Accession PRJCA007105
Title Training an authentic neural network model using Nanopore sequencing data of Arabidopsis transcripts for detection and quantification of N6-methyladenosine on RNA
Relevance Model organism
Data types Epigenomics
Transcriptome or Gene expression
Raw sequence reads
Nanopore direct RNA sequencing
Organisms Arabidopsis thaliana
Description Nanopore direct RNA-seq for wild-type (Col-0) and two m6A-deficient A.thaliana mutants, fip37-4 SALK_018636 allele and ABI3prom:MTB complemented mtb WiscDsLox336H07 allele, using SQK-RNA001Kit and R9.4 flowcells on GridION platform
Sample scope Monoisolate
Release date 2021-12-09
Publication
PubMed ID Article title Journal name DOI Year
35039061 DENA: training an authentic neural network model using Nanopore sequencing data of Arabidopsis transcripts for detection and quantification of N6-methyladenosine on RNA Genome Biology 10.1186/s13059-021-02598-3 2022
Grants
Agency program Grant ID Grant title
National Key Research and Development Program of China 2018YFA0900700
National Key Research and Development Program of China 2019YFA0904601
Strategic Priority Research Program of Chinese Academy of Sciences XDA24010400
National Natural Science Foundation of China 31771412
National Natural Science Foundation of China 31972881
Submitter xuan    li  (lixuan@cemps.ac.cn)
Organization CAS Center for Excellence in Molecular Plant Sciences / Institute of Plant Physiology and Ecology
Submission date 2021-11-05

Project Data

Resource name Description
BioSample (9)  show -
GSA (1) -
CRA005317 DENA: training an authentic neural network model using Nanopore sequencing data of Arabidopsis transcripts for detection and quantification of N6-methyladenosine on RNA