Abstract 158: Custom primer design pipeline and analysis workflow for targeted methylation sequencing using NGS Ion AmpliSeq technology
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,036 | 0,028 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».