Assembly independent functional annotation of short-read data using SOFA: Short-ORF functional annotation
Bibliographic record
Abstract
Accurate description of the microbial communities driving matter and energy transformations in complex ecosystems such as soils cannot yet be effectively accomplished using assembly-based approaches despite the rise of next generation sequencing technologies. Here we present SOFA, an open source pipeline enabling comparative functional annotation of unassembled short-read data. The pipeline attempts to merge mate pairs in fastq files, predicts open reading frames (ORFs) on merged and unmerged reads as small as 70 bps, and completes an additional step, we term `deduplication'. Deduplication prevents the double counting of ORFs predicted from unmerged paired-end reads by checking for homologous annotations that span the same ORF, allowing for quantitatively accurate predictions. The effectiveness of SOFA is validated with both simulated and bone fide soil metagenomes, and empirical results are compared to existing strategies for obtaining accurate ORF counts, and an analytical model of read duplication. SOFA enables downstream processing stages within the existing MetaPathways pipeline, and is available for download as a stand alone application at https://github.com under the MIT license.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".