Biology subjects

Magner, R.

Publications and source records attributed to Magner, R..

3 recordsLinked to original sources

bioRxiv2024

Blended Length Genome Sequencing (blend-seq): Combining Short Reads with Low-Coverage Long Reads to Maximize Variant Discovery

We introduce blend-seq, a workflow for combining data from traditional short-read sequencing pipelines with low-coverage long reads, to improve variant discovery for single samples without the full cost of high-coverage long reads. We demonstrate that with only 4x long-read coverage augmenting 30x short reads, we can improve SNP discovery across the genome, exceeding performance beyond even high-coverage short reads (60x). For genotype-agnostic discovery of structural variants, we see a threefold improvement in recall while maintaining precision by using the low-coverage long reads on their own, and show how we can improve genotyping accuracy by adding in the short-read data. In addition, we demonstrate how the long reads can better phase these variants, incorporating long-context information in the genome to substantially outperform phasing with short reads alone. Our experiments highlight the complementary nature of short- and long-read technologies: the former contributing higher depth for genotyping and the latter better resolution of larger events or those in difficult regions.

genomics↗

bioRxiv2024

Secure Discovery of Genetic Relatives across Large-Scale and Distributed Genomic Datasets

Finding relatives within a study cohort is a necessary step in many genomic studies. However, when the cohort is distributed across multiple entities subject to data-sharing restrictions, performing this step often becomes infeasible. Developing a privacy-preserving solution for this task is challenging due to the significant burden of estimating kinship between all pairs of individuals across datasets. We introduce SF-Relate, a practical and secure federated algorithm for identifying genetic relatives across data silos. SF-Relate vastly reduces the number of individual pairs to compare while maintaining accurate detection through a novel locality-sensitive hashing approach. We assign individuals who are likely to be related together into buckets and then test relationships only between individuals in matching buckets across parties. To this end, we construct an effective hash function that captures identity-by-descent (IBD) segments in genetic sequences, which, along with a new bucketing strategy, enable accurate and practical private relative detection. To guarantee privacy, we introduce an efficient algorithm based on multiparty homomorphic encryption (MHE) to allow data holders to cooperatively compute the relatedness coefficients between individuals, and to further classify their degrees of relatedness, all without sharing any private data. We demonstrate the accuracy and practical runtimes of SF-Relate on the UK Biobank and All of Us datasets. On a dataset of 200K individuals split between two parties, SF-Relate detects 94.9% of third-degree relatives, and 99.9% of second-degree or closer relatives, within 15 hours of runtime. Our work enables secure identification of relatives across large-scale genomic datasets.

bioinformatics↗

bioRxiv2022

Cost-efficient whole genome-sequencing using novel mostly natural sequencing-by-synthesis chemistry and open fluidics platform

We introduce a massively parallel novel sequencing platform that combines an open flow cell design on a circular wafer with a large surface area and mostly natural nucleotides that allow optical end-point detection without reversible terminators. This platform enables sequencing billions of reads with longer read length ([~]300bp) and fast runs times (<20hrs) with high base accuracy (Q30 > 85%), at a low cost of $1/Gb. We establish system performance by whole-genome sequencing of the Genome-In-A-Bottle reference samples HG001-7, demonstrating high accuracy for SNPs (99.6%) and Indels in homopolymers up to length 10 (96.4%) across the vast majority (>98%) of the defined high-confidence regions of these samples. We demonstrate scalability of the whole-genome sequencing workflow by sequencing an additional 224 selected samples from the 1000 Genomes project achieving high concordance with reference data.

genomics↗