Abstract

High-throughput sequencing has allowed unprecedented insight into the composition and function of complex microbial communities. With the onset of metatranscriptomics, it is now possible to interrogate the transcriptome of multiple organisms simultaneously to get an overview of the gene expression of the entire community. Studies have successfully used metatranscriptomics to identify and describe relationships between gene expression levels and community characteristics. However, metatranscriptomic datasets contain a rich suite of additional information which is just beginning to be explored. In this minireview we discuss the different computational strategies for handling antisense expression in metatranscriptomic samples and highlight their potentially detrimental effects on downstream analysis and interpretation. We also surveyed the antisense transcriptome of multiple genomes and metagenome-assembled genomes (MAGs) from five different datasets and found high variability in the level of antisense transcription for individual species which were consistent across samples. Importantly, we tested the hypothesis that antisense transcription is primarily the product of transcriptional noise and found mixed support, suggesting that the total observed antisense RNA in complex communities arises from a compounded effect of both random, biological and technical factors. Antisense transcription can provide a rich set of information, from technical details about data quality to novel insight into the biology of complex microbial communities.\n\nKey pointsO_LISeveral fundamentally different approaches are used to handle antisense RNA\nC_LIO_LIPrevalence of antisense RNA is highly variable between communities, genomes, and genes.\nC_LIO_LIAntisense RNA is likely an opaque mixture of technical, biological and random effects\nC_LI