Targeted nanopore sequencing enables complete characterisation of structural deletions initially identified using exon-based short-read sequencing strategies

Cas9-targeted-based long-read sequencing for genetic screening of RPE65 locus

Introduction

Long-read sequencing (LRS) enables accurate structural variant detection and variant phasing. When a molecular diagnosis is suspected, target enrichment can reduce the cost and duration of sequencing.

Methods

LRS was conducted in five inherited retinal dystrophy (IRD) patients harboring a monoallelic variant in RPE65 that remained uncharacterized after clinical exome sequencing (CES). CRISPR-Cas9 guide RNA probes were designed to target a 31 kb region, including the entire RPE65 locus. The DNA was sequenced on a MinION platform. Short-read ×30 whole-genome sequencing (WGS) was performed for five patients to validate nanopore results.

Results

The nanopore sequencing process yielded a median of 271 reads within the targeted region, with a mean depth of 109 and a median read size of 8 kb. All variants identified by CES have been detected using this approach, and no additional RPE65 gene causative variants were found. Nanopore variant detection demonstrated performance akin to short-read WGS at similar coverage levels, although exhibiting increased false positive calls at lower coverage.

Discussion

In this study, we explore the advantages of using a targeted approach together with long-read sequencing to identify variants associated with IRD. The results underscore the utility of targeted long reads for characterizing patients affected by rare diseases when first-tier diagnostic tests are non-conclusive.

Advances in long-read single-cell transcriptomics

Long-read single-cell transcriptomics (scRNA-Seq) is revolutionizing the way we profile heterogeneity in disease. Traditional short-read scRNA-Seq methods are limited in their ability to provide complete transcript coverage, resolve isoforms, and identify novel transcripts. The scRNA-Seq protocols developed for long-read sequencing platforms overcome these limitations by enabling the characterization of full-length transcripts. Long-read scRNA-Seq techniques initially suffered from comparatively poor accuracy compared to short read scRNA-Seq. However, with improvements in accuracy, accessibility, and cost efficiency, long-reads are gaining popularity in the field of scRNA-Seq. This review details the advances in long-read scRNA-Seq, with an emphasis on library preparation protocols and downstream bioinformatics analysis tools.

Long-read sequencing for brain tumors

Brain tumors and genomics have a long-standing history given that glioblastoma was the first cancer studied by the cancer genome atlas. The numerous and continuous advances through the decades in sequencing technologies have aided in the advanced molecular characterization of brain tumors for diagnosis, prognosis, and treatment. Since the implementation of molecular biomarkers by the WHO CNS in 2016, the genomics of brain tumors has been integrated into diagnostic criteria. Long-read sequencing, also known as third generation sequencing, is an emerging technique that allows for the sequencing of longer DNA segments leading to improved detection of structural variants and epigenetics. These capabilities are opening a way for better characterization of brain tumors. Here, we present a comprehensive summary of the state of the art of third-generation sequencing in the application for brain tumor diagnosis, prognosis, and treatment. We discuss the advantages and potential new implementations of long-read sequencing into clinical paradigms for neuro-oncology patients.

O’Brien T, Perucca P, Ge Z, Anderson A. Applications for Deep Learning in Epilepsy Genetic Research

Epilepsy is a group of brain disorders characterised by an enduring predisposition to generate unprovoked seizures. Fuelled by advances in sequencing technologies and computational approaches, more than 900 genes have now been implicated in epilepsy. The development and optimisation of tools and methods for analysing the vast quantity of genomic data is a rapidly evolving area of research. Deep learning (DL) is a subset of machine learning (ML) that brings opportunity for novel investigative strategies that can be harnessed to gain new insights into the genomic risk of people with epilepsy. DL is being harnessed to address limitations in accuracy of long-read sequencing technologies, which improve on short-read methods. Tools that predict the functional consequence of genetic variation can represent breaking ground in addressing critical knowledge gaps, while methods that integrate independent but complimentary data enhance the predictive power of genetic data. We provide an overview of these DL tools and discuss how they may be applied to the analysis of genetic data for epilepsy research.