OEZY: Optimising EnZYme selection for best performing terminal restriction fragment length polymorphism analysis using ARB. Issue 2 (16th September 2015)

• Record Type: Journal Article
• Authors: Mühling, Martin; Beier, Rico; Müller, Patricia; Petzsch, Patrick; Drechsel, Anna; Schlömann, Michael; Labudde, Dirk
• Editors: Vamosi, Jana
• Is Part Of: Methods in ecology and evolution. Volume 7:Issue 2(2016:Feb.)
• Page Start: 242
• Page End: 248
• Publication Date: 2015-09-16
• Subjects: archaea -- bacteria -- genetic fingerprint analysis -- microbial diversity -- terminal restriction fragment length polymorphism, TRFLP; Ecology -- Periodicals; Evolution -- Periodicals; 577
• DOI: 10.1111/2041-210X.12463
• Languages: English
• ISSNs: 2041-210X

Abstract

Summary: Terminal restriction fragment length polymorphism (TRFLP) analysis remains a useful technique to obtain insights into the genetic diversity of microbial populations. A crucial parameter of the technique is the selection of appropriate restriction endonucleases (REs) to achieve high resolution between the PCR‐amplified fragments of a marker gene (usually a ribosomal RNA gene). However, despite the development of several computer‐supported programmes to improve the selection of REs for TRFLP analysis, there is still a lack of software that offers both of two aspects: first, availability of a sequence data base from which sequences can easily and without further formatting and ranking be selected for analysis; secondly, selection of sets of REs for highest genetic resolution while providing the possibility to assess and quantify the correlation of the TRFs with the phylogeny of the target group of 16S rRNA sequences. Here, we present a new and freely available software tool which utilises ARB in combination with the silva data base of hundreds of thousands of aligned ribosomal RNA genes or user‐submitted sequences as basis for the selection of optimal sets of REs of various sizes. Apart from coping with missing sequence information and providing extensive information on the obtained TRF patterns, this new programme for O ptimising E n ZY me selection for best performing TRFLP analysis using ARB (OEZY) also assesses the level at which the resulting TRF pattern reflects the phylogeny based on the data base gene sequences. Optimising EnZYme is a substantial extension to hitherto available software as it opens the chance to correctly predict the phylogenetic position of yet unknown sequence types. Choosing REs that lead to a high correlation between the resulting TRFs and the phylogeny of the micro‐organisms based on the nucleotide sequence of the marker gene makes it likely that the TRFs also fall within the corresponding phylogenetic clade. OEZY therefore provides a diagnostic tool for the analysis of microbial populations.