Quality assurance as well as concern regarding sample authenticity in biotechnology and bioengineering have always been serious topics in both production and research. While next generation sequencing techniques greatly enhance in-depth analyses of DNA-samples, they, however, introduce additional error-sources. Resulting error-signatures can neither be easily removed from resulting sequencing data nor even recognized, which is mainly due to the massive amount of data. Altogether biologists and sequencing facility technicians face not only issues of minor relevance, e.g. suboptimal library preparation, but also serious incidents, including sample-contamination or even mix-up, ultimately threatening the accuracy of biological conclusions.
Unfortunately, most of the problems and evolving questions raised above can't be solved and answered entirely. However, the sequencing data quality control tool of the CLC Genomics Workbench provides various generic tools to assist in the quality control process of the samples by assessing and visualizing statistics on:
- Sequence-read lengths and base-coverages
- Nucleotide-contributions and base-ambiguities
- Quality scores as emitted by the base-caller
- Over-represented sequences and hints suggesting contamination events
This tool aims at assessing above quality-indicators and investigates proper and improper result presentation. The inspiration comes from the FastQC-project (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/).