MITE Digger, an efficient and accurate algorithm for genome wide discovery of miniature inverted repeat transposable elements
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Miniature inverted repeat transposable elements (MITEs) are abundant non-autonomous elements, playing important roles in shaping gene and genome evolution. Their characteristic structural features are suitable for automated identification by computational approaches, however, de novo MITE discovery at genomic levels is still resource expensive. Efficient and accurate computational tools are desirable. Existing algorithms process every member of a MITE family, therefore a major portion of the computing task is redundant. RESULTS: In this study, redundant computing steps were analyzed and a novel algorithm emphasizing on the reduction of such redundant computing was implemented in MITE Digger. It completed processing the whole rice genome sequence database in ~15 hours and produced 332 MITE candidates with low false positive (1.8%) and false negative (0.9%) rates. MITE Digger was also tested for genome wide MITE discovery with four other genomes. CONCLUSIONS: MITE Digger is efficient and accurate for genome wide retrieval of MITEs. Its user friendly interface further facilitates genome wide analyses of MITEs on a routine basis. The MITE Digger program is available at: http://labs.csb.utoronto.ca/yang/MITEDigger.
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.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it