Parent-of-origin detection and chromosome-scale haplotyping using long-read DNA methylation sequencing and Strand-seq.

Hundreds of loci in human genomes have alleles that are methylated differentially according to their parent of origin. These imprinted loci generally show little variation across tissues, individuals, and populations. We show that such loci can be used to distinguish the maternal and paternal homologs for all human autosomes without the need for the parental DNA. We integrate methylation-detecting nanopore sequencing with the long-range phase information in Strand-seq data to determine the parent of origin of chromosome-length haplotypes for both DNA sequence and DNA methylation in five trios with diverse genetic backgrounds. The parent of origin was correctly inferred for all autosomes with an average mismatch error rate of 0.31% for SNVs and 1.89% for insertions or deletions (indels). Because our method can determine whether an inherited disease allele originated from the mother or the father, we predict that it will improve the diagnosis and management of many genetic diseases.

Chromosome-Length Haplotypes with StrandPhaseR and Strand-seq.

Dense local haplotypes can now readily be extracted from long-read or droplet-based sequence data. However, these methods struggle to combine subchromosomal haplotype blocks into global chromosome-length haplotypes. Strand-seq is a single cell sequencing technique that uses read orientation to capture sparse global phase information by sequencing only one of two DNA strands for each parental homolog. In combination with dense local haplotypes from other technologies, Strand-seq data can be used to obtain complete chromosome-length phase information. In this chapter, we run the R package StrandPhaseR to phase SNVs using publicly available sequence data for sample HG005 of the Genome in a Bottle project.

Benchmarking challenging small variants with linked and long reads.

Genome in a Bottle benchmarks are widely used to help validate clinical sequencing pipelines and develop variant calling and sequencing methods. Here we use accurate linked and long reads to expand benchmarks in 7 samples to include difficult-to-map regions and segmental duplications that are challenging for short reads. These benchmarks add more than 300,000 SNVs and 50,000 insertions or deletions (indels) and include 16% more exonic variants, many in challenging, clinically relevant genes not covered previously, such as PMS2. For HG002, we include 92% of the autosomal GRCh38 assembly while excluding regions problematic for benchmarking small variants, such as copy number variants, that should not have been in the previous version, which included 85% of GRCh38. It identifies eight times more false negatives in a short read variant call set relative to our previous benchmark. We demonstrate that this benchmark reliably identifies false positives and false negatives across technologies, enabling ongoing methods development.

A survey of current methods to detect and genotype inversions.

Polymorphic inversions are ubiquitous in humans and they have been linked to both adaptation and disease. Following their discovery in Drosophila more than a century ago, inversions have proved to be more elusive than other structural variants. A wide variety of methods for the detection and genotyping of inversions have recently been developed: multiple techniques based on selective amplification by PCR, short- and long-read sequencing approaches, principal component analysis of small variant haplotypes, template strand sequencing, optical mapping, and various genome assembly methods. Many methods apply complex wet lab protocols or increasingly refined bioinformatic analyses. This review is an attempt to provide a practical summary and comparison of the methods that are in current use, with a focus on metrics such as the maximum size of segmental duplications at inversion breakpoints that each method can tolerate, the size range of inversions that they recover, their throughput, and whether the locations of putative inversions must be known beforehand.

Construction of Strand-seq libraries in open nanoliter arrays.

Single-cell Strand-seq generates directional genomic information to study DNA repair, assemble genomes, and map structural variation onto chromosome-length haplotypes. We report a nanoliter-volume, one-pot (OP) Strand-seq library preparation protocol in which reagents are added cumulatively, DNA purification steps are avoided, and enzymes are inactivated with a thermolabile protease. OP-Strand-seq libraries capture 10%-25% of the genome from a single-cell with reduced costs and increased throughput.

Mapping of sister chromatid exchange events and genome alterations in single cells.

Mammalian genomes encode over a hundred different helicases, many of which are implicated in the repair of DNA lesions by acting on DNA structures arising during DNA replication, recombination or transcription. Defining the in vivo substrates of such DNA helicases is a major challenge given the large number of helicases in the genome, the breadth of potential substrates in the genome and the degree of genetic pleiotropy among DNA helicases in resolving diverse substrates. Helicases such as WRN, BLM and RECQL5 are implicated in the resolution of error-free recombination events known as sister chromatid exchange events (SCEs). Single cell Strand-seq can be used to map the genomic location of individual SCEs at a resolution that exceeds that of classical cytogenetic techniques by several orders of magnitude. By mapping the genomic locations of SCEs in the absence of different helicases, it should in principle be possible to infer the substrate specificity of specific helicases. Here we describe how the genome can be interrogated for such DNA repair events using single-cell template strand sequencing (Strand-seq) and bioinformatic tools. SCEs and copy-number alterations were mapped to genomic locations at kilobase resolution in haploid KBM7 cells. Strategies, possibilities, and limitations of Strand-seq to study helicase function are illustrated using these cells before and after CRISPR/Cas9 knock out of WRN, BLM and/or RECQL5.

InvertypeR: Bayesian inversion genotyping with Strand-seq data.

BACKGROUND: Single cell Strand-seq is a unique tool for the discovery and phasing of genomic inversions. Conventional methods to discover inversions with Strand-seq data are blind to known inversion locations, limiting their statistical power for the detection of inversions smaller than 10 Kb. Moreover, the methods rely on manual inspection to separate false and true positives. RESULTS: Here we describe "InvertypeR", a method based on a Bayesian binomial model that genotypes inversions using fixed genomic coordinates. We validated InvertypeR by re-genotyping inversions reported for three trios by the Human Genome Structural Variation Consortium. Although 6.3% of the family inversion genotypes in the original study showed Mendelian discordance, this was reduced to 0.5% using InvertypeR. By applying InvertypeR to published inversion coordinates and predicted inversion hotspots (n = 3701), as well as coordinates from conventional inversion discovery, we furthermore genotyped 66 inversions not previously reported for the three trios. CONCLUSIONS: InvertypeR discovers, genotypes, and phases inversions without relying on manual inspection. For greater accessibility, results are presented as phased chromosome ideograms with inversions linked to Strand-seq data in the genome browser. InvertypeR increases the power of Strand-seq for studies on the role of inversions in phenotypic variation, genome instability, and human disease.

Somatic mutations substantially increase the per-generation mutation rate in the conifer Picea sitchensis.

The rates and biological significance of somatic mutations have long been a subject of debate. Somatic mutations in plants are expected to accumulate with vegetative growth and time, yet rates of somatic mutations are unknown for conifers, which can reach exceptional sizes and ages. We investigated somatic mutation rates in the conifer Sitka spruce (Picea sitchensis (Bong.) Carr.) by analyzing a total of 276 Gb of nuclear DNA from the tops and bottoms of 20 old-growth trees averaging 76 m in height. We estimate a somatic base substitution rate of 2.7 × 10-8 per base pair within a generation. To date, this is one of the highest estimated per-generation rates of mutation among eukaryotes, indicating that somatic mutations contribute substantially to the total per-generation mutation rate in conifers. Nevertheless, as the sampled trees are centuries old, the per-year rate is low in comparison with nontree taxa. We argue that although somatic mutations raise genetic load in conifers, they generate important genetic variation and enable selection both among cell lineages within individual trees and among offspring.