Key result
mQC tool provides a modular, easy-to-use solution for visualizing mapped ribosome profiling data quality.
mQC provides an accessible, modular tool for essential post-mapping quality control and data exploration of ribosome profiling datasets.
Supports post-mapping QC in ribosome profiling; leaves open validation for GWAS applications.
BACKGROUND AND OBJECTIVE: Ribosome profiling is a recent next generation sequencing technique enabling the genome-wide study of gene expression in biomedical research at the translation level. Too often, researchers precipitously start trying to test their hypotheses after alignment of their data, without checking the quality and the general features of their mapped data. Despite the fact that these checks are essential to prevent errors and ensure valid conclusions afterwards, easy-to-use tools for visualizing the quality and overall outlook of mapped ribosome profiling data are lacking. METHODS: We present mQC, a modular tool implemented as a Bioconda package and also available in the Galaxy tool shed. Herewith both bio-informaticians as well as non-experts can easily perform the indispensable visualization of both the quality and the general features of their mapped P-site corrected ribosome profiling reads. The user manual, the raw code and more information can be found on its GitHub repository (https://github.com/Biobix/mQC). RESULTS: mQC was tested on multiple datasets to assess its general applicability and was compared to other tools that partly perform similar tasks. CONCLUSIONS: Our results demonstrate that mQC can accomplish an unfilled but essential position in the ribosome profiling data analysis procedure by performing a thorough RIBO-Seq-specific exploration of aligned and P-site corrected ribosome profiling data.
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Verbruggen et al. (2018) studied Ribosome profiling data analysis. mQC tool vs. Other tools was evaluated on General applicability and performance in RIBO-Seq-specific exploration. The mQC tool provides a modular, easy-to-use solution for visualizing the quality and general features of mapped P-site corrected ribosome profiling data.
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