Page Comparison

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idev  -m 240180 -r CCBB_Bio_Summer_School_2016_Day2 -A UT-2015-05-18 -N 2 -n 8

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See the Wikipedia FASTQ format page for more information.

Now that you know the basics, see if you can complete the following exercises on your own.

Exercise: Examine the 2nd sequence in a FASTQ file

What is the 2nd sequence in the file $WORK$BI/GVA2016gva_course/mapping/data/SRR030257_1.fastq is?

head $WORK$BI/GVA2016gva_course/mapping/data/SRR030257_1.fastq 

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wc -l $WORK$BI/GVA2016gva_course/mapping/data/SRR030257_1.fastq 

Exercise: Counting FASTQ file lines

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gunzip -c $WORK$BI/GVA2016gva_course/mapping/data/SRR030257_2.fastq.gz | wc -l

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While checking the number of reads a file has can solve some of the most basic problems, it doesn't really provide any direct evidence as to the quality of the sequencing data. To get this type of information before starting meaningful analysis other programs must be used.

FASTQ Evaluation Tools

The Place your sticky note on your computer when you have made it this far and start looking over the fastqc links below. Once everyone has caught up we will go over this together.

FASTQ Evaluation 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 that could require you to redo an entire analysis after they rear their ugly heads.

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

Useful links:

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 Online documentation for FastQC 

• Good Illumina Data
• Bad Illumina Data
• Adapter Dimer Contamination

First and foremost, the FastQC "Summary" on the left 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.

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FastQC is available from the TACC module system on lonestar. Interactive GUI versions are also available for Windows and Macintosh and can be downloaded from the Babraham Bioinformatics web site.FastQC creates a sub-directory for each analyzed FASTQ file, so we should copy the file we want to look at locally first. Here's how to run FastQC using the version we installed:. We don't want to clutter up our work space so copy the SRR030257_1.fastq file to a new directory named BDIB_fastqc_tutorial on scratch, use the module system to load fastqc, use fastqc's help option after the module is loaded to figure out how to run the program. Once the program is completed use scp to copy the important file back to your local machine (The bold words are key words that may give you a hint of what steps to take next)

mkdir $SCRATCH/BDIB_fastqc_tutorial
cd $SCRATCH/BDIB_fastqc_tutorial
cp $BI/gva_course/webmapping/yeast_stuffdata/Sample_Yeast_L005_R1.catSRR030257_1.fastq .gz
module .load fastqc
# 
runningfastqc the-h program fastqc Sample_Yeast_L005_R1.cat.fastq.gz

 # examine program options
fastqc SRR030257_1.fastq  # examinerun extra options
fastqc -hthe program

Exercise: FastQC results

What did FastQC create?

drwxrwxr-rwxr-xr-x 41 abattenhded G-803889     4096802740 498588268 May 2023 2212:5906 Sample_Yeast_L005_R1.cat_fastqc
-rw-rwSRR030257_1.fastq
-rw-r--r-- 1 abattenhded G-803889802740    198239291714 May 2023 2212:5907 Sample_Yeast_L005_R1.catSRR030257_1_fastqc.ziphtml
-rw-r--rwxrr-xr-x 1 abattenhded G-803889 51065629802740    455677 May 2023 2212:5907 SampleSRR030257_Yeast1_L005_R1.cat.fastq.gz
fastqc.zip

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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. You should copy You should copy the results back to your local machine (via scp or a GUI secure ftp client) to open them in a web browser.If you want to skip that step (we recommend doing it for practice!), we have put a copy of the output at this URL:

# on tacc terminal
pwd
 
# on new terminal of local computer
scp <username>@ls5.tacc.utexas.edu/BioITeam/yeast_stuff/Sample_Yeast_L005_R1.cat_fastqc/fastqc_report.html
:<pwd_results_from_other_window>/SRR030257_1_fastqc.html ~/Desktop
 
# open the newly transfered file from from the desktop and see how the data looks

Exercise: Should we trim this data?

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Samstat

The samstat program can also produce a quality report for FASTQ files. (We also use it again later to report on aligned sequences in a BAM file).

This program is not available through the TACC module system but is available in our $BI/bin directory (which is on your $PATH because of our common profile). You should be able just to type samstat and see some documentation.

Running samstat on FASTQ files

# setup
cds
mkdir samstat_test
cd samstat_test
cp $BI/gva_course/mapping/data/SRR030257_1.fastq .

# run the program
samstat SRR030257_1.fastq

This produces a file named SRR030257_1.fastq.html which you need to view in a web browser. We put a copy at this URL:

http://loving.corral.tacc.utexas.edu/bioiteam/SRR030257_1.fastq.html

FASTQ Processing Tools

Trimming low quality bases

Low quality base reads from the sequencer can cause an otherwise mappable sequence not to align. 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.

module spider fastx_toolkit
module load fastx_toolkit

Here's an example of how to run fastx_trimmer to trim all input sequences down to 50 bases. By default the program reads its input data from standard input and writes trimmed sequences to standard output:

FASTQ Processing Tools

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 and fastx_clipper for trimming specific sequences (such as adapters).

FASTX-Toolkit is available via the TACC module system.

module spider fastx_toolkit
module load fastx_toolkit

Trimming low quality bases

Low quality base reads from the sequencer can cause an otherwise mappable sequence not to align. 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, but  fastx_trimmer has the advantage of being a module on TACC and therefore the easiest to use. By default the program reads its input data from standard input and writes trimmed sequences to standard output, use what you know about piping and printing text to the screen to trip the fastq file to 34 bases.

cat SRR030257_1.fastq | fastx_trimmer -l 34 > SRR030257_1.trimmed.fastq

• The cat command prints the contents of the SRR030257_1.fastq file to the screen
• The | redirects that output to the fastx_trimmer command
• The -l 34 option says that base 34 should be the last base (i.e., trim down to 34 bases)
• The > redirects that output to the new file on the right side, in this case SRR030257_1.trimmed.fastq

Exercise: compressing the fastx_trimmer output

Compressed files are smaller, easier to transfer, and many programs allow for their use directly. How would you tell fastx_trimmer to compress (gzip) its output file?

cat SRR030257_1.fastq | fastx_trimmer -l 34 -z > SRR030257_1.trimmed.fastq.gz

cat SRR030257_1.fastq | fastx_trimmer -l 5034 -Q| 33gzip > SRR030257_1.trimmed.fastq.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. These days Illumina base qualities follow the Sanger FASTQ standard (Phred score + 33 to make an ASCII character).

Exercise: compressing the fastx_trimmer output

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gz

Both of the above solutions give the same final product, but are clearly achieved in different ways. This is done to show you that data analysis is a results driven process, if the result is correct, and you know how you got the result it is correct as long as it is reproducable.

Adapter trimming

 As mentioned above, fastx_clipper can be used to trim specific sequences, and based on our fastqc analysis, the sequence AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA is significantly overrepresented in our data. How would you use fastx_clipper to remove those sequences from the fastq file?

gunzip -c $BI/web/yeast_stuff/Sample_Yeast_L005_R1.cat.fastq.gz | fastx_trimmer -l 50 -Q 33 -z > trimmed.fq.gz

gunzip -c $BI/web/yeast_stuff/Sample_Yeast_L005_R1.cat.fastq.gz | fastx_trimmer -l 50 -Q 33 | gzip > trimmed.fq.gz

Exercise: fastx toolkit programs

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fastx_clipper -i SRR030257_1.trimmed.fastq -o SRR030257_1.trimmed.depleted.fastq -a AAAAAAAAAAAAAAAAAAAA -l 34 -n 

Other fastx toolkit programs

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

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 The available modules are described on their website, but we can also learn some things from the command prompt using module spider fastx_toolkit.

ls $TACC_FASTX_BIN

Adapter trimming

Data from RNA-seq or other library prep methods that resulted in very short fragments can cause problems with moderately long (100-250 base) reads since the 3' end of sequence can extend through to the 3' adapter at a variable position and even past the end of the fragment. 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 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

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.

The most common application of cutadapt is to remove adapter contamination from small RNA library sequence data, so that's what we'll show here. Note that this step is increasingly needed for genomic sequencing of MiSeq data with 250 base reads.

Running cutadapt on small RNA library data

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

cutadapt -m 22 -O 10 -a AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC

cutadapt -m 22 -O 10 -a TGATCGTCGGACTGTAGAACTCTGAACGTGTAGA

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.
• the -O 10 option says not to trim 3' adapter sequences unless at least the first 10 bases of the adapter are ssen 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.

<P5 capture> <indexRead2> <Read 1 primer> [insert] <Read 2 primer> <indexRead1> <P7 capture>

AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC

TCTACACGTTCAGAGTTCTACAGTCCGACGATCA    # small RNA sequencing primer site
CAGGTTCAGAGTTCTACAGTCCGACGATCA        # "other"
TCTACACTCTTTCCCTACACGACGCTCTTCCGATCT  # TruSeq Read 1 primer site. This is the RC of the R2 adapter

raries, both R1 and R2 reads}
cutadapt -a GATCGGAAGAGCACACGTCTGAACTCCAGTCAC

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)

Flexbar

Flexbar provides a flexible suite of commands for demultiplexing barcoded reads and removing adapter sequences or low quality regions from the ends of reads.

flexbar -n 1 --adapters adaptors.fna --source example.fastq --target example.ar --format fastq-sanger --adapter-threshold 2 --adapter-min-overlap 6 --adapter-trim-end RIGHT_TAIL

>adaptor1
AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCT
>adaptor2
AGATCGGAAGAGCACACGTCTGAACTCCAGTCACNNNNNNNNATCTCGTATGCCGTCTTCTGCTTG
>adaptor1_RC
AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTAGATCTCGGTGGTCGCCGTATCATT
>adaptor2_RC
CAAGCAGAAGACGGCATACGAGATNNNNNNNNGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT

Note that flexbar only searches for the sequences given (with options to allow for a given number of mismatches) NOT the reverse complement of those sequences therefore you must provide them yourself.

Trimmomatic

Trimmomatic offers similar options to Flexbar with the potential benefit that many illumina adaptor sequences are already "built-in". It is available here.

More Example Data

See if you can figure out what's wrong with these data sets (copy them to your $SCRATCH directory before analyzing them) and then process them to get rid of the problem(s). If you're very ambitious, you could also map them to the reference genomes and perform variant calling before and after cleaning them up to see how the results change. Each file has a different problem.

Example #1: Single-end Illumina MiSeq data for E. coli

$BI/gva_course/read_processing/JJM104_TAAGGCGA-TAGATCGC_L001_R1_001.fastq.gz
$BI/gva_course/read_processing/REL606.fna

Example #2: Paired-end Illumina Genome Analyzer IIx data for E. coli

$BI/gva_course/read_processing/61FTVAAXX_2_R1_ZDB172.fastq.gz
$BI/gva_course/read_processing/61FTVAAXX_2_R2_ZDB172.fastq.gz
$BI/gva_course/read_processing/REL606.fna

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Consider how you might use fastq_quality_filter and fastq_quality_trimmer to limit your data based on quality scores.

Optional Exercise: Improve the quality of R2 the same way you did for R1.

Unfortunately we don't have time during the class to do this, but as a potential exercise in your free time, you could improve R2 the same way you did R1 and use the improved fastq files in the subsequent read mapping and variant calling tutorials to see the difference it can make.

Return to GVA2016 course page