db bacteria --threads 20 --gzip-compressed --fastq-output --classified-out my_reads_out --fastq-input my_reads.fastq.gz --output kraken.out

You only have to run the first command with --preload, all other kraken commands do not need the --preload option

Sample command to create labels for the classified hits

kraken-report --db bacteria kraken.out > kraken_report.out



GCATemplates available: grace (pe)

Trimmomatic homepage

Trimmomatic manual

 module spider Trimmomatic

Trimmomatic performs a variety of useful trimming tasks for illumina paired-end and single ended data

Sample command for version 0.39

 java -jar $EBROOTTRIMMOMATIC/trimmomatic-0.39.jar [SE|PE] <options> <files> ... ILLUMINACLIP:$EBROOTTRIMMOMATIC/adapters/TruSeq3-PE.fa:2:30:10


GCATemplates available: no

Cutadapt homepage

 module spider cutadapt

Cutadapt finds and removes adapter sequences, primers, poly-A tails and other types of unwanted sequence from your high-throughput sequencing reads.


To keep all reads that do not align to any database and also reads that align to all databases (conserved reads) you need to do three steps for paired end files:

1) use FastQScreen with the following options: Filter results into new fastq file? => Align and graph results without filtering fastq file Select how many reads to screen => Use all reads Create tagged file? => Yes Then select the aligner and check the checkboxes for the databases you want. This will produce a TAGGED file which you will use as input for the "Select FastQ Screen reads"

2) In the "Select FastQ Screen reads" tool, select the following options: that => match tags Tag type => preconfigured Tags => All db's are 0's or all db's are non-0's

3) Then if these are paired reads, you can use the Re-pair tool.

Sequencing Error Correction


GCATemplates available: no

Quake homepage

 module spider Quake

Quake is a package to correct substitution sequencing errors in experiments with deep coverage (e.g. >15X), specifically intended for Illumina sequencing reads.


GCATemplates available: no

Lighter homepage

 module spider Lighter

Lighter is a kmer-based error correction method for whole genome sequencing data.

Lighter uses sampling (rather than counting) to obtain a set of kmers that are likely from the genome.

Using this information, Lighter can correct the reads containing sequence errors.

Merge overlapping reads


GCATemplates available: grace

FLASH homepage

 module spider FLASH

FLASH (Fast Length Adjustment of SHort reads) is a very fast and accurate software tool to merge paired-end reads from next-generation sequencing experiments.

FLASH is designed to merge pairs of reads when the original DNA fragments are shorter than twice the length of reads.

The resulting longer reads can significantly improve genome assemblies.

They can also improve transcriptome assembly when FLASH is used to merge RNA-seq data.


GCATemplates available: no

Pear homepage

 module spider Pear

PEAR is an ultrafast, memory-efficient and highly accurate pair-end read merger. It is fully parallelized and can run with as low as just a few kilobytes of memory.

PEAR evaluates all possible paired-end read overlaps and without requiring the target fragment size as input. In addition, it implements a statistical test for minimizing false-positive results.


GCATemplates available: no

Coperead homepage

 module spider Coperead

COPE (Connecting Overlapped Pair-End reads) is a method to align and connect the illumina sequenced Pair-End reads of which the insert size is smaller than the sum of the two read length.