The quality control of fastq data produced by high throughput sequencers is performed by FastQC software. FastQC aims to provide a QC report which can spot problems which originate either in the sequencer or in the starting library material. A series of analysis modules are performed by FastQC as shown in the figure below. A ‘normal’ sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse. Here are URLs of two example reports for illumina data: the good example report and the bad example report.
Specific guidance on how to interpret the output of each module are summarized below.

• Basic Statistics
  

• Per Base Sequence Quality
  

• Per Sequence Quality Scores
  

• Per Base Sequence Content
  

• Per Sequence GC Content
  

• Per Base N Content
  

• Sequence Length Distribution
  

• Duplicate Sequences
  

• Overrepresented Sequences
  

• Adapter Content
  

• Kmer Content
  

• Per Tile Sequence Quality

• 12 fastq files were processed by FASTQC software.
  

• FASTQ files have the same sequence length (150 bases)
  

• FASTQ files have the same quality score Encoding (Sanger / Illumina 1.9)
  

• All the fastq files pass the base sequence quality check
  

• Not all the fastq files pass the gc content check
  

• All the fastq files pass the adapter content check
  

• All the fastq files pass overrepresented sequences check