Software for Automated Analysis of DNA Fingerprinting Gels
Bibliographic record
Abstract
Here we describe software tools for the automated detection of DNA restriction fragments resolved on agarose fingerprinting gels. We present a mathematical model for the location and shape of the restriction fragments as a function of fragment size, with model parameters determined empirically from "marker" lanes containing molecular size standards. Automated identification of restriction fragments involves several steps, including: image preprocessing, to put the data in a form consistent with a linear model; marker lane analysis, for determination of the model parameters; and data lane analysis, a procedure for detecting restriction fragment multiplets while simultaneously determining the amplitude curve that describes restriction fragment amplitude as a function of mobility. In validation experiments conducted on fingerprinted and sequenced Bacterial Artificial Chromosome (BAC) clones, sensitivity and specificity of restriction fragment identification exceeded 96% on restriction fragments ranging in size from 600 base pairs (bp) to 30,000 bp. The integrated suite of software tools, written in MATLAB and collectively called BandLeader, is in use at the BC Cancer Agency Genome Sciences Centre (GSC) and the Washington University Genome Sequencing Center, and has been provided to the Wellcome Trust Sanger Institute and the Whitehead Institute. Employed in a production mode at the GSC, BandLeader has been used to perform automated restriction fragment identification for more than 850,000 BAC clones for mouse, rat, bovine, and poplar fingerprint mapping projects.
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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 | 0.001 | 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 | 0.000 | 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 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".