Abstract 158: Custom primer design pipeline and analysis workflow for targeted methylation sequencing using NGS Ion AmpliSeq technology
Bibliographic record
Abstract
Abstract Changes in DNA methylation, causing chromosome instability and altered gene expression, have been strongly associated with carcinogenesis. Due to the involvement of methylation in cancer, methylation profiles have been heralded as promising cancer biomarkers. Here, we present a primer design pipeline and an analysis workflow that we have developed to design and analyze custom methylation panels and detect methylation status. An automated primer design pipeline for methylation sequencing has been developed, consisting of genome conversion, primer selection, amplicon tiling, and generation of optimal amplicons. Custom methylation panels can be designed using pre-converted genomes or reference genome sequences for any other organism which can then be converted. The pipeline has the capability to create custom targeted panels specific to any methylation sites of interest. The pipeline designs Ion AmpliSeq primers to enable high multiplexing and robust amplification of low abundance or degraded DNA. Following the creation of a custom panel, a complete 3-day workflow has been developed, comprising bisulfite conversion, library construction, template preparation, sequencing and data analysis. This 3-day protocol offers manual or automated library options, low input (10-20ng DNA) and a flexible multiplexed approach with quantitative information at single base pair resolution. Sequencing is performed on the Ion GeneStudio S5 system. The bioinformatics analysis has been streamlined into a downloadable plugin performing alignment and DNA methylation calling for amplicons on both the Watson and Crick strands. To evaluate the in silico performance of the primer design pipeline for targeted bisulfite sequencing, a custom methylation panel was created using a set of 48 oncology markers from the BLUEPRINT consortium. These markers were also used for the Ion AmpliSeq Methylation Panel for Cancer Research, which was compared to the custom methylation panel to evaluate the performance of the pipeline. Key metrics from in silico design such as total number of degenerate oligos, mean amplicon length and average Tm spread for the custom methylation panel are equal to or better than Ion AmpliSeq Methylation Panel for Cancer Research. To assess the sequencing performance of the panel, two control gDNA samples were used. The expected average methylation status across all CpGs were >98% and <5% for the first sample and the second sample, respectively. The evaluation was also carried out with an equal mixture of these two samples. The wet lab testing of the custom methylation panel generated comparable results to the Ion AmpliSeq Methylation Panel for Cancer Research. The primer design pipeline and 3-day workflow provide custom design of targeted methylation panels along with quantitative analysis of relevant oncology markers from low DNA input. Citation Format: Zunping Luo, Loni Pickle, Andrew Hatch, Aren Ewing, Fiona Hyland, David Berman, Palak Patel, Mark Andersen. Custom primer design pipeline and analysis workflow for targeted methylation sequencing using NGS Ion AmpliSeq technology [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 158.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.036 | 0.028 |
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".