STRAF—A convenient online tool for STR data evaluation in forensic genetics. (September 2017)

• Record Type: Journal Article
• Authors: Gouy, Alexandre; Zieger, Martin
• Is Part Of: Forensic science international. Volume 30(2017)
• Page Start: 148
• Page End: 151
• Publication Date: 2017-09
• Subjects: Population -- Data analysis -- Statistics -- STR -- Software; Forensic genetics -- Periodicals; Génétique légale -- Périodiques; Forensic genetics; Electronic journals; Periodicals; 614.1
• DOI: 10.1016/j.fsigen.2017.07.007
• Languages: English
• ISSNs: 1872-4973

Abstract

Highlights: STRAF (STR Analysis for Forensics) computes all the required standard statistics for autosomal STR population data in forensic genetics. It is freely available, easy to use, requires a very simple input file and results can be downloaded as convenient tables or displayed graphically online. STRAF includes a PCA module that can be used for quality control and population substructure detection. Abstract: Population data in forensic genetics has to be checked for a variety of statistical parameters before it can be employed for case work. A lot of very powerful statistical tools are available for this task, most of them developed by labs having their research focus on population genetics or evolution. However, most of these programs require a substantial amount of experience. In addition, to our knowledge, none of the freely available programs calculates all the common parameters for a population study in forensic genetics at once, based on a single input file. We present here a convenient online tool that fills this gap. STRAF (STR Analysis for Forensics) provides an intuitive interface and input file format and computes all the relevant parameters for a classical population study based on autosomal STR data at once and in a convenient way. In addition, STRAF includes a PCA module that can be used for population substructure detection or quality control. The results generated by the program were verified by recalculating parameters from an already published population study.