Before you start the alignment and analysis processes, it us useful to perform some initial quality checks on your raw data. You may also need to pre-process the sequences to trim them or remove adapters. Here we will assume you have paired-end data from one of GSAF's Illumina sequencers.


Use our summer school reservation (CoreNGS-Wed) when submitting batch jobs to get higher priority on the ls6 normal queue today:

sbatch --reservation=CoreNGS-Wed <batch_file>.slurm
idev -m 120 -N 1 -A OTH21164 -r CoreNGS-Wed

FASTQ Quality Assurance tools

The first order of business after receiving sequencing data should be to check your data quality. This often-overlooked step helps guide the manner in which you process the data, and can prevent many headaches.


FastQC is a tool that produces a quality analysis report on FASTQ files.

Useful links:

First and foremost, the FastQC "Summary" should generally be ignored. Its "grading scale" (green - good, yellow - warning, red - failed) incorporates assumptions for a particular kind of experiment, and is not applicable to most real-world data. Instead, look through the individual reports and evaluate them according to your experiment type.

The FastQC reports I find most useful, and why:

  1. Should I trim low quality bases?
    • consult the Per base sequence quality report
      • based on all sequences
  2. Do I need to remove adapter sequences?
    • consult the Adapter Content report
  3. Do I have other contamination?
    • consult the Overrepresented Sequences report
      • based on the 1st 100,000 sequences, trimmed to 75bp
  4. How complex is my library?
    • consult the Sequence Duplication Levels report
    • but remember that different experiment types are expected to have vastly different duplication profiles

For many of its reports, FastQC analyzes only the first ~100,000 sequences in order to keep processing and memory requirements down. Consult the Online documentation for each FastQC report for full details.

Running FastQC

Make sure you're in an idev session. If you're in an idev session, the hostname command will display a name like But if you're on a login node the hostname will be something like

If you're on a login node, start an idev session like this:

Start an idev session
idev -m 120 -N 1 -A OTH21164 -r CoreNGS-Wed

FastQC is available as part of BioContainers on ls6. To make it available:

# Load the main BioContainers module then load the fastqc module
module load biocontainers  # make take a while
module load fastqc

It has a number of options (see fastqc --help | more) but can be run very simply with just a FASTQ file as its argument.

# Setup (if needed)
export CORENGS=/work/projects/BioITeam/projects/courses/Core_NGS_Tools 
mkdir -p $SCRATCH/core_ngs/fastq_prep
cd $SCRATCH/core_ngs/fastq_prep
cp $CORENGS/misc/small.fq .
Running fastqc on a FASTQ file
# make sure you're in your $SCRATCH/core_ngs/fastq_prep directory
cd core_ngs/fastq_prep
fastqc small.fq

Exercise: What did FastQC create?

ls -l shows two new items.

-rw-rw-r-- 1 abattenh G-823651 676531 Jun  9 20:53 small_fastqc.html
-rw-rw-r-- 1 abattenh G-823651 464403 Jun  9 20:53
  • small_fastqc.html is the FastQC report, in HTML format.
  • is a zipped (compressed) directory of FastQC output files.

Let's unzip the .zip file and see what's in it.


What was created?

ls -l shows one new item, the small_fastqc directory (note the "d" in "drwxrwxr-x")

drwxrwxr-x 4 abattenh G-823651      6 Jun 10  2022

ls -l small_fastqc shows the directory contents:

drwxrwxr-x 2 abattenh G-823651      4 Jun 10  2022 Icons
drwxrwxr-x 2 abattenh G-823651      9 Jun 10  2022 Images
-rw-rw-r-- 1 abattenh G-823651  77464 Jun 10  2022
-rw-rw-r-- 1 abattenh G-823651  25602 Jun 10  2022 fastqc_data.txt
-rw-rw-r-- 1 abattenh G-823651 676531 Jun 10  2022 fastqc_report.html
-rw-rw-r-- 1 abattenh G-823651    419 Jun 10  2022 summary.txt

Looking at FastQC output

You can't run a web browser directly from your "dumb terminal" command line environment. The FastQC results have to be placed where a web browser can access them. One way to do this is to copy the results back to your laptop, for example by using scp from your computer (read more at Copying files from TACC to your laptop).

For convenience, we put an example FastQC report at this URL: 

Exercise: Based on this FastQC output, should we trim this data?

The Per base sequence quality report does not look good. The data should probably be trimmed (to 40 or 50 bp) before alignment.

Newer versions of FastQC have slightly different report formats. See this example:

Using MultiQC to consolidate multiple QC reports

FastQC reports are all well and good, but what if you have dozens of samples? It quickly becomes tedious to have to look through all the separate FastQC reports, including separate R1 and R2 reports for paired end datasets.

The MultiQC tool helps address this issue. Once FastQC reports have been generated, it can scan them and create a consolidated report from all the individual reports.

Whats even cooler, is that MultiQC can also consolidate reports from other bioinformatics tools (e.g. bowtie2 aligner statistics, samtools statistics, cutadapt, Picard, and may more). And if your favorite tool is not known by MultiQC, you can configure custom reports fairly easily. For more information, see this recent Byte Club tutorial on Using MultiQC.

Here we're just going to create a MultiQC report for two paired-end ATAC-seq datasets – 4 FASTQ files total. First stage the data:

mkdir -p $SCRATCH/core_ngs/multiqc/fqc.atacseq
cd $SCRATCH/core_ngs/multiqc/fqc.atacseq
cp $CORENGS/multiqc/fqc.atacseq/*.zip .

You should see these 4 files in your $SCRATCH/core_ngs/multiqc/fqc.atacseq directory:  

Now make the BioContainers MultiQC accessible in your environment.

Make sure you're in an idev session. If you're in an idev session, the hostname command will display a name like But if you're on a login node the hostname will be something like

If you're on a login node, start an idev session like this:

Start an idev session
idev -m 120 -N 1 -A OTH21164 -r CoreNGSday3
# Load the main BioContainers module if you have not already
module load biocontainers  # may take a while

# Load the multiqc module and ask for its usage information
module load multiqc
multiqc --help | more
export CORENGS=/work/projects/BioITeam/projects/courses/Core_NGS_Tools
mkdir -p $SCRATCH/core_ngs/multiqc/fqc.atacseq
cd $SCRATCH/core_ngs/multiqc/fqc.atacseq
cp $CORENGS/multiqc/fqc.atacseq/*.zip .

Even though multiqc has many options, it is quite easy to create a basic report by just pointing it to the directory where individual reports are located:

cd $SCRATCH/core_ngs/multiqc
multiqc fqc.atacseq

Exercise: How many reports did multiqc find?

Based on its execution output, it found 4 reports

[WARNING]         multiqc : MultiQC Version v1.12 now available!
[INFO   ]         multiqc : This is MultiQC v1.7
[INFO   ]         multiqc : Template    : default
[INFO   ]         multiqc : Searching 'fqc.atacseq/'
[INFO   ]          fastqc : Found 4 reports
[INFO   ]         multiqc : Compressing plot data
[INFO   ]         multiqc : Report      : multiqc_report.html
[INFO   ]         multiqc : Data        : multiqc_data
[INFO   ]         multiqc : MultiQC complete

Exercise: What was created by running multiqc?

One file was created (multiqc_report.html) and one directory (multiqc_data).

You can see the resulting MultiQC report here:

And an example of a MultiQC report that includes both standard and custom plots is this is the Tag-Seq post-processing MultiQC report produced by the Bioinformatics Consulting Group:

Trimming sequences

There are two main reasons you may want to trim your sequences:

  • As a quick way to remove 3' adapter contamination, when extra bases provide little additional information
    • For example, 75+ bp ChIP-seq reads – 50 bases are more than enough for a good mapping, and trimming to 50 is easier than adapter removal, especially for paired end data.
    • You would not choose this approach for RNA-seq data, where 3' bases may map to a different exon, and that is valuable information.
      • Instead you would specifically remove adapter sequences.
  • Low quality base reads from the sequencer can affect some programs
    • This is an issue with sequencing for genome or transcriptome assembly.
    • Aligners such as bwa and bowtie2 seem to do fine with a few low quality bases, soft clipping them if necessary.

There are a number of open source tools that can trim off 3' bases and produce a FASTQ file of the trimmed reads to use as input to the alignment program.

FASTX Toolkit

The FASTX Toolkit provides a set of command line tools for manipulating both FASTA and FASTQ files. The available modules are described on their website. They include a fast fastx_trimmer utility for trimming FASTQ sequences (and quality score strings) before alignment.

Make sure you're in an idev session. If you're in an idev session, the hostname command will display a name like But if you're on a login node the hostname will be something like

If you're on a login node, start an idev session like this:

Start an idev session
idev -m 120 -N 1 -A OTH21164 -r CoreNGS-Wed

FASTX Toolkit is available as a BioContainers module.

module load biocontainers  # takes a while
module spider fastx
module load fastxtools

Here's an example of how to run fastx_trimmer to trim all input sequences down to 50 bases.

Where does fastx_trimmer read its input from? And where does it write its output? Ask the program for its usage.

# will fastx_trimmer give us usage information?
fastx_trimmer --help

# no, it wants you to use the -h option to ask for help:
fastx_trimmer -h

The usage: its help information

fastx_trimmer [-h] [-f N] [-l N] [-t N] [-m MINLEN] [-z] [-v] [-i INFILE] [-o OUTFILE] 

Because the [-i INFILE] [-o OUTFILE] options are shown in brackets [ ], reading from a file and writing to a file are optional. That means that by default the program reads its input data from standard input and writes trimmed sequences to standard output:

Set up directory for working with FASTQs
export CORENGS=/work/projects/BioITeam/projects/courses/Core_NGS_Tools

# Create a $SCRATCH area to work on data for this course,
# with a sub-direct[1ory for pre-processing raw fastq files
mkdir -p $SCRATCH/core_ngs/fastq_prep

# Make a symbolic links to the original yeast data:
cd $SCRATCH/core_ngs/fastq_prep
ln -s -f $CORENGS/yeast_stuff/
ln -s -f $CORENGS/yeast_stuff/
Trimming FASTQ sequences to 50 bases with fastx_trimmer
# make sure you're in your $SCRATCH/core_ngs/fastq_prep directory
cd $SCRATCH/core_ngs/fastq_prep
zcat | fastx_trimmer -l 50 -Q 33 > trim50_R1.fq
  • The -l 50 option says that base 50 should be the last base (i.e., trim down to 50 bases)
  • The -Q 33 option specifies how base Qualities on the 4th line of each FASTQ entry are encoded.
    • The FASTX Toolkit is an older program written in the time when Illumina base qualities were encoded differently, so its default does not work for modern FASTQ files.
    • These days Illumina base qualities follow the Sanger FASTQ standard (Phred score + 33 to make an ASCII character).

Exercise: compressing fastx_trimmer output

How would you tell fastx_trimmer to compress (gzip) its output file?

Type fastx_trimmer -h (help) to see program documentation

You could supply the -z option like this:

zcat | fastx_trimmer -l 50 -Q 33 -z > trim50_R1.fq.gz

# or, using the -o option:
zcat | fastx_trimmer -l 50 -Q 33 -z -o trim50_R1.fq.gz 

Or you could gzip the output yourself.

zcat | fastx_trimmer -l 50 -Q 33 | gzip > trim50_R1.fq.gz

See the 3x+ difference in file sizes when the output is compressed with ls -lh trim*

Exercise: other fastx toolkit programs

What other FASTQ manipulation programs are part of the FASTX Toolkit?

Type fastx_ then tab twice (completion) to see their names.

The FASTX Toolkit also has programs that work on FASTA files. To see them, type fasta_ then tab twice (completion) to see their names.

Adapter trimming with cutadapt

Data from RNA-seq or other library prep methods that result in short fragments can cause problems with moderately long (50-100bp) reads, since the 3' end of sequences can be read into (or even through) to the 3' adapter at different read offsets . This 3' adapter contamination can cause the "real" insert sequence not to align because the adapter sequence does not correspond to the bases at the 3' end of the reference genome sequence.

Unlike general fixed-length trimming (e.g. trimming 100 bp sequences to 50 bp), specific adapter trimming removes differing numbers of 3' bases depending on where the adapter sequence is found.

You must tell any adapter trimming program what your R1 and R2 adapters look like.

The GSAF website describes the flavors of Illumina adapter and barcode sequences in more detail:  Illumina - all flavors (USE with Caution, this is outdated but can be useful for a basic understanding of the adapters, the GSAF primarily only uses UDI's for all projects).

The cutadapt program, available in BioContainers, is an excellent tool for removing adapter contamination.

Make sure you're in an idev session. If you're in an idev session, the hostname command will display a name like But if you're on a login node the hostname will be something like

If you're on a login node, start an idev session like this:

Start an idev session
idev -m 120 -N 1 -A OTH21164 -r CoreNGS-Wed
module load biocontainers
module spider cutadapt 

module load cutadapt 
cutadapt --help

A common application of cutadapt is to remove adapter contamination from RNA library sequence data. Here we'll show that for some small RNA libraries sequenced by GSAF, using their documented small RNA library adapters.

When you run cutadapt you give it the adapter sequence to trim, and the adapter sequence is different for R1 and R2 reads. Here's what the options look like (without running it on our files yet).

cutadapt command for R1 sequences (GSAF RNA library)
cutadapt -m 22 -O 4 -a AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC <fastq_file>
cutadapt command for R2 sequences (GSAF RNA library)
cutadapt -m 22 -O 4 -a TGATCGTCGGACTGTAGAACTCTGAACGTGTAGA <fastq_file>


  • The -m 22 option says to discard any sequence that is smaller than 22 bases (minimum) after trimming.
    • This avoids problems trying to map very short, highly ambiguous sequences.
  • the -O 4 (Overlap) option says not to trim 3' adapter sequences unless at least the first 4 bases of the adapter are seen at the 3' end of the read.
    • This prevents trimming short 3' sequences that just happen by chance to match the first few adapter sequence bases.

Figuring out which adapter sequence to use when can be tricky. Your sequencing provider can tell you what adapters they used to prep your libraries. For GSAF's adapter layout, please refer to Illumina - all flavors (USE with Caution, this is outdated but can be useful for a basic understanding of the adapters, the GSAF primarily only uses UDI's for all projects) (you may want to read all the "gory details" below later).

The top strand, 5' to 3', of a read sequence looks like this.

Illumina library read layout
<P5 capture> <indexRead2> <Read 1 primer> [insert] <Read 2 primer> <indexRead1> <P7 capture>

The -a argument to cutadapt is documented as the "sequence of adapter that was ligated to the 3' end". So we care about the <Read 2 primer> for R1 reads, and the <Read 1 primer> for R2 reads.

The "contaminent" for adapter trimming will be the <Read 2 primer> for R1 reads. There is only one Read 2 primer:

Read 2 primer, 5' to 3', used as R1 sequence adapter

The "contaminent" for adapter trimming will be the <Read 1 primer> for R2 reads. However, there are three different Read 1 primers, depending on library construction:

Read 1 primer depends on library construction
TCTACACTCTTTCCCTACACGACGCTCTTCCGATCT  # TruSeq Read 1 primer site. This is the RC of the R2 adapter

Since R2 reads are the reverse complement of R1 reads, the R2 adapter contaminent will be the RC of the Read 1 primer used.

For ChIP-seq libraries where reads come from both DNA strands, the TruSeq Read 1 primer is always used.
Since it is the RC of the Read 2 primer, its RC is just the Read 1 primer back.
Therefore, for ChIP-seq libraries the same cutadapt adapter option can be used for both R1 and R2 reads:

Cutadapt adapter sequence for ChIP-seq lib

For RNAseq libraries in this class, we use the small RNA sequencing primer as the Read 1 primer.
The contaminent is then the RC of this, minus the 1st and last bases:

Small RNA library Read 1 primer, 5' to 3', used as R2 sequence adapter
TCTACACGTTCAGAGTTCTACAGTCCGACGATCA    # R1 primer - small RNA sequencing Read 1 primer site, 5' to 3'
TGATCGTCGGACTGTAGAACTCTGAACGTGTAGA    # R2 adapter contaminent (RC of R1 small RNA sequencing Read 1 primer)

Exercise: other cutadapt options

The cutadapt program has many options. Let's explore a few.

How would you tell cutadapt to trim trailing N's?

cutadapt --help | less

Then, in the less pager, type /trim <enter> to look for the first occurrence of the string "trim", then n to look for subsequent occurrences.

The relevant option is --trim-n

How would you control the accuracy (error rate) of cutadapt's matching between the adapter sequences and the FASTQ sequences?

Use the less pager to search for terms like "error" or "accuracy".

cutadapt --help | less

Then, in the less pager, type /error <enter> to look for the first occurrence of the string "error", then n to look for subsequent occurrences.

The relevant option is  -e <floating point error rate> or --error-rate=<floating point error rate>:

-e ERROR_RATE, --error-rate=ERROR_RATE
     Maximum allowed error rate (no. of errors divided by
     the length of the matching region) (default: 0.1)

Suppose you are processing 100 bp reads with 30 bp adapters. By default, how many mismatches between the adapter and a sequence will be tolerated?

cutadapt's default error rate is 0.1 (10%)

Up to three mismatches will be tolerated when the whole 30 bp adapter is found (10% of 30).

If only 20 of the 30 adapter bases are found, up to two mismatches will be tolerated (10% of 20).

How would you require a more stringent matching (i.e., allowing fewer mismatches)?

Providing --error-rate=0.05 (or -e 0.05) as an option, for example, would specify a 5% error rate, or no more than 1 mismatching base in 20.

cutadapt example

Let's run cutadapt on some real human miRNA (micro-RNA) data.

First, stage the data we want to use. This data is from a small RNA library where the expected insert size is around 15-25 bp.

Setup for cutadapt on miRNA FASTQ
mkdir -p $SCRATCH/core_ngs/fastq_prep
cd $SCRATCH/core_ngs/fastq_prep
cp $CORENGS/human_stuff/ .
cp $CORENGS/human_stuff/ .

Exercise: How many reads are in these files? Is it single end or paired end data?

echo $(( `zcat | wc -l` / 4 ))
# or
zcat | wc -l | awk '{print $1 / 4}'

Looking at the FASTQ file names, we see this is two lanes of single-end reads (L004 and L005).

The data from lane 4 has 2,001,337 reads, the data from lane 5 has 2,022,237 reads.

Exercise: How long are the reads?

You could just Look at the size of the actual sequence on the 2nd line of any FASTQ entry and count the characters....

But you're experts now! So challenge yourself.

Use a combination of tail and head to extract the 2nd line of the .gz file.

Then use the wc program, but not with the -l option (check wc --help).

zcat | head -2 | tail -1 | wc -c

These are 101-base reads. wc -c counts the "invisible" newline character, so subtract 1 from the character count it returns for a line.

Here's a way to strip the trailing newline characters from the quality scores string before calling wc -c to count the characters. We use the echo -n option that tells echo not to include the trailing newline in its output. We gemerate that text using sub-shell evaluation (an alternative to backtick evaluation) of that zcat ... command:

echo -n $( zcat | head -2 | tail -1 ) | wc -c

Adapter trimming is a rather slow process, and these are large files. So to start with we're going to create a smaller FASTQ file to work with.

# Remember, FASTQ files have 4 lines per read
zcat | head -2000 > miRNA_test.fq

Now execute cutadapt like this:

Setup for cutadapt on miRNA FASTQ
export CORENGS=/work/projects/BioITeam/projects/courses/Core_NGS_Tools
mkdir -p $SCRATCH/core_ngs/fastq_prep
cd $SCRATCH/core_ngs/fastq_prep
cp $CORENGS/human_stuff/miRNA_test.fq .
Cutadapt command for R1 FASTQ
  2> miRNA_test.cuta.log \
  | gzip > miRNA_test.cutadapt.fq.gz


  • Here's one of those cases where you have to be careful about separating standard output and standard error
    • cutadapt writes its FASTQ output to standard output by default, and writes summary information to standard error.
  • In this command we first redirect standard error to a log file named miRNA_test.cuta.log using the 2> syntax, in order to capture cutadapt diagnostics.
    • Then the remaining standard output is piped to gzip, whose output is the redirected to a new compressed FASTQ file.
    • (Read more about Standard streams and redirection)

You should see a miRNA_test.cuta.log log file when the command completes. How many lines does it have?

wc -l miRNA*log

Take a look at the first 15 lines.

head -15 miRNA_test.cuta.log

It will look something like this:

cutadapt log file
This is cutadapt 1.18 with Python 3.7.1
Command line parameters: -m 20 -a AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC miRNA_test.fq
Processing reads on 1 core in single-end mode ...
Finished in 0.06 s (113 us/read; 0.53 M reads/minute).

=== Summary ===

Total reads processed:                     500
Reads with adapters:                       492 (98.4%)
Reads that were too short:                  64 (12.8%)
Reads written (passing filters):           436 (87.2%)

Total basepairs processed:        50,500 bp
Total written (filtered):         10,909 bp (21.6%)


  • The Total reads processed line tells you how many sequences were in the original FASTQ file.
  • Reads with adapters tells you how many of the reads you gave it had at least part of an adapter sequence that was trimmed.
    • Here adapter was found in nearly all (98.4%) of the reads. This makes sense given this is a short (15-25 bp) RNA library.
  • The Reads that were too short line tells you how may sequences were filtered out because they were shorter than our minimum length (20) after adapter removal (these may have ben primer dimers).
    • Here ~13% of the original sequences were removed, which is reasonable.
  • Reads written (passing filters) tells you the total number of reads that were written out by cutadapt
    • These are reads that were at least 20 bases long after adapters were removed

The cutadapt --help output describes its usage as follows:

    cutadapt -a ADAPTER [options] [-o output.fastq] input.fastq

From this we see that the input.fastq is a required argument. Clearly, it can be a FASTQ file, and it can be compressed based on this help:

Compressed input and output is supported and
auto-detected from the file name (.gz, .xz, .bz2).
Use the file name '-' for standard input/output.

And this says that input reads can also be provided on standard input, if that argument is a hyphen ( - ). So input data can come:

  • from a file named as an argument:
    • cutadapt -a CGTAATTCGCG -o small.trim.fq  small.fq
    • and that input.fastq file can be provided in one of three compression formats
  • from standard input if the input.fastq argument is replaced with a dash ( - )
    • cat small.fq | cutadapt -a CGTAATTCGCG -o small.trim.fq  -

What about cutadapt output (the trimmed reads)? The brackets around the usage -o option indicate that the resulting trimmed FASTQ can be written to a file, but is not by default. This implies that cutadapt by default writes its results to standard output. So output can go

  • to a file, using the -o option
    • cutadapt -a CGTAATTCGCG -o small.trim.fq  small.fq
  • to standard output without the -o option
    • cutadapt -a CGTAATTCGCG small.fq 1> small.trim.fq

Finally, as we've seen, cutadapt also writes diagnostic output. Where does it go? The usage line doesn't say anything about diagnostics explicitly. But in the Output section of cutadapt --help:

   -o FILE, --output=FILE
        Write trimmed reads to FILE. FASTQ or FASTA format is
        chosen depending on input. The summary report is sent
        to standard output. Use '{name}' in FILE to
        demultiplex reads into multiple files. Default: write
to standard output

Careful reading of this suggests that:

  • When the trimmed output is sent to a file with the -o output.fastq option,
    • diagnostics are written to standard output
      • so can be redirected to a log file with 1> small.trim.log
    • cutadapt -a CGTAATTCGCG -o small.trim.fq  small.fq 1> small.trim.log
  • But when the -o option is omitted, and output goes to standard output,
    • diagnostics must be written to standard error
      • so can be redirected to a log file with 2> trim.log
    • cutadapt -a CGTAATTCGCG small.fq 1> small.trim.fq 2> small.trim.log

paired-end data considerations

Special care must be taken when removing adapters for paired-end FASTQ files.

  • For paired-end alignment, aligners want the R1 and R2 fastq files to be in the same name order and be the same length.
  • Adapter trimming can remove FASTQ sequences if the trimmed sequence is too short
    • But different R1 and R2 reads may be discarded
    • This leads to mis-matched R1 and R2 FASTQ files, which can cause problems with aligners like bwa
  •  cutadapt has a protocol for re-syncing the R1 and R2 when the R2 is trimmed.

running cutadapt in a batch job

Now we're going to run cutadapt on the larger FASTQ files, and also perform paired-end adapter trimming on some yeast paired-end RNA-seq data.

First stage the 4 FASTQ files we will work on:

Setup for cutadapt
mkdir -p $SCRATCH/core_ngs/cutadapt
cd $SCRATCH/core_ngs/cutadapt
cp $CORENGS/human_stuff/ .
cp $CORENGS/human_stuff/ .
cp $CORENGS/custom_tracks/Yeast_RNAseq_L002_R1.fastq.gz .
cp $CORENGS/custom_tracks/Yeast_RNAseq_L002_R2.fastq.gz .

Instead of running cutadapt on the command line, we're going to submit a job to the TACC batch system to perform single-end adapter trimming on the two lanes of miRNA data, and paired-end adapter trimming on the two yeast RNAseq FASTQ files.

Paired end adapter trimming is rather complicated, so instead of trying to do it all in one command line we will use one of the handy BioITeam scripts that handles all the details of paired-end read trimming, including all the environment setup.

Paired-end RNA fastq trimming script

The BioITeam has an a number of useful NGS scripts that can be executed by anyone on ls6. or stampede2. They are located in the /work/projects/BioITeam/common/script/ directory.

For groups that participate in BRCF pods, the scripts are available in /mnt/bioi/script on any compute server.

The name of the script we want is Just type the full path of the script with no arguments to see its help information:

You should see something like this: 2020_04_20
Trim adapters from single- or paired-end sequences using cutadapt. Usage: <in_fq> <out_pfx> [ paired min_len adapter1 adapter2 ]

Required arguments:
  in_fq     For single-end alignments, path to input fastq file.
            For paired-end alignemtts, path to the the R1 fastq file
            which must contain the string 'R1' in its name. The
            corresponding 'R2' must have the same path except for 'R1'
  out_pfx   Desired prefix of output files.
Optional arguments:
  paired    0 = single end alignment (default); 1 = paired end.
  min_len   Minimum sequence length after adapter removal. Default 32.
            Specifiy 'illumina' for AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC
            (standard Illumina TruSeq3 indexed adapter).
            Specifiy 'illumina' for AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTA
            (standard Illumina TruSeq universal adapter).
Environment variables:
  show_only 1 = only show what would be done (default not set)
  keep      1 = keep intermediate file(s) (default 0, don't keep)
  cuta_args other cutadapt options (e.g. '--trim-n --max-n=0.25')

  export cuta_args='-O 5'; my.fastq.gz h54_b1 1 40 my_fastq.gz yeast_b3 1 28 Illumina Illumina

Based on this information, here are the 3 cutadapt commands we want to execute:

Adapter trimming commands file
/work/projects/BioITeam/common/script/ H54_miRNA_L004 0 20
/work/projects/BioITeam/common/script/ H54_miRNA_L005 0 20
/work/projects/BioITeam/common/script/ Yeast_RNAseq_L002_R1.fastq.gz yeast_rnaseq 1

Let's put these command into a cuta.cmds commands file. But first we need to learn a bit about Editing files in Linux.

Exercise: Create cuta.cmds file

Use nano or emacs to create a cuta.cmds file with the 3 cutadapt processing commands above. If you have trouble with this, you can copy a pre-made commands file:

cd $SCRATCH/core_ngs/cutadapt
cp $CORENGS/tacc/cuta.cmds .

Or use this "cat to MARKER" trick, also known as an heredoc. The MARKER tag can be anything; below it is EOL.

cd $SCRATCH/core_ngs/cutadapt
cat > cuta.cmds << EOL
/work/projects/BioITeam/common/script/ H54_miRNA_L004 0 20
/work/projects/BioITeam/common/script/ H54_miRNA_L005 0 20
/work/projects/BioITeam/common/script/ Yeast_RNAseq_L002_R1.fastq.gz yeast_rnaseq 1

When you're finished you should have a cuta.cmds file that is 3 lines long (check this with wc -l).

Next create a batch submission script for your job and submit it to the normal queue with a maximum run time of 2 hours.

Since batch jobs can't be submitted from an idev session, make sure you are back on a login node (just exit the idev session).

Create and submit cutadapt batch script
cd $SCRATCH/core_ngs/cutadapt -j cuta.cmds -n cuta -t 01:00:00 -a OTH21164 -q normal 
sbatch --reservation=CoreNGS-Wed cuta.slurm
showq -u

# or, if you're not on the reservation: -j cuta.cmds -n cuta -t 01:00:00 -a OTH21164 -q development
sbatch cuta.slurm
showq -u

How will you know your job is done?

Your cuta job will no longer be displayed in the showq -u output.

You can also ask the batch system to send you email when the job starts to run and completes. The has a -e option that lets you provide an email on the command line. Or you can set the EMAIL_ADDRESS environment variable if you want to always fill in this field:


All our BioITeam scripts, if they complete without errors, will write a line to their logfile that includes the words "completed successfully!". So another way of checking that each command completed is to search for that text in the logfiles.

Here we use the powerful grep (general regular expression processor) tool:

# to see all lines in any log file with the words 'completed successfully':
grep 'completed successfully!' *.log

# or, to simply count how many lines have the the words 'completed successfully':
grep 'completed successfully!' *.log | wc -l

# or to see only the names of files that have the words 'completed successfully'
grep -l 'completed successfully!' *.log

# to see the names of the files that do NOT have the words 'completed successfully'
# note we only look in *.cuta.log files since the pass0, pass1, pass2 log files will
# never contain those words.
grep -L 'completed successfully!' *.cuta.log

You should see several log files when the job is finished:

  • H54_miRNA_L004.cuta.log, H54_miRNA_L005.cuta.log, yeast_rnaseq.cuta.log
    • these are the main execution log files, one for each command
  • H54_miRNA_L004.acut.pass0.log, H54_miRNA_L005.acut.pass0.log
    • these are cutadapt statistics files for the single-end adapter trimming
    • their contents will look like our small example above
  • yeast_rnaseq.acut.pass1.log, yeast_rnaseq.acut.pass2.log
    • these are cutadapt statistics files from trimming the R1 and R2 adapters, respectively.

Take a look at the first part of the yeast_rnaseq.acut.pass1.log log file:

more yeast_rnaseq.acut.pass1.log

It will look something like this:

cutadapt pass1 log file
This is cutadapt 1.18 with Python 3.7.1
Command line parameters: -m 32 -a GATCGGAAGAGCACACGTCTGAACTCCAGTCAC --trim-n --paired-output yeast_rnaseq_R2.tmp.cuta.fastq -o yeast_rnaseq_R1.tmp.cuta.fastq Yeast_RNAseq_L002_R1.fastq.gz Yeast_RNAseq_L002_R2.fastq.gz
Processing reads on 1 core in paired-end legacy mode ...
WARNING: Legacy mode is enabled. Read modification and filtering options
*ignore* the second read. To switch to regular paired-end mode,
provide the --pair-filter=any option or use any of the
-A/-B/-G/-U/--interleaved options.
Finished in 151.54 s (24 us/read; 2.55 M reads/minute).

=== Summary ===

Total read pairs processed:          6,440,847
  Read 1 with adapter:               3,875,741 (60.2%)
  Read 2 with adapter:                       0 (0.0%)
Pairs that were too short:             112,847 (1.8%)
Pairs written (passing filters):     6,328,000 (98.2%)
  • cutadapt started with 6,440,847 read pairs
    • 112,847 reads were discarded as too short after the R1 adapter was removed
      • the same 112,847 reads were discarded from both the R1 and R2 files
    • the remaining 6,328,000 were then subjected to pass2 processing.

The corresponding yeast_rnaseq.acut.pass2.log file looks like this:

cutadapt pass2 log file
This is cutadapt 1.18 with Python 3.7.1
Command line parameters: -m 32 -a TGATCGTCGGACTGTAGAACTCTGAACGTGTAGA --paired-output yeast_rnaseq_R1.cuta.fastq -o yeast_rnaseq_R2.cuta.fastq yeast_rnaseq_R2.tmp.cuta.fastq yeast_rnaseq_R1.tmp.cuta.fastq
Processing reads on 1 core in paired-end legacy mode ...
Finished in 83.64 s (13 us/read; 4.54 M reads/minute).

=== Summary ===

Total read pairs processed:          6,328,000
  Read 1 with adapter:                  90,848 (1.4%)
  Read 2 with adapter:                       0 (0.0%)
Pairs that were too short:                   0 (0.0%)
Pairs written (passing filters):     6,328,000 (100.0%)

Total basepairs processed: 1,198,172,994 bp
  Read 1:   639,128,000 bp
  Read 2:   559,044,994 bp
Total written (filtered):  1,197,894,462 bp (100.0%)
  • cutadapt started with 6,328,000 pass1 reads
    • no additional reads were discarded as too short after the R2 adapter was removed
    • so new R1 and R2 files, both with 6,328,000 reads, were produced
      • yeast_rnaseq_R1.cuta.fastq.gz and yeast_rnaseq_R2.cuta.fastq.gz

Exercise: Verify that both adapter-trimmed yeast_rnaseq fastq files have 6,328,000 reads

echo "$((`zcat yeast_rnaseq_R1.cuta.fastq.gz | wc -l` / 4))"
zcat yeast_rnaseq_R2.cuta.fastq.gz | wc -l | awk '{printf("%d\n", $1/4)}'

For more on printf, which is available in most programming languages, see

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