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INNUca - our software pipeline for automatic QA/QC measures of FastQ reads

posted Jul 11, 2016, 3:39 AM by Mirko Rossi   [ updated Jul 11, 2016, 3:40 AM ]
In the epidemiological surveillance of food-borne pathogens (FBP) using whole-genome sequencing (WGS) data, the raw read sequencing data (and downstream derived data) need to be properly handled and interpreted in order to be useful for public health action, i.e., allow meaningful linkage of cases, and subsequently timely detection of clusters and outbreaks. As such, it is critical to evaluate the potential existence of technical errors occurring in sequencing and downstream analyses (whose effect is still unclear) and further minimize them by setting up careful QA/QC measures. In this regard, the development of software pipeline for automatic QA/QC measures ultimately aims at producing consistent high-quality comparable genomics data. Such data can be subsequently processed through plenty of downstream analytical strategies (i.e. gene-by-gene, FCST, SNPs) depending on the pathogen under epidemiological surveillance and on the type of analysis (e.g. fast clustering analysis for real-time outbreak investigation or a population genetic study for long-term prospective epidemiological surveillance).


We developed an automatic bioinformatics pipeline INNUca - INNUENDO Reads Control and Assembly – for performing QA/QC measures on sequencing. The software pipeline was conceived to be fully flexible, portable and pathogen-independent, thus potentiating its suitability for systematic processing of large numbers of files and minimizing inter-laboratory variation.

The software is available for downloads at https://github.com/INNUENDOCON/INNUca