If you are using FQStat, please cite: Chanumolu SK, Albahrani M, Otu HH. FQStat: a parallel architecture for very high-speed assessment of sequencing quality metrics. BMC Bioinformatics. 2019 Aug 15;20(1):424. [link to publication]
FQStat performs quality control (QC) analysis for DNA/RNA sequencing fastq files. FQStat is written in Python and uses a parallel programming architecture. In contrast to existing tools that assess the QC of sequencing data, FQStat introduces the following improvements:
1. Automatic configuration of system parameters (e.g., core assignment and file segmentation) for optimum performance.
2. Analysis of multiple data sets for comparative assessment of QC parameters.
3. Not being coupled with other preprocessing steps (e.g., low quality base trimming) for an easy-to-use, simple, and fast calculation of QC parameters only.
4. Generating analysis results separately at the lane-, sample-, and experiment-level so the users can pick and choose high quality subsets of the sample and/or experiment data.
5. Flagging low quality lanes and/or samples that warrant further analysis.
6. Generating publication quality output figures and tables.
| Sample fastq file with 10e7 reads used to evaluate the performance of FQStat (Figures 2 & 3) (File Size: 408MB) | Click Here |
| One Hundred 2GB fastq files used to generate Tables 2 & 3 (File Size:50GB) | Click Here |
| Sample dataset used to generate Figure 4 (Sample Results) (File Size: 2GB) | Click Here |
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