Sensitive and fast mapping of di-base encoded reads
Bibliographic record
Abstract
Bioinformatics (2011) 27(4), 1915–1921. The authors find it worth mentioning that the parameters used to run the PerM mapper were not optimal to achieve full sensitivity. Based on the new recommendations of the developers of PerM, we used the latest version of PerM (v. 0.3.6), and updated two parameters as follows: –seed F2 (full sensitivity for 1 SNPs); -v 2 (number of mismatches); -k 1 000 000 (maximum number of alignment for a read); -A (report all possible mapping for a reads). Previously, we have used ‘–seed S20 -k 10000 -v 4’. With this update, PerM now achieves full sensitivity in our simulation experiment. With real datasets (Table 6), PerM tends to map more reads compared with Bowtie, but maps slightly less than Mapreads and SOCS. We would like to apologize for the previous parameter sets we used for PerM, due to our misinterpretation of its documentation. We now update the relevant rows in Tables 3 and 6 as follows. Performance of PerM with simulated datasets considering the new parameters Reads are simulated from human reference genome build 35 (chromosome 1). Set 1: no errors; Set 2: color errors; Set 3: substitutions. Performance of PerM with real datasets using the new parameters
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.011 |
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".