Additional file 1 of Nanopore sequencing with unique molecular identifiers enables accurate mutation analysis and haplotyping in the complex lipoprotein(a) KIV-2 VNTR
Creators: name: Amstler, Stephan
name: Streiter, Gertraud
name: Pfurtscheller, Cathrin
name: Forer, Lukas
name: Di Maio, Silvia
name: Weissensteiner, Hansi
name: Paulweber, Bernhard
name: Schönherr, Sebastian
name: Kronenberg, Florian
name: Coassin, Stefan
Publisher: name: figshare
Year: 2024
Subjects: subject: Biochemistry
subject: Microbiology
subject: FOS: Biological sciences
subject: Genetics
subject: Biotechnology
subject: Evolutionary Biology
subject: Biological Sciences not elsewhere classified

description: Additional file 1. Further notes on quality control of the amplification, additional description of methods, all Supplementary Figures and Supplementary Tables that are mentioned in the manuscript. Supplementary Notes: UMI-ONT-Seq showed negligible to absent co-amplification of other KIV units and no detectable amplification bias of specific KIV-2 repeats. Supplementary Methods: Human samples for validation; Mutation density of KIV units; Testing for co-amplification of other KIV units; Extraction of the KIV-2 intronic CA STR; Detailed PCR Protocols; Quality control of the UMI-amplicons; Ligation sequencing amplicons – Native Barcoding Kit V14 and R9. Supplementary Figures: Fig. S1: Principle of the plasmid mixtures containing KIV-2A and KIV-2B inserts in different ratios that were used for benchmarking UMI-ONT-Seq. Fig. S2: Distribution of the number of KIV units per allele, KIV-2 copy number and Lp(a) per sample of 15 European samples from the SAPHIR study. Fig. S3: Flow chart of the UMI-ONT-Seq analysis pipeline. Fig. S4: Concept of the UMI-ONT-Seq based extraction of unique haplotypes per sample. Fig. S5: Variant detection in plasmid mixtures with the UMI-ONT-Seq analysis pipeline using default clustering (left column) or cluster splitting strategy (right column) for the R9 chemistry and HAC basecalling. Fig. S6: Dataset Q-score estimation using KIV-2A and KIV-2B containing plasmids for the R9 chemistry and HAC basecalling algorithm. Fig. S7: Distribution of the consensus sequence Q-score depending on the minimal cluster threshold for all sequencing conditions of the 4 unmixed KIV-2A or KIV-2B plasmids (both PCR2645 and PCR5104). Fig. S8: Performance measures for the R9 chemistry and HAC basecalling algorithm for 15 human gDNA samples (SAPHIR). Fig. S9: Performance of the nanopore-specific low-level variant caller ClairS-TO for 63 genomic DNA samples (15 samples from SAPHIR, 48 samples of 4 populations from 1000G) compared to conventional KIV-2 NGS (SAPHIR, turquoise) and variants derived from publically available WGS data (1000G, red). Fig. S10: Correlation of the variant levels in standard UMI-free ONT-Seq and in UMI ONT Seq with levels determined by deep KIV-2 NGS for all sequencing chemistries and basecalling algorithms. Fig. S11: Bland–Altman plot for ONT-Seq and UMI-ONT-Seq compared to NGS for all sequencing chemistries and basecalling algorithms used. Fig. S12: Correlation of UMI-ONT-Seq estimated and actual number of KIV-2 repeats measured by ddPCR (expected) and haplotype cluster size distribution of 15 human gDNA samples and two 100% KIV-2A or KIV-2B plasmid. Fig. S13: Occurrence of the different haplotypes for the 15 SAPHIR samples. Fig. S14: Correlation of UMI-ONT-Seq and ddPCR based number of KIV-2 repeats and Venn diagrams showing the haplotypes for 3 trios. Fig. S15: Bland–Altman plot of the 48 1000G samples analyzed with UMI-ONT-Seq compared variant levels derived from publically available WGS data. Fig. S16: SNP density of the KIV domains and variant levels in the KIV-2. Fig. S17: Correlation of expected (ddPCR) and UMI-ONT-Seq predicted number of KIV-2 for different steps of the haplotype extraction and number of KIV-2 repeat prediction in 1000G. Fig. S18: Length distribution (relative occurrence) of the extracted STR-region of 63 gDNA samples of 5 different populations (1000G and SAPHIR) and the two unmixed plasmids. Fig. S19: KIV-2 subtype specific haplotype diversity of all 63 human gDNA samples in the present study (1000G, SAPHIR). Fig. S20: Classification of KIV-2A repeat subclusters using 3 positions (35, 3103, 4358) of a previously suggested haplotype in a multi-ancestry sample set (1000G and SAPHIR) of 63 samples. Fig. S21: Classification of KIV-2B and KIV-2C repeats into three main subclusters using 5 positions (50, 2409, 5037, 5045, 5052) in a multi-ancestry sample set (1000G and SAPHIR) of 63 samples. Fig. S22: Classification of KIV-2C containing repeats with the commonly used KIV-2 subtype defining positions (594, 621, 666) in a multi-ancestry sample set (1000G and SAPHIR) of 63 samples. Fig. S23: Classification of the two most frequent disease relevant variants in the KIV-2 VNTR (4925G &gt; A: red and 4733G &gt; A: green) in a multi-ancestry sample set (1000G and SAPHIR) of 63 samples. Supplementary Tables: Table S1: ddPCR validation of the two unmixed and the plasmid mixtures for both fragments. Table S2: ddPCR and qPCR reaction conditions. Table S3: Primer sequences. Table S4: PCR conditions for the 2645 and the 5104 fragment. Table S5: Summary statistics of the sequencing results per kit and basecalling algorithm. Table S6: Sequencing overview R9 HAC. Table S7: Sequencing overview V14 HAC. Table S8: Sequencing overview V14 SUP. Table S9: Sequencing overview V14 SUPDUP. Table S10: Performance measures for all plasmid mixtures analyzed with the initial version of the pipeline. Table S11: Edit distances within UMI sequences in the same UMI cluster for the original ONT pipeline. Table S12: Variant levels and noise of variant level using UMI-ONT-Seq with the default clustering strategy. Table S13: Performance measures for all plasmid mixtures analyzed with by UMI-ONT-Seq with the cluster splitting strategy. Table S14: Noise reduction of detected variant levels of the UMI-ONT-Seq analysis pipeline using either the default clustering or the cluster splitting strategy. Table S15: Variant levels and noise of variant level of the UMI-ONT-Seq analysis pipeline using the cluster splitting strategy. Table S16: Threshold values for reaching a dataset Q-score of Q40. Table S17: Consensus sequence summary statistics for a minimal cluster size threshold of 6 reads per UMI cluster. Table S18: Summary of the error profiles for all cluster thresholds greater than 10. Table S19: Dataset Q-scores per sequencing chemistry and basecalling algorithm after error-profile adjustment. Table S20: Performance measures for all human gDNA samples for all sequencing chemistries and basecalling conditions. Table S21: Summary performance measures for UMI-ONT-Seq. Table S22: Summary performance measures for ONT-Seq without UMIs. Table S23: Differences and correlation values between ddPCR measured and UMI-ONT-Seq predicted number of KIV-2 repeats for different prediction strategies. Table S24: Overview of the three trios, including number of consensus sequences, uniquely occurring haplotypes (used for the Venn diagram), the KIV-2 copy number estimation based on the coverage-corrected haplotypes and the results of the ddPCR. Table S25: KIV-2 subtype occurrence in the SAPHIR and 1000G dataset. Table S26: KIV-2 subtype specific summary performance measures for UMI-ONT-Seq analysis of the 48 1000G samples. Table S27: Per-sample performance measures for UMI-ONT-Seq analysis of the 48 1000G samples for V14 SUP. Table S28: Summary performance measures for UMI-ONT-Seq analysis of the 48 1000G samples without KIV-2B specific variants. Table S29: Correlation between the number of KIV-2 repeats quantified by ddPCR and by UMI-ONT-Seq for each step of the UMI-ONT-Seq KIV-2 quantification algorithm in the 1000G dataset. Table S30: Extracted STR sequences of all 63 human gDNA samples and two unmixed KIV-2A, respectively KIV-2B plasmids. Table S31: Population-specific degeneration of the STR region. Table S32: Diversity of the extracted STR sequences per population. Table S33: Predicted number of KIV-2 repeats for 48 samples and the three trios of the 1000G project using either UMI ONT Seq, ddPCR or the results from Behera et al. Table S34: Number of KIV unit specific sequence patterns found in the raw reads of the 48 1000 Genome samples.