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Old 05-09-2015, 06:15 PM   #1
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Default How do I know whether my fastq file is ready for alignment?

I received my fastq files from a ChIP-Seq experiment. The Sequencing core guy told me that the raw data was converted from .bcl file format to .fastq format using CASAVA v1.8.2. They run a standard paired-end sequencing reaction to generate 50 bp of sequence in each direction in the Illumina HiSeq2000 platform.
How do I know whether the fastq files are ready for alignment to generate the bam files using Bowtie? This is because I wonder how to know in the fastq file that the adaptors/barcodes have been removed from the read. If the fastq files still contain these primers how do I know it and how do I trim them.
Below and attaching a couple of reads from the fastq files:
@D5VG2KN1:206:C3LG1ACXX:8:1101:1216:2098 1:N:0:GCCAAT
@D5VG2KN1:206:C3LG1ACXX:8:1101:1437:2084 1:N:0:GCCAAT

this is kind of basic but and I a newbie in NGS analysis.
thanks for any clues
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Old 05-09-2015, 06:53 PM   #2
Brian Bushnell
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The easiest way is to trim adapters and see if anything happens. The tool will report whether or not there were adapters.
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Old 05-09-2015, 06:56 PM   #3
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Your file is ready for alignment now but it is always a good idea to pass it through a trimming program to remove any extraneous sequence (adapters etc) that may be present.
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Old 05-11-2015, 06:42 AM   #4
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FastQC (http://www.bioinformatics.babraham.a...ojects/fastqc/) has a module that checks for adapter contamination. It looks for the universal adapter sequences (used in TruSeq RNA/DNA and NEBNext among others), the Nextera sequences and Illumina small RNA sequences.
It looks for both read-through and dimer (over-represented sequences).

In general, it's always a good idea to run your fastq through FastQC to check it looks as expected. HOWEVER, please be aware that the tests implemented assume you are sequencing highly diverse, genomic material. You may get failed modules on perfectly good data as the assumptions made when performing the test are incorrect. Check the documentation and ask yourself "Is this test applicable to my data set"? For example, you may see a high level of duplication when sequencing highly enriched samples (such as ChIP and/or RNA-Seq).
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