Next generation sequencing (NGS) technologies generate huge amounts of sequencing data. Several microbial genome projects, in particular fungal whole genome sequencing, have used NGS techniques, because of their cost efficiency. However, NGS techniques also demand for computational tools to process and analyze massive datasets. Implementation of few data processing steps, including quality and length filters, often leads to a remarkable improvement in the accuracy and quality of data analyses. Choosing appropriate parameters for this purpose is not always straightforward, as these will vary with the dataset. In this study we present the FastQFS (Fastq Quality Filtering and Statistics) tool, which can be used for both read filtering and filtering parameters assessment. There are several tools available, but an important asset of FastQFS is that it provides the information of filtering parameters that fit best to the raw dataset, prior to computationally expensive filtering. It generates statistics of reads meeting different quality and length thresholds, and also the expected coverage depth of the genome which would be left after applying different filtering parameters. The FastQFS tool will help researchers to make informed decisions on NGS reads filtering parameters, avoiding time-consuming optimization of filtering criteria.
