Research Article DNAseq Workflow in a Diagnostic Context and an Example of a User Friendly Implementation Beat Wolf, 1,2 Pierre Kuonen, 1 Thomas Dandekar, 2 and David Atlan 3 1 University of Applied Sciences and Arts of Western Switzerland, Perolles 80, 1700 Fribourg, Switzerland 2 University of W¨ urzburg, Am Hubland, 97074 W¨ urzburg, Germany 3 Phenosystems SA, 137 Rue de Tubize, 1440 Braine le Chateau, Belgium Correspondence should be addressed to Beat Wolf; beat.wolf@hefr.ch Received 13 March 2015; Revised 10 May 2015; Accepted 18 May 2015 Academic Editor: Hong Lu Copyright © 2015 Beat Wolf et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Over recent years next generation sequencing (NGS) technologies evolved from costly tools used by very few, to a much more accessible and economically viable technology. Trough this recently gained popularity, its use-cases expanded from research environments into clinical settings. But the technical know-how and infrastructure required to analyze the data remain an obstacle for a wider adoption of this technology, especially in smaller laboratories. We present GensearchNGS, a commercial DNAseq sofware suite distributed by Phenosystems SA. Te focus of GensearchNGS is the optimal usage of already existing infrastructure, while keeping its use simple. Tis is achieved through the integration of existing tools in a comprehensive sofware environment, as well as custom algorithms developed with the restrictions of limited infrastructures in mind. Tis includes the possibility to connect multiple computers to speed up computing intensive parts of the analysis such as sequence alignments. We present a typical DNAseq workfow for NGS data analysis and the approach GensearchNGS takes to implement it. Te presented workfow goes from raw data quality control to the fnal variant report. Tis includes features such as gene panels and the integration of online databases, like Ensembl for annotations or Cafe Variome for variant sharing. 1. Introduction Next generation sequencing (NGS) technologies saw a rapid progression over recent years and improved in many aspects since their introduction. Te data quality, the length of the sequences, and the speed at which the data is generated improved massively all while decreasing the costs associ- ated with the technology [1]. Cost reductions and quality improvements now allow the technology to be used in a diagnostic setting, replacing older technologies such as Sanger sequencing [2]. An increasing number of laboratories are either considering or already implementing NGS for routine diagnostics procedures. Next generation sequencing has many use-cases in diagnostics and, depending on the analysis, diferent types of information are searched for in the sequencing data. A very common type of analysis is the search for SNPs (single-nucleotide polymorphisms) and small indels (insertions and deletions) in patient DNA. With the increasing number of documented variations in well- known disease related genes, this type of analysis became very common. Increasingly, other types of analyses are also used in diagnostics, such as the detection of structural variations, which became possible with the increased quality of sequencing data. Te sequencing data, which can come from various sequencing technologies, can be either tar- geted, such as targeted gene panel sequencing and whole exome sequencing (WES), or nontargeted like whole genome sequencing (WGS). Tose diferent approaches to sequence the patient’s genome produce a varying amount of data [3] and are suited for diferent types of analyses. Te complexity of the analysis increases with the amount of data to process, both from a computational point of view and from a human resources point of view. Te general tendency today is to move away from targeted sequencing towards WGS, as this removes the need for targeted amplicon libraries, as well as making it easier to reanalyse a sample at a later date without Hindawi Publishing Corporation BioMed Research International Volume 2015, Article ID 403497, 11 pages http://dx.doi.org/10.1155/2015/403497