ReadQC toolbox

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Perform quality control on your data before its too late

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Short-read quality control and cleanup tools

While some tools can deal rather well with reads of mixed qualities and contaminations, other will be biased and will produce incorrect results. As always in NGS data analysis, continuous and extensive quality control of the data at each step will prevent bias effects, allow comparison of your results with gold standards, and ensure that you do not perform useless and lengthy runs with 'bad' quality data.

Handicon.png Always perform QC at each step of your analyses, this time investment will never be lost

A number of tools allow controlling NGS data at different levels. Some are presented below that show some levels of redundancy but also unique features.

  • cutadapt filters out reads that contain a provided adapter (taken from FastQC reports)
  • fastQC analyse reads and find biases
  • fastX_toolkit cleans reads and counts read metrics
  • PrinSeq, analyze and clean your reads
  • Qualimap, plot mapping result QC and identify biases.
  • RSeQC, perform various QC analyses on bam files obtained after read mapping (and more).
  • seq_crumbs, perform various filtering on reads, including identification of the read Phred scale.
  • trimmomatic for the trimming and cleaning of Illlumina reads.

Common sources of errors in NGS data can be found on the QCFAIL [1] web resource.


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