To assemble or not to resemble—A validated Comparative Metatranscriptomics Workflow (CoMW)

Abstract

Background Metatranscriptomics has been used widely for investigation and quantification of microbial communities’ activity in response to external stimuli. By assessing the genes expressed, metatranscriptomics provides an understanding of the interactions between different major functional guilds and the environment. Here, we present a de novo assembly-based Comparative Metatranscriptomics Workflow (CoMW) implemented in a modular, reproducible structure. Metatranscriptomics typically uses short sequence reads, which can either be directly aligned to external reference databases (“assembly-free approach”) or first assembled into contigs before alignment (“assembly-based approach”). We also compare CoMW (assembly-based implementation) with an assembly-free alternative workflow, using simulated and real-world metatranscriptomes from Arctic and temperate terrestrial environments. We evaluate their accuracy in precision and recall using generic and specialized hierarchical protein databases. Results CoMW provided significantly fewer false-positive results, resulting in more precise identification and quantification of functional genes in metatranscriptomes. Using the comprehensive database M5nr, the assembly-based approach identified genes with only 0.6% false-positive results at thresholds ranging from inclusive to stringent compared with the assembly-free approach, which yielded up to 15% false-positive results. Using specialized databases (carbohydrate-active enzyme and nitrogen cycle), the assembly-based approach identified and quantified genes with 3–5 times fewer false-positive results. We also evaluated the impact of both approaches on real-world datasets. Conclusions We present an open source de novo assembly-based CoMW. Our benchmarking findings support assembling short reads into contigs before alignment to a reference database because this provides higher precision and minimizes false-positive results.

High temperatures enhance the microbial genetic potential to recycle C and N from necromass in high‐mountain soils

“…To address changes in specific microbial functions with warming, we annotated the predicted genes against different databases: We used the broad and general database EggNOG (Huerta‐Cepas et al, 2016) for (i) an explorative analysis of changes in microbial functional genes and (ii) for assessing changes in genes related with microbial stress response. EggNOG annotations were conducted via the md5nr database (Wilke et al, 2012), enabling the use of assembled metagenomes (Anwar et al, 2019). To assess changes in C‐cycling genes, we annotated the predicted genes against the ‘Carbohydrate‐Active EnZymes database’ (CAZy; Cantarel et al, 2008).…”

“…To assess changes in N‐cycling genes, we annotated the predicted genes against a curated database specific for N‐cycling genes (NCycDB; Tu et al, 2018). Annotation against each of these databases was done with SWORD v1.0.3 (Vaser et al, 2016) with parameters (‐v 10‐5) as in Anwar et al (2019).…”

SCycDB: A curated functional gene database for metagenomic profiling of sulphur cycling pathways

“…Recently, a specific “small database” NCycDB was developed to facilitate shotgun metagenome sequencing data analysis of nitrogen cycling gene families (Tu et al, 2019). NCycDB has been applied to profile N cycling microbial communities from various environments (Anwar et al, 2019; Zhang et al, 2020), demonstrating its high coverage, accuracy and efficiency. Therefore, it is essential to develop a comprehensive and accurate database for fast functional and taxonomic analysis of S cycling microbial communities in metagenomic studies.…”

Transcriptomic responses to warming and cooling of an Arctic tundra soil microbiome

“…In addition, the eggNOG category ‘Defence mechanisms’ decreased between W 2°C and C 2°C . These represent a modest response to soil thaw compared to other studies (Mackelprang et al, 2011; Coolen & Orsi, 2015) and probably reflects the shorter time span between thawing and sampling in our experiment and our more robust annotation protocol (Anwar et al, 2019). The modest response to thaw indicates a lag phase longer than one day, which we initially hypothesized to be caused by the microorganisms focussing on downregulating non-stress genes as part of their stress response (Horn et al, 2007).…”

“…Our low annotation rates were likely partly due to limitations of the database when working with (Arctic) soils and partly to our stringent bioinformatic pipeline filtering less abundant contigs and potential non-coding RNAs. However, the pipeline adds more confidence to the annotation output (Anwar et al, 2019).…”