XNA Basecaller
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB82716 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Cellular identity in complex multicellular organisms is controlled in part by the physical organization of cells. However, large-scale investigation of the cellular interactome has been technically challenging. Here we develop Cell Interaction by Multiplet sequencing (CIM-seq), an unsupervised and high-throughput method to analyze direct physical cell-cell interactions between every cell types presented in a tissue. CIM-seq is based on RNA sequencing of incompletely dissociated cells, followed by computational deconvolution into constituent cell types using machine learning. Contrary to previous deconvolution-based methods, CIM-seq estimates parameters such as number of cells and cell types in each multiplet directly from sequencing the data, making it compatible with high-throughput droplet-based methods. When applied to gut epithelium, or whole dissociated lung and spleen, CIM-seq correctly identifies known interactions, including those between different cell lineages and immune cells. In the colon, CIM-seq identifies a previously unrecognized goblet cell subtype expressing the wound-healing marker Plet1, which is directly adjacent to colonic stem cells. Our results from different tissue types demonstrate that CIM-seq is broadly applicable to profile cell type interactions in different tissue types using in an unsupervised manner.
2021-05-28 | GSE175664 | GEO
Project description:Long-read nanopore sequencing has emerged as a potent tool for studying RNA modifications. However, the detection of N4-acetylcytidine (ac4C) based on nanopore sequencing remains largely unexplored. Here, we introduce ac4Cnet, a deep learning frame utilizing Oxford Nanopore direct RNA sequencing to accurately identify ac4C sites. Our methodology involves training ac4Cnet capable of distinguishing ac4C from unmodified cytidine and 5-methylcytosine (m5C), as well as estimating the modification rate at each ac4C site. We demonstrate the robustness of our approach through validations on independent in vitro datasets and a human cell line, highlighting its versatility and potential for advancing the study of ac4C modifications.
2025-02-08 | GSE267558 | GEO
Project description:State-of-the-art algorithms for m6A detection and quantification via nanopore direct RNA sequencing have been continuously developed, little is known about their capacities and limitations, which makes a comprehensive assessment in urgent need. Therefore, we performed comprehensive benchmarking of 10 computational tools relying on current-based and base-calling “errors” strategies for m6A detection by nanopore sequencing.
2023-03-01 | GSE195618 | GEO
Project description:To detect the modifed bases in SINEUP RNA, we compared chemically modified in vitro transcribed (IVT) SINEUP-GFP RNA and in-cell transcribed (ICT) SINEUP RNA from SINEUP-GFP and sense EGFP co-transfected HEK293T/17 cells. Comparative study of Nanopore direct RNA sequencing data from non-modified and modified IVT samples against the data from ICT SINEUP RNA sample revealed modified k-mers positions in SINEUP RNA in the cell.
2023-02-04 | GSE224018 | GEO
Project description:Interventions: Case vs control:No
Primary outcome(s): High-throughput sequencing
Study Design: Case-Control study
| 2676067 | ecrin-mdr-crc
Project description:Deconvolution models are a powerful tool for extracting cell type-specific information from bulk gene expression profiles. Current methods leverage advanced machine learning models and high-resolution sequencing, like single-cell RNA-sequencing (scRNA-seq), showing promising results across diverese tissues and conditions. However, they still present important limitations: Many depend on selecting a robust reference, which can strongly affect the deconvolution. Secondly, pseudobulk data used for training and real bulk RNA-seq samples often exhibit strong distribution shifts, which are currently unaccounted for. Finally, most deconvolution approaches behave as black boxes, which can compromise the reliability of the results. Here, we present Sweetwater, an adaptive and interpretable autoencoder that efficiently deconvolves bulk samples leveraging multiple classes of reference data. Moreover, we propose an improved way of generating training data from a mixture of FACS-sorted FASTQ files, reducing platform-specific biases and outperforming current single-cell-based references. Furthermore, we introduce a gold standard dataset to facilitate fair and accurate evaluation of deconvolution approaches. Finally, we demonstrate that Sweetwater adapts effectively to deconvolved samples during training, uncovering biologically meaningful patterns and enhancing result's reliability. Sweetwater is available at https://github.com/ML4BM-Lab/Sweetwater, and we anticipate it will expedite the accurate examination of high-throughput clinical data across diverse applications.
2025-09-10 | GSE297720 | GEO
Project description:5-methylcytosine (5mC) is the most important DNA modification in mammalian genomes as a lineage-defining mark dynamically altered in development and disease. The ideal method for 5mC localization would be both non-destructive of DNA and direct, without requiring inference based on detection of unmodified cytosines. Here, we present Direct Methylation Sequencing (DM-Seq), a bisulfite-free method for profiling 5mC at single-base resolution, using nanogram quantities of input DNA. DM-Seq employs two key DNA modifying enzymes: a neomorphic DNA methyltransferase engineered to generate the unnatural base 5-carboxymethylcytosine, and a DNA deaminase capable of precise discrimination between cytosine modification states. Coupling these activities requires a novel adapter strategy employing 5-propynylcytosine, ultimately resulting in the accurate and direct detection of only 5mC via a C-to-T transition in sequencing. In performing comparisons to DM-Seq, we uncover a systematic bias in 5mC detection seen with the hybrid enzymatic-chemical TAPS sequencing approach. Furthermore, by applying DM-Seq to a human glioblastoma tumor, we demonstrate that DM-Seq, unlike bisulfite-sequencing, detects 5mC at prognostically-important CpGs, without confounding by 5-hydroxymethylcytosine. DM-Seq thus leverages unnatural DNA modifications to create the first method for direct 5mC profiling entirely using enzymes rather than chemical reagents.
2023-04-24 | GSE225971 | GEO
Project description:5-methylcytosine (5mC) is the most important DNA modification in mammalian genomes as a lineage-defining mark dynamically altered in development and disease. The ideal method for 5mC localization would be both non-destructive of DNA and direct, without requiring inference based on detection of unmodified cytosines. Here, we present Direct Methylation Sequencing (DM-Seq), a bisulfite-free method for profiling 5mC at single-base resolution, using nanogram quantities of input DNA. DM-Seq employs two key DNA modifying enzymes: a neomorphic DNA methyltransferase engineered to generate the unnatural base 5-carboxymethylcytosine, and a DNA deaminase capable of precise discrimination between cytosine modification states. Coupling these activities requires a novel adapter strategy employing 5-propynylcytosine, ultimately resulting in the accurate and direct detection of only 5mC via a C-to-T transition in sequencing. In performing comparisons to DM-Seq, we uncover a systematic bias in 5mC detection seen with the hybrid enzymatic-chemical TAPS sequencing approach. Furthermore, by applying DM-Seq to a human glioblastoma tumor, we demonstrate that DM-Seq, unlike bisulfite-sequencing, detects 5mC at prognostically-important CpGs, without confounding by 5-hydroxymethylcytosine. DM-Seq thus leverages unnatural DNA modifications to create the first method for direct 5mC profiling entirely using enzymes rather than chemical reagents.
2023-04-24 | GSE225967 | GEO
Project description:5-methylcytosine (5mC) is the most important DNA modification in mammalian genomes as a lineage-defining mark dynamically altered in development and disease. The ideal method for 5mC localization would be both non-destructive of DNA and direct, without requiring inference based on detection of unmodified cytosines. Here, we present Direct Methylation Sequencing (DM-Seq), a bisulfite-free method for profiling 5mC at single-base resolution, using nanogram quantities of input DNA. DM-Seq employs two key DNA modifying enzymes: a neomorphic DNA methyltransferase engineered to generate the unnatural base 5-carboxymethylcytosine, and a DNA deaminase capable of precise discrimination between cytosine modification states. Coupling these activities requires a novel adapter strategy employing 5-propynylcytosine, ultimately resulting in the accurate and direct detection of only 5mC via a C-to-T transition in sequencing. In performing comparisons to DM-Seq, we uncover a systematic bias in 5mC detection seen with the hybrid enzymatic-chemical TAPS sequencing approach. Furthermore, by applying DM-Seq to a human glioblastoma tumor, we demonstrate that DM-Seq, unlike bisulfite-sequencing, detects 5mC at prognostically-important CpGs, without confounding by 5-hydroxymethylcytosine. DM-Seq thus leverages unnatural DNA modifications to create the first method for direct 5mC profiling entirely using enzymes rather than chemical reagents.
2023-04-24 | GSE225964 | GEO
Project description:5-methylcytosine (5mC) is the most important DNA modification in mammalian genomes as a lineage-defining mark dynamically altered in development and disease. The ideal method for 5mC localization would be both non-destructive of DNA and direct, without requiring inference based on detection of unmodified cytosines. Here, we present Direct Methylation Sequencing (DM-Seq), a bisulfite-free method for profiling 5mC at single-base resolution, using nanogram quantities of input DNA. DM-Seq employs two key DNA modifying enzymes: a neomorphic DNA methyltransferase engineered to generate the unnatural base 5-carboxymethylcytosine, and a DNA deaminase capable of precise discrimination between cytosine modification states. Coupling these activities requires a novel adapter strategy employing 5-propynylcytosine, ultimately resulting in the accurate and direct detection of only 5mC via a C-to-T transition in sequencing. In performing comparisons to DM-Seq, we uncover a systematic bias in 5mC detection seen with the hybrid enzymatic-chemical TAPS sequencing approach. Furthermore, by applying DM-Seq to a human glioblastoma tumor, we demonstrate that DM-Seq, unlike bisulfite-sequencing, detects 5mC at prognostically-important CpGs, without confounding by 5-hydroxymethylcytosine. DM-Seq thus leverages unnatural DNA modifications to create the first method for direct 5mC profiling entirely using enzymes rather than chemical reagents.
2023-04-24 | GSE225973 | GEO