Project description:Acute leukemias (AL) are aggressive blood cancers that require precise molecular classification and urgent treatment. However, standard-of-care diagnostic tests are time and resource intensive and do not capture the full spectrum of AL heterogeneity. Here, we developed a machine learning framework to classify AL using genome-wide DNA methylation profiling. We first assembled a large reference cohort (n=2,540 samples) and defined 38 distinct methylation classes across AL lineages and age groups. A subset of methylation classes mirrored established AL categories defined by genetics, such as AML with CBFB::MYH11 and B-ALL with ETV6::RUNX1, while others revealed epigenetic heterogeneity not captured by standard-of-care testing alone, including four methylation classes associated with NPM1-mutant AML (HOXA/B-activated) and four with KMT2A-rearranged AML (HOXA-activated). Using this reference, we next developed a deep neural network model (MARLIN) for methylation-based AL classification from extremely sparse data, and applied this for rapid analysis of clinical samples profiled by nanopore sequencing. In a retrospective AL cohort, MARLIN-based classifications were concordant with standard-of-care diagnoses in 25/26 (96.2%) cases with high-confidence prediction scores, including samples of diverse lineages and molecular subtypes. In five additional patients presenting with suspected AL, we performed nanopore sequencing and MARLIN classification of clinical blood and bone marrow samples in real-time, typically achieving an accurate methylation class prediction in less than two hours from sample receipt and less than one hour of sequencing time. In summary, we present a DNA methylation-based machine learning framework for rapid AL classification in the clinic that can complement and enhance standard-of-care diagnostics.
Project description:The heterogeneous composition of cellular transcriptomes poses a major challenge for detecting weakly expressed RNA classes, as they can be obscured by abundant RNAs. Although biochemical protocols can enrich or deplete specified RNAs, they are time-consuming, expensive and can compromise RNA integrity. Here we introduce RISER, a biochemical-free technology for the real-time enrichment or depletion of RNA classes. RISER performs selective rejection of molecules during direct RNA sequencing by identifying RNA classes directly from nanopore signals with deep learning and communicating with the sequencing hardware in real time. By targeting the dominant messenger and mitochondrial RNA classes for depletion, RISER reduced their respective read counts by more than 85%, resulting in an increase in sequencing depth of up to 93% for long non-coding RNAs. We also applied RISER for the depletion of globin mRNA in whole blood, achieving a decrease in globin reads by more than 90% as well as a significant increase in non-globin reads. Furthermore, using a GPU or a CPU, RISER is faster than GPU-accelerated basecalling and mapping. RISER’s modular and retrainable software and intuitive command-line interface allow easy adaptation to other RNA classes. RISER is available at https://github.com/comprna/riser.
Project description:The heterogeneous composition of cellular transcriptomes poses a major challenge for detecting weakly expressed RNA classes, as they can be obscured by abundant RNAs. Although biochemical protocols can enrich or deplete specified RNAs, they are time-consuming, expensive and can compromise RNA integrity. Here we introduce RISER, a biochemical-free technology for the real-time enrichment or depletion of RNA classes. RISER performs selective rejection of molecules during direct RNA sequencing by identifying RNA classes directly from nanopore signals with deep learning and communicating with the sequencing hardware in real time. By targeting the dominant messenger and mitochondrial RNA classes for depletion, RISER reduced their respective read counts by more than 85%, resulting in an increase in sequencing depth of up to 93% for long non-coding RNAs. We also applied RISER for the depletion of globin mRNA in whole blood, achieving a decrease in globin reads by more than 90% as well as a significant increase in non-globin reads. Furthermore, using a GPU or a CPU, RISER is faster than GPU-accelerated basecalling and mapping. RISER’s modular and retrainable software and intuitive command-line interface allow easy adaptation to other RNA classes. RISER is available at https://github.com/comprna/riser.
Project description:Whole-genome bisulfite sequencing (WGBS) is currently the gold standard for DNA methylation (5-methylcytosine, 5mC) profiling, however the destructive nature of sodium bisulfite results in DNA fragmentation and subsequent biases in sequencing data. Such issues have led to the development of bisulfite-free methods for 5mC detection. Nanopore sequencing is a long read non-destructive approach that directly analyzes DNA and RNA fragments in real time. Recently, computational tools have been developed that enable base-resolution detection of 5mC from Oxford Nanopore sequencing data. In this chapter we provide a detailed protocol for preparation, sequencing, read assembly and analysis of genome-wide 5mC using Nanopore sequencing technologies.
2021-12-03 | GSE179673 | GEO
Project description:High-resolution and real-time wastewater viral surveillance by Nanopore sequencing
| PRJNA1097737 | ENA
Project description:multiplex RPA based Nanopore sequencing for real-time detection of virus
Project description:Affymetrix OncoScan data from primary and metastatic intracranial lesions collected for the validation of our intraoperative brain tumor classification with nanopore sequencing. All samples were processed from FFPE by our clinical lab using the standard protocol for clinical samples.
Project description:In this study, we compared the two long-read sequencing platforms, namely the single-molecule real-time sequencing by Pacific Biosciences and nanopore sequencing by Oxford Nanopore Technologies, for the analysis of cell-free DNA from plasma. Artificial mixtures of sonicated human and mouse DNA at different sizes were sequenced with the two platforms.
| EGAS00001006329 | EGA
Project description:Rapid metagenomic identification of two major swine pathogens with real-time nanopore sequencing
| PRJNA840864 | ENA
Project description:Real time, field-deployable whole genome sequencing of malaria parasites using nanopore technology