|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|
Picard BAM/SAM, JRE
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- person_outline Simon Andrews <>
FastQC in pipelines(1156)
[…] yielded 115,183,830 raw reads for the amazon molly p. formosa, 117,678,742 for the sailfin molly p. latipinna, and 100,309,634 for the atlantic molly p. mexicana (table ). the quality control with fastqc showed that the phred quality was lower in the first three base pairs of the reads, as well as at the end. after adapter clipping and trimming, the number of read pairs was 56,916,341 for p. […]
[…] by using trimmomatic () to remove adaptor sequences and reads less than 70 bp when a sliding window of 4 bp and minimum phred score of 30 was applied. subsequent read quality was assessed using fastqc (http://www.bioinformatics.babraham.ac.uk/projects/fastqc), and assembly was performed using spades genome assembler v3.9.0 (). an average genome coverage of 1,254× for phage apc_jm3.2 […]
[…] using a paired-end read length of 2 × 101 bp, and genome libraries were constructed using a truseq dna pcr-free library preparation kit (illumina, inc.). the quality of the raw data was checked with fastqc (), and adapters were trimmed with trimmomatic (). the genomes were assembled with velvet version 1.2.10 () and annotated with the ncbi prokaryotic genome annotation pipeline ()., the ehec […]
[…] an agilent technologies 2100 bioanalyzer with a high-sensitivity chip. the libraries were enriched using adapter-compatible pcr primers., the quality of the raw sequence reads was confirmed using fastqc v0.10.1, and the adapters and low-quality reads were filtered. the filtered high-quality sequences were aligned and mapped onto the b73 reference genome […]
[…] for each strain. the sequencing data are available in the arrayexpress database (www.ebi.ac.uk/arrayexpress) under accession number e-mtab-5275., the quality of the sequencing reads was checked with fastqc 0.11.4 (www.bioinformatics.babraham.ac.uk/projects/fastqc). reads were aligned to the s. cerevisiae genome r64 using hisat 2.0.3-beta . non-uniquely mapping reads (mapq < 10) […]
FastQC in publications(3473)
[…] prepared using nebnext ultra dna library prep kit for illumina (new england biolabs) and sequenced on illumina hiseq 4000 using 50 bp single-read chemistry., raw chip-seq reads were evaluated with fastqc (version 0.11.4). quality-filtering and trimming was done with prinseq-lite (version 0.20.4). resulting high-quality reads were simultaneously mapped against the mus musculus (grcm38) […]
[…] sequences and default filtering parameters was performed as suggested in the program’s documentation, with exception of a hard clip of the first 13 bases of the reads; this latter step was based on fastqc visualization of the read qualities before trimming, which indicated significantly lower read qualities in those bases as compared to the remainder of the read. trimmed reads that passed […]
[…] the manufacturer’s instructions. seven biological replicate samples of each group were used, and a total of 28 libraries were sequenced., we performed quality control on raw sequence data using the fastqc tool. to obtain high-quality, clean data for analysis, adaptor sequence trimming and removal of low-quality reads were performed. clean reads were aligned with the rat genome using tophat […]
[…] out on an illumina hiseq 2500 platform, generating 125 bp paired-end reads at the australian genome research facility (agrf), parkville, australia. read quality for each sample was assessed with fastqc v.0.11.2, prior to trimming, read mapping, and de novo assembly using clc genomics workbench (qiagen, usa). initial read mapping to the reference c. psittaci 6bc, as well as horse_pl […]
[…] across 4 lanes on an illumina hiseq 2000 instrument (paired end)., see for an overview of the data analysis steps. briefly, quality control (qc) of mirna sequencing data was performed using fastqc before and after adaptor trimming with trimmomatic (). then, the paired-end reads were assembled using pandaseq and aligned to the hg38 genome assembly using bowtie2 (, ). finally, the total […]
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.