Additional file 1 of Benchmarking datasets for assembly-based variant calling using high-fidelity long reads
Creators: name: Lee, Hyunji
name: Kim, Jun
name: Lee, Junho
Publisher: name: figshare
Year: 2023
Subjects: subject: Genetics
subject: FOS: Biological sciences

description: Additional file 1: Fig. S1. Length distribution of HiFi reads for two multiplexed samples, ALT1 and ALT2 (shown in color). Each vertical dashed line represents the average read length for each sample. Fig. S2. Summary of assembly-based variant calling outputs using SVIM-asm. Variants are categorized into five types: DEL, deletion; INS, insertion; INV, inversion; DUP_INT, interspersed duplication; and DUP_TAN, tandem duplication. Each graph on the left shows variants &lt;2 kb. Graphs on the right show all variants. Each table in the right graph presents the number of variants in the corresponding call set. Fig. S3. Size distribution of common founder variants shared between ALT1 and ALT2 detected using the CLR or HiFi assemblies. The numbers of variants ranging in size from 5 bp to ≤10 kb and &gt;10 kb. The numbers of &gt;10-kb variants were too small and are therefore represented as absolute numbers (0 or 1). Fig. S4. Overlap ratio between all acquired variants detected using CLR and HiFi for each strain. A, Proportion of CLR variants covered by HiFi. B, Proportion of HiFi variants covered by CLR. Color represents partial- or full-matched variant sets. Shapes (circle or triangle) represent each strain. Fig. S5. Performance of four read-based SV callers using HiFi reads in various sequencing depths. Violin plots represent the number of total detected variants. Violin plots represent the detection rate of true-positive variants from the four SV callers, SVIM, Sniffles, PBSV, and MAMnet. We used the full-matched common founder variants in ALT1 and ALT2 described above (total 1804) as the true-positive variant set to estimate the true-positive variant detection rate. Red represents the detection rate based on at least partial match to the true-positive variant set, and blue does only for perfect match. Subsampling was repeated 5 times for each sequencing depth. Each horizontal solid line of the violin plot means a median of each 5 repeats. Fig. S6. Performance of HiFi read-based reliable variant calling at varying sequencing depths. Violin plots representing the detection rate of true-positive variants. The full-matched common founder variants in ALT1 and ALT2 described above (1804 in total) were used as the true-positive variant set. Red represents the detection rate based on at least partial matching to the true-positive variant set and blue represents the detection rate based on perfect matching. Subsampling was repeated five times for each sequencing depth. Reliable variants were filtered from those obtained using read-based variant calling according to their quality metric (Q ≥ 5; Q5). Fig. S7. Performance of the three SV callers, Sniffles, PBSV, and MAMnet, in detecting reliable true-positive variants in various sequencing depths. Violin plots represent the number of total detected variants. Red represents the number of variants at least partially matched to the common founder variants (total 1804), and blue does only for perfect match. Gray represents the remaining variants out of the total. Reliable variants were filtered by their quality metric (Q ≥ 5; Q5) from read-based variant calling, except Sniffles and PBSV, in which the quality metric is over 25 or missing, respectively. Subsampling was repeated 5 times for each sequencing depth. Table S1. Statistics for polymerase reads and subreads. Table S2. Statistics for HiFi reads. Table S3. Statistics for assembled contigs built on CLR or depth-matched HiFi reads. Table S4. Number of founder and acquired variants detected in each assembly for each strain.