To assemble or not to resemble—A validated Comparative Metatranscriptomics Workflow (CoMW)
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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.
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A version of this preprint has been published in the Open Access journal GigaScience (see paper https://doi.org/10.1093/gigascience/giz096), where the paper and peer reviews are published openly under a CC-BY 4.0 license.
These peer reviews were as follows:
Reviewer 1: http://dx.doi.org/10.5524/REVIEW.102982
Reviewer 2: http://dx.doi.org/10.5524/REVIEW.102983
Reviewer 3: http://dx.doi.org/10.5524/REVIEW.102984
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