Reconstruction of small subunit ribosomal RNA from high‐throughput sequencing data: A comparative study of metagenomics and total RNA sequencing

Abstract

Abstract The small subunit (SSU) ribosomal RNA (rRNA) is the most commonly used marker for the identification of microbial taxa, but its full‐length reconstruction from high‐throughput sequencing (HTS) data remains challenging. Metagenomics and total RNA sequencing (total RNA‐Seq) are target‐PCR‐free HTS methods that are used to characterize microbial communities and simultaneously reconstruct SSU rRNA sequences. However, more testing is required to determine and improve their effectiveness. We processed metagenomics and total RNA‐Seq data retrieved from a commercially available mock microbial community and an aquarium sample using 112 combinations of data processing tools. We determined the SSU rRNA reconstruction completeness of both sequencing methods for both samples and analysed the impact of data processing tools on SSU rRNA completeness. In contrast to metagenomics, total RNA‐Seq allowed for the complete or near‐complete reconstruction of all mock community SSU rRNA sequences and generated up to 438 SSU rRNA sequences with ≥80% completeness from the aquarium sample using only 1/5 of an Illumina MiSeq run. SSU rRNA completeness of metagenomics significantly correlated with the genome size of mock community species. Data processing tools impacted SSU rRNA completeness, in particular the utilized assemblers. These results are promising for the high‐throughput reconstruction of novel full‐length SSU rRNA sequences and could advance the simultaneous application of multiple ‐omics approaches in routine environmental assessments to allow for more holistic assessments of ecosystems.

Keywords

• bioinformatics
• data processing tool benchmarking
• metagenomics
• metatranscriptomics
• microbial identification
• mock community