Hunting Down Frame Shifts: Ecological Analysis of Diverse Functional Gene Sequences.

Functional gene ecological analyses using amplicon sequencing can be challenging as translated sequences are often burdened with shifted reading frames. The aim of this work was to evaluate several bioinformatics tools designed to correct errors which arise during sequencing in an effort to reduce the number of frameshifts (FS). Genes encoding for alpha subunits of biphenyl (bphA) and benzoate (benA) dioxygenases were used as model sequences. FrameBot, a FS correction tool, was able to reduce the number of detected FS to zero. However, up to 44% of sequences were discarded by FrameBot as non-specific targets. Therefore, we proposed a de novo mode of FrameBot for FS correction, which works on a similar basis as common chimera identifying platforms and is not dependent on reference sequences. By nature of FrameBot de novo design, it is crucial to provide it with data as error free as possible. We tested the ability of several publicly available correction tools to decrease the number of errors in the data sets. The combination of maximum expected error filtering and single linkage pre-clustering proved to be the most efficient read processing approach. Applying FrameBot de novo on the processed data enabled analysis of BphA sequences with minimal losses of potentially functional sequences not homologous to those previously known. This experiment also demonstrated the extensive diversity of dioxygenases in soil. A script which performs FrameBot de novo is presented in the supplementary material to the study or available at https://github.com/strejcem/FBdenovo. The tool was also implemented into FunGene Pipeline available at http://fungene.cme.msu.edu/FunGenePipeline/.