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Trimming low quality bases

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 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.

FASTX-Toolkit is available via the TACC module system.

FASTX_toolkit module description
module spider fastx
module load fastx_toolkit

Let's run fastx_trimmer to trim all input sequences down to 90 bases:

Run fastx_Trimmer
fastx_trimmer -i data/Sample1_R1.fastq -l 90 -Q 33 -o Sample1_R1.trimmed.fastq

 

  • The -l 90 option says that base 90 should be the last base (i.e., trim down to 90 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. These days Illumina base qualities follow the Sanger FASTQ standard (Phred score + 33 to make an ASCII character).

Exercise: fastx toolkit programs

What other fastx manipulation programs are part of the fastx toolkit?

 Hint

Type fastx_ then tab to see their names
See all the programs like this:

fastx toolkit programs
ls $TACC_FASTX_BIN

Exercise: What if you just want to get rid of reads that are too low in quality?

 

fastx_quality_filter syntax
fastq_quality_filter -q <N> -p <N> -i <inputfile> -o <outputfile>
-q N: Minimum Base quality score
-p N: Minimum percent of bases that must have [-q] quality

Let's try it on our data- trim it to only include reads with atleast 80% of the read having a quality score of 30 or above.

Run fastx_quality_filter
fastq_quality_filter -q 20 -p 80 -i data/Sample1_R1.fastq -Q 33 -o Sample1_R1.filtered.fastq

Exercise: Compare the results of fastq_trimmer vs fastq_quality_filter


Compare results
grep '^@HWI' Sample1_R1.trimmed.fastq |wc -l
grep '^@HWI' Sample1_R1.filtered.fastq |wc -l


Adaptor Trimming

Data from RNA-seq or other library prep methods that resulted in very short fragments can cause problems with moderately long (50-100bp) reads since the 3' end of sequence can be read through to the 3' adapter at a variable position. This 3' adapter contamination can cause the "reql" 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 40 or 50 bp), adapter trimming removes differing numbers of 3' bases depending on where the adapter sequence is found.

The GSAF website describes the flavaors of Illumina adapter and barcode sequence in more detail https://wikis.utexas.edu/display/GSAF/Illumina+-+all+flavors

 

WARNING: ADAPTORS FOR RNA LIBRARIES
Below commands are for data from old ligation-based RNA-libraries. In this case, the R1 adaptor is different from the R2 adaptor.
 
For current stranded RNA libraries (such as dUTP, used by GSAF), the R1 adaptor would be the same as the R2 adaptor.
 
Fastqc reports will give you an idea about which adaptor is present in your data. Further, it's always a good idea to grep -c <partofdaptorseq> <fastqfile> to make sure you have the right adaptor sequence before trimming.

FASTX Toolkit

One of the programs available as part of the fastx toolkit does a crude job of clipping adaptors out of sequences. 

fastx_clipper will clip a certain nucl. sequence (eq: adapter) from your reads.

fastx_Clipper general syntax
fastx_clipper -a <adapter> -i <inputfile> -o <outputfile> -l <discardSeqsShorterThanN>

Cutadapt

The cutadapt program is an excellent tool for removing adapter contamination. The program is not available through TACC's module system but we've installed a copy in our $BI/bin directory. Cutadapt has some advantages over fastx_clipper:

  • Cutadapt allows for mismatches.
  • Cutadapt allows for paired end support.

cutadapt general syntax
cutadapt -a <adapter> -e <errorRate> -m <minLength> -o <outputFile> <InputFile>

When you run cutadapt you give it the adapter sequence to trim, and this is different for R1 and R2 reads.  

cutadapt command for R1 sequences
cutadapt -m 22 -a GATCGGAAGAGCACACGTCTGAACTCCAGTCAC -o Sample1_R1.cutadapt.fastq Sample1_R1.fastq
cutadapt command for R2 sequences
#If there was a R2 fastq file
cutadapt -m 22 -a TGATCGTCGGACTGTAGAACTCTGAACGTGTAGA -o Sample1_R2.cutadapt.fastq Sample1_R2.fastq  

Notes:

  • The -m 22 option says to discard any sequence that is smaller than 22 bases after trimming. This avoids problems trying to map very short, highly ambiguous sequences.

Paired end commands are a little more complicated: It's a multi step process: first providing both R1 and R2 files to create tmp files which are input to the second cutadapt command.

cutadapt command for R2 sequences
cutadapt -a GATCGGAAGAGCACACGTCTGAACTCCA -m 22 --paired-output tmp.2.fastq -o tmp.1.fastq  Sample1_R1.fastq Sample1_R2.fastq &
cutadapt -a ATCGTCGGACTGTAGAACTCTGAACGTG -m 22 --paired-output trimmed.1.fastq -o trimmed.2.fastq tmp.2.fastq tmp.1.fastq &
rm tmp.1.fastq tmp.2.fastq

**** NOTE: If the $BI/bin cutadapt doesnt work, use the one installed in my work directory. To use it, do this first:

Set PATH first
 PATH=/work/01184/daras/bin/cutadapt-1.3/bin:$PATH
 The gory details on the *-a* adapter sequence argument

Please refer to https://wikis.utexas.edu/display/GSAF/Illumina+-+all+flavors for Illumina library adapter layout.

 

Below information holds true from old ligation-based RNA-libraries. In this case, the R1 adaptor is different from the R2 adaptor.
 
For current stranded RNA libraries (such as dUTP, used by GSAF), the R1 adaptor would be the same as the R2 adaptor.
 
Fastqc reports will give you an idea about which adaptor is present in your data. Further, it's always a good idea to grep -c <partofdaptorseq> <fastqfile> to make sure you have the right adaptor sequence before trimming.

 

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