Benchmarking and optimizing qualitative and quantitative pipelines in environmental metatranscriptomics using mixture controlling experiments.

Metatranscriptomic analysis is increasingly performed in environments to provide dynamic gene expression information on ecosystems, responding to their changing conditions. Many computational methods have undergone remarkable development in the past years, but a comprehensive benchmark study is still lacking. There are concerns regarding the accuracies of the qualitative and quantitative profilers obtained from metatranscriptomic analysis, especially for the microbiota in extreme environments, most of them are unculturable and lack well-annotated reference genomes. Here, we presented a benchmark experiment that included 10 single-species and their cell or RNA-admixtures with the predefined species compositions and varying evenness, simulating the low annotation rate and high heterogeneity. In total, 1 metagenome sample and 24 metatranscriptome were sequenced for the comparisons of 36 combination of analysis methods for tasks ranging from sample preparation, quality control, rRNA removal, alignment strategies, taxonomic profiling, and transcript quantification. For each part of the workflow mentioned above, corresponding metrics have been established to serve as standards for assessment and comparison. Evaluation revealed the performances and proposed an optimized pipeline named MT-Enviro (MetaTranscriptomic analysis for ENVIROnmental microbiome). Our data and analysis provide a comprehensive framework for benchmarking computational methods with metatranscriptomic analysis. MT-Enviro is implemented in Nextflow and is freely available from https://github.com/Li-Lab-SJTU/MT-Enviro.