Under-representation of repetitive sequences in whole-genome shotgun sequence databases: an illustration using a recently acquired transposable element
Bibliographic record
Abstract
It is widely accepted in a conceptual framework that repetitive sequences, especially those with high sequence homogeneity among copies, tend to be under-represented in whole-genome shotgun sequence databases, because of the difficulty of assembling sequence reads into contigs. Although this is easily inferred, there is no quantitative illustration of this phenomenon. An example using a currently used database is expected to contribute to the intuitive understanding of how serious the under-representation is. The present study provides the first quantitative example (in the case of 16 copies of virtually identical, 4.7-kb sequences in a genome of 7 × 10 (8) bp) by comparing the results of BLAST searches of a sequence database (contig N50; 9.8 kb) with those of Southern blot analysis of genomic DNA. This has revealed that the internal regions of the repetitive sequences are under-represented to a striking extent.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".