Eukfinder: a pipeline to retrieve microbial eukaryote genome sequences from metagenomic data
Bibliographic record
Abstract
ABSTRACT Whole-genome shotgun (WGS) metagenomic sequencing of microbial communities enables the discovery of the functions, physiologies, and evolutionary histories of prokaryotic and eukaryotic microbes. However, metagenomic studies of microbial eukaryotes lag due to challenges in identifying and assembling high-quality genomes from WGS data. To address this problem, we developed Eukfinder, a bioinformatics pipeline that identifies potential eukaryotic sequences from WGS metagenomic data, with a complementary binning workflow for recovering nuclear and mitochondrial genomes. Eukfinder uses two specialized databases for read/contig classification, customizable to specific data sets or environments. We tested Eukfinder on simulated gut microbiome data sets which included varying numbers of reads from the protist Blastocystis , a human gut commensal. We also applied Eukfinder to previously published human gut microbiome WGS metagenomic data to recover new genomes of Blastocystis . Compared to other workflows, Eukfinder offers the potential to recover high-quality, near-complete genomes of diverse eukaryotes, including different Blastocystis subtypes, without relying on a reference genome. With sufficient sequencing depth, Eukfinder outperforms similar tools for recovering eukaryotic genomes from metagenomic data. Eukfinder is a valuable tool for reference-independent and cultivation-free studies of eukaryotic microbial genomes from environmental WGS metagenomic samples. IMPORTANCE Advancements in next-generation sequencing have made whole-genome shotgun (WGS) metagenomic sequencing an efficient method for de novo reconstruction of microbial genomes from various environments. Thousands of new prokaryotic genomes have been characterized; however, the large size and complexity of protistan genomes have hindered the use of WGS metagenomics to sample microbial eukaryotic diversity. Eukfinder enables the recovery of eukaryotic microbial genomes from environmental WGS metagenomic samples. Retrieval of high-quality protistan genomes from diverse metagenomic samples increases the number of reference genomes available. This aids future metagenomic investigations into the functions, physiologies, and evolutionary histories of eukaryotic microbes in the gut microbiome and other ecosystems.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".