Ontology highlight
ABSTRACT:
PROVIDER: PRJEB32782 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| Other | ||||
| ERR3486100_1.fastq.gz | Fastqsanger.gz | |||
| ERR3486100_2.fastq.gz | Fastqsanger.gz | |||
| ERR3486110_1.fastq.gz | Fastqsanger.gz | |||
| ERR3486110_2.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 74 |
Similar Datasets
Project description:<p>Despite the nuclear localization of the m6A machinery, the genomes of multiple exclusively-cytoplasmic RNA viruses, such as chikungunya (CHIKV) and dengue (DENV), are reported to be extensively m6A-modified. However, these findings are mostly based on m6A-seq, an antibody-dependent technique with a high rate of false positives. Here, we addressed the presence of m6A in CHIKV and DENV RNAs. For this, we combined m6A-seq and the antibody-independent SELECT and nanopore direct RNA sequencing techniques with functional, molecular, and mutagenesis studies. Following this comprehensive analysis, we found no evidence of m6A modification in CHIKV or DENV transcripts. Furthermore, depletion of key components of the host m6A machinery did not affect CHIKV or DENV infection. Moreover, CHIKV or DENV infection had no effect on the m6A machinery’s localization. Our results challenge the prevailing notion that m6A modification is a general feature of cytoplasmic RNA viruses and underscore the importance of validating RNA modifications with orthogonal approaches.</p>
2024-01-25 | MTBLS6978 | MetaboLights
Project description:RNA internal modifications play critical role in development of multicellular organisms and their response to environmental cues. Using nanopore direct RNA sequencing (DRS), we constructed a large in vitro epitranscriptome (IVET) resource from plant cDNA library labeled with m6A, m1A and m5C respectively. Furthermore, after transfer learning, the pre-trained model was used to detect additional RNA internal modification such as m1A, hm5C, m7G and Ψ modification. Finally, we illustrated a global view of epitranscriptome with m6A, m1A, m5C, m7G and Ψ modification in rice seedlings under normal and high salinity environment. In summary, we provided a strategy for creating IVET resource from cDNA library and developed a computational method that use IVET-based transfer learning termed TandemMod for profiling epitranscriptome landscape with co-occupancy of multiple types of RNA modification in plants responsive to environmental signal.
2024-03-28 | GSE227087 | 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:We report the direct RNA sequencing of HEK293 and a primary human mammary epithelial cell (HMEC) line using Oxford Nanopore based sequencing. Using this data, we built an algorithm to detect m6A modifications within the DRACH motif context. Evaluation of m6A sites was carried out with HEK METTL3 knockdown and HMEC ALKBH5 over expression cell lines.
2020-01-14 | GSE132971 | GEO
Project description:Applying a refined m6A RNA immunoprecipitation method, we profiled the m6A epitranscriptome on 10 non-neoplastic lung (NL) tissues and 53 lung adenocarcinoma tumors.
| EGAD00001008026 | EGA
Project description:We performed m6A-RIPs in Ascl1-induced neurons (iNeurons) to investigate the neuronal m6A epitranscriptome. Immunoprecipitation was done twice using two different antibodies, acquired from Abcam and Synaptic Systems (SySy), allowing for a more robust detection of m6A modification marks. Additionally, RIP-seq was performed separately with intact and fragmented RNA. The former approach allowed to identify proportions of m6A-modified transcripts among the total number, while the latter approach provided the information to identify genomic coordinates of m6A peaks.
2023-07-12 | E-MTAB-9542 | biostudies-arrayexpress
Project description:N6-methyladenosine (m6A) is a widespread reversible chemical modification of RNAs, implicated in many aspects of RNA metabolism. Little quantitative information exists as to either how many transcript copies of particular genes are m6A modified (âm6A levelsâ), or the relationship of m6A modification(s) to alternative RNA isoforms. To deconvolute the m6A epitranscriptome, we developed m6A level and isoform-characterization sequencing (m6A-LAIC-seq). We found that cells exhibit a broad range of non-stoichiometric m6A levels with cell type specificity. At the level of isoform characterization, we discovered widespread differences in use of tandem alternative polyadenylation (APA) sites by methylated and nonmethylated transcript isoforms of individual genes. Strikingly, there is a strong bias for methylated transcripts to be coupled with proximal APA sites, resulting in shortened 3â untranslated regions (3â-UTRs), while nonmethylated transcript isoforms tend to use distal APA sites. m6A-LAIC-seq yields a new perspective on transcriptome complexity and links APA usage to m6A modifications. m6A-LAIC-seq of H1-ESC and GM12878 cell lines, each cell line has two replicates
2016-06-24 | E-GEOD-66086 | biostudies-arrayexpress
Project description:N6-methyladenosine (m6A) and pseudouridine (Ψ) are the two most abundant modifications in mammalian mRNA, but the coordination of their biological functions remains poorly understood. We develop a machine learning-based nanopore direct RNA sequencing method (NanoSPA) that simultaneously analyzes m6A and Ψ in the human transcriptome. Applying NanoSPA to polysome profiling, we reveal opposing transcriptomic co-occurrence of m6A and Ψ and synergistic, hierarchical effects of m6A and Ψ on the polysome.
2024-01-06 | GSE230936 | GEO
Project description:N6-methyladenosine (m6A) has been one of the most abundant and well-known modifications in mRNA since its discovery in 1970s. Recent studies have demonstrated that m6A gets involved in various biological processes such as alternative splicing and RNA degradation, playing an important role in all kinds of diseases. To better understand the role of m6A, transcriptome-wide m6A profiling data is indispensable. In these years, the Oxford Nanopore Technology Direct RNA Sequencing (DRS) platform has shown promise in RNA modification detection based on current disruptions measured in transcripts. However, decoding current intensity data into modification profiles remains a challenging task. Here, we introduce m6A Transcriptome-wide Mapper (m6ATM), a novel Python-based computational pipeline that applies deep neural networks to predict m6A sites at single-base resolution using DRS data. The m6ATM model architecture incorporates a WaveNet encoder and a dual-stream multiple instance learning model to extract features from specific target sites and characterize the m6A epitranscriptome. For validation, m6ATM achieved an accuracy of 80 to 98% across in-vitro transcription datasets containing varying m6A modification ratios and outperformed other tools in benchmarking with human cell-line data. Moreover, we demonstrated the versatility of m6ATM in providing reliable stoichiometric information and used it to pinpoint PEG10 as a potential m6A target transcript in liver cancer cells. In conclusion, we showed that m6ATM is a high-performance m6A detection tool and our results paved the way for epitranscriptomic precision medicine.
2024-10-28 | GSE265867 | GEO
Project description:N6-methyladenosine (m6A) has been one of the most abundant and well-known modifications in mRNA since its discovery in 1970s. Recent studies have demonstrated that m6A gets involved in various biological processes such as alternative splicing and RNA degradation, playing an important role in all kinds of diseases. To better understand the role of m6A, transcriptome-wide m6A profiling data is indispensable. In these years, the Oxford Nanopore Technology Direct RNA Sequencing (DRS) platform has shown promise in RNA modification detection based on current disruptions measured in transcripts. However, decoding current intensity data into modification profiles remains a challenging task. Here, we introduce m6A Transcriptome-wide Mapper (m6ATM), a novel Python-based computational pipeline that applies deep neural networks to predict m6A sites at single-base resolution using DRS data. The m6ATM model architecture incorporates a WaveNet encoder and a dual-stream multiple instance learning model to extract features from specific target sites and characterize the m6A epitranscriptome. For validation, m6ATM achieved an accuracy of 80 to 98% across in-vitro transcription datasets containing varying m6A modification ratios and outperformed other tools in benchmarking with human cell-line data. Moreover, we demonstrated the versatility of m6ATM in providing reliable stoichiometric information and used it to pinpoint PEG10 as a potential m6A target transcript in liver cancer cells. In conclusion, we showed that m6ATM is a high-performance m6A detection tool and our results paved the way for epitranscriptomic precision medicine.
2024-10-28 | GSE265754 | GEO