Comprehensive de novo mutation discovery with HiFi long-read sequencing

Abstract

Background Long-read sequencing (LRS) techniques have been very successful in identifying structural variants (SVs). However, the high error rate of LRS made the detection of small variants (substitutions and short indels < 20 bp) more challenging. The introduction of PacBio HiFi sequencing makes LRS also suited for detecting small variation. Here we evaluate the ability of HiFi reads to detect de novo mutations (DNMs) of all types, which are technically challenging variant types and a major cause of sporadic, severe, early-onset disease. Methods We sequenced the genomes of eight parent–child trios using high coverage PacBio HiFi LRS (~ 30-fold coverage) and Illumina short-read sequencing (SRS) (~ 50-fold coverage). De novo substitutions, small indels, short tandem repeats (STRs) and SVs were called in both datasets and compared to each other to assess the accuracy of HiFi LRS. In addition, we determined the parent-of-origin of the small DNMs using phasing. Results We identified a total of 672 and 859 de novo substitutions/indels, 28 and 126 de novo STRs, and 24 and 1 de novo SVs in LRS and SRS respectively. For the small variants, there was a 92 and 85% concordance between the platforms. For the STRs and SVs, the concordance was 3.6 and 0.8%, and 4 and 100% respectively. We successfully validated 27/54 LRS-unique small variants, of which 11 (41%) were confirmed as true de novo events. For the SRS-unique small variants, we validated 42/133 DNMs and 8 (19%) were confirmed as true de novo event. Validation of 18 LRS-unique de novo STR calls confirmed none of the repeat expansions as true DNM. Confirmation of the 23 LRS-unique SVs was possible for 19 candidate SVs of which 10 (52.6%) were true de novo events. Furthermore, we were able to assign 96% of DNMs to their parental allele with LRS data, as opposed to just 20% with SRS data. Conclusions HiFi LRS can now produce the most comprehensive variant dataset obtainable by a single technology in a single laboratory, allowing accurate calling of substitutions, indels, STRs and SVs. The accuracy even allows sensitive calling of DNMs on all variant levels, and also allows for phasing, which helps to distinguish true positive from false positive DNMs.

Towards routine long-read sequencing for rare disease: a national pilot study on chromosomal rearrangements

“…Starting with DNA extraction, our results show that lrGS with PacBio Revio performs well with blood samples extracted in routine diagnostics. We obtained >20x coverage for all the samples, which is likely enough for calling of both small and large variants (27, 28). The two samples where HMW-DNA was extracted from fresh blood (P8.1, P8.2) gave slightly more data on average, but we also obtained high yields from the three routine DNA samples (P11, P12, P13) where we had enough DNA to perform gel-based size selection (Additional file 2: Document S2).…”

Comparative evaluation of SNVs, indels, and structural variations detected with short- and long-read sequencing data

“…Fully phased genome assemblies using long-read WGS of 35 individuals identified a significant number of variants that were not observed in short-read WGS data 13 . PacBio HiFi long reads identified more de novo indels and SVs with greater accuracy than short reads 14 . However, short-read data have been routinely used for variant detection, especially in multiple samples, due to the high cost of long-read sequencing and the high demands on the quality and quantity of input DNA.…”

Steps to Improve Precision Medicine in Epilepsy

“…On the other hand, long-read sequencing technologies provide lower per read accuracy than short-read sequencing, and error correction remains an important step in long-read analysis pipelines [ 71 ]. This limitation could be overcome with the spreading of the HiFi long-read sequencing that provides exceptional read lengths without compromising accuracy [ 72 ]. Data from multi-modal experiments including genomics, proteomics, transcriptomics, metabolomics and imaging techniques can be analysed through high-dimensional multivariate statistics.…”