Gist – an ensemble approach to the taxonomic classification of metatranscriptomic sequence data
Bibliographic record
Abstract
ABSTRACT The study of whole microbial communities through RNA-seq, or metatranscriptomics, offers a unique view of the relative levels of activity for different genes across a large number of species simultaneously. To make sense of these sequencing data, it is necessary to be able to assign both taxonomic and functional identities to each sequenced read. High-quality identifications are important not only for community profiling, but to also ensure that functional assignments of sequence reads are correctly attributed to their source taxa. Such assignments allow biochemical pathways to be appropriately allocated to discrete species, enabling the capture of cross-species interactions. Typically read annotation is performed by a single alignment-based search tool such as BLAST. However, due to the vast extent of bacterial diversity, these approaches tend to be highly error prone, particularly for taxonomic assignments. Here we introduce a novel program for generating taxonomic assignments, called Gist, which integrates information from a number of machine learning methods and the Burrows-Wheeler Aligner. Uniquely Gist establishes the most appropriate weightings of methods for individual genomes, facilitating high classification accuracy on next-generation sequencing reads. We validate our approach using a synthetic metatranscriptome generator based on Flux Simulator, termed Genepuddle. Further, unlike previous taxonomic classifiers, we demonstrate the capacity of composition-based techniques to accurately inform on taxonomic origin without resorting to longer scanning windows that mimic alignment-based methods. Gist is made freely available under the terms of the GNU General Public License at compsysbio.org/gist .
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".