CandiMeth: Powerful yet simple visualization and quantification of DNA methylation at candidate genes
Bibliographic record
Abstract
BACKGROUND: DNA methylation microarrays are widely used in clinical epigenetics and are often processed using R packages such as ChAMP or RnBeads by trained bioinformaticians. However, looking at specific genes requires bespoke coding for which wet-lab biologists or clinicians are not trained. This leads to high demands on bioinformaticians, who may lack insight into the specific biological problem. To bridge this gap, we developed a tool for mapping and quantification of methylation differences at candidate genomic features of interest, without using coding. FINDINGS: We generated the workflow "CandiMeth" (Candidate Methylation) in the web-based environment Galaxy. CandiMeth takes as input any table listing differences in methylation generated by either ChAMP or RnBeads and maps these to the human genome. A simple interface then allows the user to query the data using lists of gene names. CandiMeth generates (i) tracks in the popular UCSC Genome Browser with an intuitive visual indicator of where differences in methylation occur between samples or groups of samples and (ii) tables containing quantitative data on the candidate regions, allowing interpretation of significance. In addition to genes and promoters, CandiMeth can analyse methylation differences at long and short interspersed nuclear elements. Cross-comparison to other open-resource genomic data at UCSC facilitates interpretation of the biological significance of the data and the design of wet-lab assays to further explore methylation changes and their consequences for the candidate genes. CONCLUSIONS: CandiMeth (RRID:SCR_017974; Biotools: CandiMeth) allows rapid, quantitative analysis of methylation at user-specified features without the need for coding and is freely available at https://github.com/sjthursby/CandiMeth.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.043 | 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".