Additional file 2 of Introduction of a multiplex amplicon sequencing assay to quantify DNA methylation in target cytosine markers underlying four selected epigenetic clocks
Creators: name: Pośpiech, Ewelina
name: Pisarek, Aleksandra
name: Rudnicka, Joanna
name: Noroozi, Rezvan
name: Boroń, Michał
name: Masny, Aleksander
name: Wysocka, Bożena
name: Migacz-Gruszka, Kamila
name: Lisman, Dagmara
name: Pruszkowska-Przybylska, Paulina
name: Kobus, Magdalena
name: Szargut, Maria
name: Dowejko, Joanna
name: Stanisz, Kamila
name: Zacharczuk, Julia
name: Zieliński, Piotr
name: Sitek, Aneta
name: Ossowski, Andrzej
name: Spólnicka, Magdalena
name: Branicki, Wojciech
Publisher: name: figshare
Year: 2024
Subjects: subject: Genetics
subject: FOS: Biological sciences

description: Additional file 2. Fig. S1 Raw reads number distribution (A) and normalized read depth (B) for three sequencing runs with three approaches to rebalance the probes performed for the BSPP technology. Fig. S2 Accuracy of DNA methylation measurement for the BSPP technology. Libraries were prepared for 0.5 DNA methylation standards. Fig. S3 Data transformation and impact on the accuracy of age prediction using the original VISAGE blood age model trained on Illumina sequencing data as applied on DNA methylation data generated with Ion AmpliSeq technology. Data were transformed using the following equation: VISAGE blood age Ion AmpliSeq TRANSFORMED = − 1.61 + (VISAGE blood age Ion AmpliSeq*0.93). Fig. S4 Data transformation and impact on the accuracy of PoAm parameter estimation as applied on DNA methylation data generated with Ion AmpliSeq and SureSelect technology. Data were transformed using the following equation: PoAm HTS Transformed = 0.23 + (PoAm HTS*0.72). Fig. S5 Data transformation and impact on the accuracy of Zhang categorical MRS parameter estimation as applied on DNA methylation data generated with Ion AmpliSeq and SureSelect technology. Data were transformed using the following equation: MRS Cat. HTS Transformed = 25.76 + 13.29*MRS Cont. HTS Transformed + 1.81*MRS Cont. HTS Transformed*MRS Cont. HTS Transformed. Fig. S6 Data transformation and impact on the accuracy of Zhang continous MRS parameter estimation as applied on DNA methylation data generated with Ion AmpliSeq and SureSelect technology. Data were transformed using the following equation: MRS Cont. HTS Transformed = − 1.72 + 0.61*MRS Cont. HTS. Fig. S7 Scatterplot of PoAm and chronological age correlation (R=0.024) after applying data transformation. Fig. S8 Scatterplot of the correlation between MRS and chronological age (R=0.783) after data transformation, taking into account the continuous (A) and categorical (B) character of the MRS parameter. Fig. S9 Analysis of the impact of the applied threshold of the minimum number of reads on the precision of DNA methylation determination: mean absolute difference between the observed and expected DNA methylation beta values and the standard deviation of the results. Table S2 Cytosines reaching the requested read depth threshold for the technologies tested. Table S3 Pearson correlation analysis for different age-related parameters and chronological age, using data generated with Ion AmpliSeq technology. Table S4 Pearson correlation analysis of results obtained for individual clocks with different DNA methylation data collection technologies.