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Lennon, N.

Publications and source records attributed to Lennon, N..

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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↗

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↗

Genetic Sex Validation for Sample Tracking in Clinical Testing

BackgroundNext generation DNA sequencing (NGS) has been rapidly adopted by clinical testing laboratories for detection of germline and somatic genetic variants. The complexity of sample processing in a clinical DNA sequencing laboratory creates multiple opportunities for sample identification errors, demanding stringent quality control procedures. MethodsWe utilized DNA genotyping via a 96-SNP PCR panel applied at sample acquisition in comparison to the final sequence, for tracking of sample identity throughout the sequencing pipeline. The 96-SNP PCR panels inclusion of sex SNPs also provides a mechanism for a genotype-based comparison to recorded sex at sample collection for identification. This approach was implemented in the clinical genomic testing pathways, in the multi-center Electronic Medical Records and Genomics (eMERGE) Phase III program ResultsWe identified 110 inconsistencies from 25,015 (0.44%) clinical samples, when comparing the 96-SNP PCR panel data to the test requisition-provided sex. The 96-SNP PCR panel genetic sex predictions were confirmed using additional SNP sites in the sequencing data or high-density hybridization-based genotyping arrays. Results identified clerical errors, samples from transgender participants and stem cell or bone marrow transplant patients and undetermined sample mix-ups. ConclusionThe 96-SNP PCR panel provides a cost-effective, robust tool for tracking samples within DNA sequencing laboratories, while the ability to predict sex from genotyping data provides an additional quality control measure for all procedures, beginning with sample collections. While not sufficient to detect all sample mix-ups, the inclusion of genetic versus reported sex matching can give estimates of the rate of errors in sample collection systems.

genomics↗

Harmonizing Clinical Sequencing And Interpretation For The eMERGE III Network

BackgroundThe eMERGE III Network was tasked with harmonizing genetic testing protocols linking multiple sites and investigators.\n\nMethodsDNA capture panels targeting 109 genes and 1551 variants were constructed by two clinical sequencing centers for analysis of 25,000 participant DNA samples collected at 11 sites where samples were linked to patients with electronic health records. Each step from sample collection, data generation, interpretation, reporting, delivery and storage, were developed and validated in CAP/CLIA settings and harmonized across sequencing centers.\n\nResultsA compliant and secure network was built and enabled ongoing review and reconciliation of clinical interpretations while maintaining communication and data sharing between investigators. Mechanisms for sustained propagation and growth of the network were established. An interim data freeze representing 15,574 sequenced subjects, informed the assay performance for a range of variant types, the rate of return of results for different phenotypes and the frequency of secondary findings. Practical obstacles for implementation and scaling of clinical and research findings were identified and addressed. The eMERGE protocols and tools established are now available for widespread dissemination.\n\nConclusionsThis study established processes for different sequencing sites to harmonize the technical and interpretive aspects of sequencing tests, a critical achievement towards global standardization of genomic testing. The network established experience in the return of results and the rate of secondary findings across diverse biobank populations. Furthermore, the eMERGE network has accomplished integration of structured genomic results into multiple electronic health record systems, setting the stage for clinical decision support to enable genomic medicine.

genomics↗