Mg-Traits pipeline: advancing functional trait-based approaches in metagenomics
Bibliographic record
Abstract
Microorganisms comprise an immense phylogenetic and metabolic diversity, inhabit every conceivable niche on earth, and play a fundamental role in global biogeochemical processes. Among others, their study is highly relevant to develop biotechnological applications, understand ecosystem processes and monitor environmental systems. Functional traits (FTs) (i.e., measurable properties of an organism that influence its fitness (McGill et al. 2006)) provide complementary information to the taxonomic composition to improve the characterization of microbial communities and study their ecology (Martiny et al. 2012). The application of FT-based approaches can be particularly enhanced when coupled with metagenomics, which as a culture-independent method, allows us to obtain the genetic material of microorganisms from the environment: Metagenomic data can be used to compute functional traits at the genome level from a random sample of individuals in a microbial community, irrespective of their taxonomic affiliation (Fierer et al. 2014). Previous works using FT-based approaches in metagenomics include the study of community assembly processes (Burke et al. 2011) and responses to environmental change (Leff et al. 2015), and ecosystem functioning (Babilonia et al. 2018). In this work, we present the Metagenomic Traits pipeline: Mg-Traits. Mg-Traits is dedicated to the computation of 25 (and counting) functional traits in short-read metagenomic data, ranging from GC content and amino acid composition to functional diversity and average genome size (see Fig. 1). As an example application, we used the Mg-Traits pipeline to process the 139 prokaryotic metagenomes of the TARA Oceans data set (Sunagawa et al. 2015). In this analysis, we observed that the computed metagenomic traits track community changes along the water column, which denote microorganisms’ environmental adaptations. Mg-Traits allows the systematic computation of a comprehensive set of metagenomic functional traits, which can be used to generate a functional and taxonomic fingerprint and reveal the predominant life-history strategies and ecological processes in a microbial community. Mg-Traits contributes to improving the exploitation of metagenomic data and facilitates comparative and quantitative studies. Considering the high genomic plasticity of microorganisms and their capacity to rapidly adapt to changing environmental conditions, Mg-Traits constitutes a valuable tool to monitor environmental systems. The Mg-Traits pipeline is available at https://github.com/pereiramemo/metagenomic_pipelines. It is programmed in AWK, BASH, and R, and it was devised using a modular design to facilitate the integration of new metagenomic traits.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".