|Interface||Command line interface, Graphical user interface|
|Restrictions to use||None|
|Biological technology||Illumina, Pacific Biosciences, Roche|
|Operating system||Unix/Linux, Mac OS, Windows|
|License||GNU General Public License version 3.0, GNU General Public License version 2.0|
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- person_outline Simon Andrews <>
[…] bioanalyzer (agilent) and sequenced using 50 bp paired-end sequencing on an illumina hiseq 2500 (illumina) by nyu genome technology center., atac-seq fastq files were quality trimmed using fastqc (v0.11.5) and mapped to the mouse genome (mm9) using bowtie (v2.2.6)67, using the default options. mapped sam files were sorted and converted to bam files with samtools (v0.1.19-96b5f2294a)56. […]
[…] fisher scientific, waltham, usa). the library was sequenced using paired-end sequencing on an illumina miseq platform (illumina, san diego, ca, usa). the quality of reads was checked with the fastqc tool. de novo assembly was performed with clc genomics workbench (length fraction, 0.5; similarity fraction, 0.8). annotation of the sequence was performed with the ncbi prokaryotic genome […]
[…] santa clara, ca). the libraries were pooled in equimolar amounts, and sequenced on an illumina hiseq 2500 instrument to generate 50-base pair reads., the quality of the raw reads was checked using fastqc (http://www.bioinformatics.babraham.ac.uk/projects/fastqc) for okf6/tert1-parental, okf6/tert1-tobacco and okf6/tert1-smoke samples. cutadapt […]
[…] were then amplified for 15 cycles using primers incorporating unique index tags. fragments were sequenced on an illumina hiseq-3000 using single reads extending 50 bases. samples were qc’d using fastqc, aligned to mm10 using star-align, and counted using htseq-count. technical replicates were collapsed in rstudio and differential expression determined using deseq2. adjusted p < 0.05 […]
[…] in ~ 21.5 million reads per cell line. the five clonally expanded s16 cells were only sequenced once. read quality was assessed using fastqc (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) and reads were aligned to the rat rnor_5.0 (ensembl) reference genome using star . default parameters were used, except only uniquely mapped reads were allowed (~ 82% of total reads […]
1. Very simple tool
I must say this is a fairly simple tool used during the first step of ngs analysis i.e. quality check. A series of tests are perfomed and each test is flagged as pass/warning/fail which depends on how far it departs from the ideal expectations.It is possible that the biological. With not too many options in the UI version, its intuitive to use and work with. Even the command line version is fairly simple.
2. Support for variety of platforms
The tools supports NGS data(fatsq) files generated on many plaforms such Illumina-HiSeq, MiSeq, pacbio, 454 etc. This makes it number one choice to get a quick assesment of quality.
3. Quality check on multiple aspects:
The tool generates colored reports on 8-10 different quality assesment metrics such as read length distribution, quality distribution, per base GC content, adapter contamination level, kmer distibution etc. This provides a global picture of the complete quality of data.
4. Automation Level:
The tools is available in 2 modes - intractive and command line. THe interactive mode allows to view results for multiple files in a single
application. Alternatively, the non interactive mode(command line) generates an HTML report for each file processed.The command line version provides a fantastic utility to automate the analysis on multiple samples (paired and unpaired fastq files) at once. User can define the number of cores using the -t option for no. of threads. It also supports wild card character example the '*' for subsetting the fastq files based on their names or extension.
5. Development, support and documentation:
The tools is well maintained and being constantly developed for bug fixes and new features. It is supported on windows,linux and mac. The latest vertsion v11.5 was released on 08-03-16. Release notes are well maintained and could be found at:
A well written documentation on the analysis modules is available at the below link:
Bugs can be tracked or reported on bugzilla at the below link
Installation is quite easy for a person having intermediate knowledge of working on linux. A well written installation documentation is available at below link:
FastQC is a cross-platform application, written in java.
- cross platform (runs on windows, linux and mac), user friendly
- interactive and command line mode
- comprehensive html summary reports
- suitable for data generated on variety of platforms
- Some of the metrics work on a subset of the overall data hence the interpretation may vary.