Demonstrating the utility of flexible sequence queries against indexed short reads with FlexTyper
Bibliographic record
Abstract
These data files are for the manuscript titled: Demonstrating the utility of flexible sequence queries against indexed short reads with FlexTyper. Accepted for publication in PLoS Computational Biology. There is a version of the manuscript available on biorxiv: https://www.biorxiv.org/content/10.1101/2020.03.02.973750v2 Within this upload are query files used for querying with the FlexTyper tool: CAAPA_PanGenome_Query.tsv - African Contig Query File hla_seqs_query.tsv - HLA Sequence Query File KIR_query.tsv - KIR Kmer Query File SProbeQuery.tsv - CytoscanHD SNP Query File path_query.txt - Pathogen Query File Additionally there are output files from each of these searches, across nine individuals: *KPIMarkers.tsv - Note the (*), 9 samples searched against the KPI markers *Ancestry_FTSearch_k31s10m200u - Note the (*), 9 samples searched against the ancestry markers ERR1955491_Cytoscan_FTSearch_Paper_k31s10m200u_Final.tsv - Searched against the Cytoscan SNP probes CombinedAfrican_UniqReads_k50s10m100u.tsv - Combined counts across the African Contigs.<br> There are both Centrifuge and Kraken2 reports for mixed patient samples Patient*_Kraken2_Report - Kraken2 Reports for the mixed viral "patient" samples. Patient*Centrifuge - Centrifuge Reports for the mixed viral "Patient" samples The collated Tables for Mixed or pure viral counts: CollatedNonMixedVirus.csv MixedViralAnalysis.tsv CombinedCentrifuge.tsv CollatedNonMixedVirusUniqRead.csv <br> The files within this repository are connected to the analysis directory for code: https://github.com/Phillip-a-richmond/FlexTyper_manuscript Please submit issues with this data to that github repository.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".