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References from Bioinformatics approaches for deciphering the epitranscriptome: Recent progress and emerging topics. Local targets link to admitted publications; unresolved targets remain external evidence.
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Drosophila YBX1 homolog YPS promotes ovarian germ line stem cell development by preferentially recognizing 5-methylcytosine RNAs
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2020 · External reference
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10.1186/s12859-017-1808-4 · 2017 · External reference
Perturbation of m6A writers reveals two distinct classes of mRNA methylation at internal and 5’ Sites
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trumpet: transcriptome-guided quality assessment of m(6)A-seq data
10.1186/s12859-018-2266-3 · 2018 · External reference
The reverse transcription signature of N-1-methyladenosine in RNA-Seq is sequence dependent
2015 · External reference
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Graphical workflow system for modification calling by machine learning of reverse transcription signatures
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meRanTK: methylated RNA analysis ToolKit
10.1093/bioinformatics/btv647 · 2016 · External reference
5-methylcytosine promotes mRNA export - NSUN2 as the methyltransferase and ALYREF as an m(5)C reader
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10.1109/tcbb.2016.2568178 · 2017 · External reference
RNAmod: an integrated system for the annotation of mRNA modifications
10.1093/nar/gkz479 · 2019 · External reference
ToNER: A tool for identifying nucleotide enrichment signals in feature-enriched RNA-seq data
10.1371/journal.pone.0178483 · 2017 · External reference
m6ASNP: a tool for annotating genetic variants by m6A function
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10.1093/nar/gky1120 · 2018 · External reference
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10.1093/nar/gkv1222 · 2015 · External reference
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Comprehensive review and assessment of computational methods for predicting RNA post-transcriptional modification sites from RNA sequences
2019 · External reference
WHISTLE: a high-accuracy map of the human N6-methyladenosine (m6A) epitranscriptome predicted using a machine learning approach
10.1093/nar/gkz074 · 2019 · External reference
Gene2vec: gene subsequence embedding for prediction of mammalian N6-methyladenosine sites from mRNA
10.1261/rna.069112.118 · 2018 · External reference
iN6-Methyl (5-step): Identifying RNA N6-methyladenosine sites using deep learning mode via Chou’s 5-step rules and Chou’s general PseKNC
10.1016/j.chemolab.2019.103811 · 2019 · External reference
Global analysis of N6-methyladenosine functions and its disease association using deep learning and network-based methods
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BERMP: a cross-species classifier for predicting m6A sites by integrating a deep learning algorithm and a random forest approach
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DeepMRMP: a new predictor for multiple types of RNA modification sites using deep learning
10.3934/mbe.2019310 · 2019 · External reference
DeepM6ASeq: prediction and characterization of m6A-containing sequences using deep learning
10.1186/s12859-018-2516-4 · 2018 · External reference
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Single-nucleotide resolution mapping of m6A and m6Am throughout the transcriptome
10.1038/nmeth.3453 · 2015 · External reference
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10.1093/nar/gkx1030 · 2018 · External reference
MeT-DB: a database of transcriptome methylation in mammalian cells
10.1093/nar/gku1024 · 2015 · External reference
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m6AVar: a database of functional variants involved in m(6)A modification
10.1093/nar/gkx895 · 2018 · External reference
A database for exploring N6-methyladenosine methylome
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The fat mass and obesity-associated FTO rs9939609 polymorphism is associated with elevated homocysteine levels in patients with multiple sclerosis screened for vascular risk factors
2014 · External reference
Decreased N6-methyladenosine in peripheral blood RNA from diabetic patients is associated with FTO expression rather than ALKBH5
10.1210/jc.2014-1893 · 2015 · External reference
Increased N6-methyladenosine in human sperm RNA as a risk factor for asthenozoospermia
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10.1136/jmedgenet-2015-103399 · External reference
Hypoxia induces the breast cancer stem cell phenotype by HIF-dependent and ALKBH5-mediated m6A-demethylation of NANOG mRNA
2016 · External reference
Hypoxia-inducible factors regulate pluripotency factor expression by ZNF217-and ALKBH5-mediated modulation of RNA methylation in breast cancer cells
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Genetic alterations of m 6 A regulators predict poorer survival in acute myeloid leukemia
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10.1016/j.ccell.2016.11.017 · External reference
10.1016/j.ccell.2017.02.013
10.1016/j.ccell.2017.02.013 · External reference
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10.1158/0008-5472.can-14-2800 · External reference
Expression and roles of W ilms' tumor 1-associating protein in glioblastoma
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10.1016/j.molcel.2016.03.021 · External reference
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RNAMethyPro: a biologically conserved signature of N6-methyladenosine regulators for predicting survival at pan-cancer level
2019 · External reference
Molecular characterization and clinical relevance of m6A regulators across 33 cancer types
10.1186/s12943-019-1066-3 · 2019 · External reference
10.1093/nar/gkx895
10.1093/nar/gkx895 · External reference
A Visualization and Exploration Database for m(6) As in Cell Lines
10.3390/cells8020168 · 2019 · External reference
DRUM: inference of disease-associated m6A RNA methylation sites from a multi-layer heterogeneous network
10.3389/fgene.2019.00266 · 2019 · External reference
m6Acomet: large-scale functional prediction of individual m6A RNA methylation sites from an RNA co-methylation network
10.1186/s12859-019-2840-3 · 2019 · External reference
Integrative network analysis identifies cell-specific trans regulators of m6A
2020 · External reference
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10.1038/nchembio.482 · External reference
10.1186/gb-2012-13-10-175
10.1186/gb-2012-13-10-175 · External reference
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10.1016/j.ygeno.2019.10.017 · 2019 · External reference
N6-methyladenosine alters RNA structure to regulate binding of a low-complexity protein
10.1093/nar/gkx141 · 2017 · External reference
RNA modifications in structure prediction - status quo and future challenges
2018 · External reference
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10.1016/j.gpb.2013.01.002 · 2013 · External reference
A Bayesian hierarchical model for analyzing methylated RNA immunoprecipitation sequencing data
10.1007/s40484-018-0149-2 · 2018 · External reference
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10.1261/rna.058206.116 · 2017 · External reference
10.1093/bioinformatics/btw281
10.1093/bioinformatics/btw281 · External reference
Spatially enhanced differential RNA methylation analysis from affinity-based sequencing data with hidden markov model
2015 · External reference
MeTDiff: a Novel Differential RNA Methylation Analysis for MeRIP-Seq Data
10.1109/tcbb.2015.2403355 · 2018 · External reference
DRME: count-based differential RNA methylation analysis at small sample size scenario
10.1016/j.ab.2016.01.014 · 2016 · External reference
Perturbation of m6A writers reveals two distinct classes of mRNA methylation at internal and 5' sites
10.1016/j.celrep.2014.05.048 · 2014 · External reference
Decomposition of RNA methylome reveals co-methylation patterns induced by latent enzymatic regulators of the epitranscriptome
10.1039/c4mb00604f · 2015 · External reference
Enhancing epitranscriptome module detection from m6A-seq data using threshold-based measurement weighting strategy
2018 · External reference
A hierarchical model for clustering m6A methylation peaks in MeRIP-seq data
10.1186/s12864-016-2913-x · 2016 · External reference
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10.3390/biom6040042 · External reference
10.1261/rna.036806.112
10.1261/rna.036806.112 · External reference
BS-RNA: an efficient mapping and annotation tool for RNA bisulfite sequencing data
10.1016/j.compbiolchem.2016.09.003 · 2016 · External reference
10.1101/gr.210666.116
10.1101/gr.210666.116 · External reference
Episo: quantitative estimation of RNA 5-methylcytosine at isoform level by high-throughput sequencing of RNA treated with bisulfite
10.1093/bioinformatics/btz900 · 2019 · External reference
10.1016/j.omtn.2017.03.006
10.1016/j.omtn.2017.03.006 · External reference
Imbalance learning for the prediction of N 6-Methylation sites in mRNAs
10.1186/s12864-018-4928-y · 2018 · External reference
10.1016/j.ab.2015.08.021
10.1016/j.ab.2015.08.021 · External reference
pRNAm-PC: predicting N6-methyladenosine sites in RNA sequences via physical-chemical properties
2015 · External reference
Detecting N6-methyladenosine sites from RNA transcriptomes using ensemble Support Vector Machines
10.1038/srep40242 · 2017 · External reference
Identification and analysis of the N6-methyladenosine in the Saccharomyces cerevisiae transcriptome
10.1038/srep13859 · 2015 · External reference
RNA-MethylPred: a high-accuracy predictor to identify N6-methyladenosine in RNA
10.1016/j.ab.2016.06.012 · 2016 · External reference
10.1109/tnb.2016.2599115
10.1109/tnb.2016.2599115 · External reference
iRNA(m6A)-PseDNC: identifying N6-methyladenosine sites using pseudo dinucleotide composition
10.1016/j.ab.2018.09.002 · 2018 · External reference
10.1016/j.omtn.2018.07.004
10.1016/j.omtn.2018.07.004 · External reference
Integration of deep feature representations and handcrafted features to improve the prediction of N 6 -methyladenosine sites
10.1016/j.neucom.2018.04.082 · 2018 · External reference
10.1016/j.jtbi.2018.07.018
10.1016/j.jtbi.2018.07.018 · External reference
10.1016/j.jtbi.2019.01.035
10.1016/j.jtbi.2019.01.035 · External reference
A linear regression predictor for identifying N6-methyleadenosine sites using frequent gapped K-mer pattern
10.1016/j.omtn.2019.10.001 · 2019 · External reference
10.1007/s00438-016-1243-7
10.1007/s00438-016-1243-7 · External reference
AthMethPre: a web server for the prediction and query of mRNA m6A sites in Arabidopsis thaliana
10.1039/c6mb00536e · 2016 · External reference
10.1007/s11103-018-0698-9
10.1007/s11103-018-0698-9 · External reference
Identifying RNA N6-methyladenosine sites in Escherichia coli genome
10.3389/fmicb.2018.00955 · 2018 · External reference
MethyRNA: A web-server for identification of N-methyladenosine sites
10.1080/07391102.2016.1157761 · 2016 · External reference
RNAMethPre: a web server for the prediction and query of mRNA m6A Sites
10.1371/journal.pone.0162707 · 2016 · External reference
10.1093/nar/gkw104
10.1093/nar/gkw104 · External reference
10.1016/j.omtn.2018.03.012
10.1016/j.omtn.2018.03.012 · External reference
10.1016/j.csbj.2020.04.015 · Admitted local publication
Identifying N6-methyladenosine sites using multi-interval nucleotide pair position specificity and support vector machine
10.1038/srep46757 · 2017 · External reference
Unresolved reference
External reference
M6AMRFS: robust prediction of N6-methyladenosine sites with sequence-based features in multiple species
10.3389/fgene.2018.00495 · 2018 · External reference
Identifying RNA 5-methylcytosine sites via pseudo nucleotide compositions
10.1039/c6mb00471g · 2016 · External reference
iRNAm 5C-PseDNC: identifying RNA 5-methylcytosine sites by incorporating physical-chemical properties into pseudo dinucleotide composition
10.18632/oncotarget.17104 · 2017 · External reference
Accurate RNA 5-methylcytosine site prediction based on heuristic physical-chemical properties reduction and classifier ensemble
10.1016/j.ab.2018.03.027 · 2018 · External reference
Identifying 5-methylcytosine sites in RNA sequence using composite encoding feature into Chou's PseKNC
10.1016/j.jtbi.2018.04.037 · 2018 · External reference
RNAm 5CPred: Prediction of RNA 5-methylcytosine sites based on three different kinds of nucleotide composition
10.1016/j.omtn.2019.10.008 · 2019 · External reference
iRNA-PseTNC: identification of RNA 5-methylcytosine sites using hybrid vector space of pseudo nucleotide composition
10.1007/s11704-018-8094-9 · 2019 · External reference
10.1109/access.2020.2991477
10.1109/access.2020.2991477 · External reference
Transcriptome-wide annotation of m5C RNA modifications using machine learning
2018 · External reference
RNAm 5Cfinder: a web-server for predicting RNA 5-methylcytosine (m5C) Sites based on random forest
10.1038/s41598-018-35502-4 · 2018 · External reference
PPUS: a web server to predict PUS-specific pseudouridine sites
10.1093/bioinformatics/btv366 · 2015 · External reference
PIANO: a web server for pseudouridine site (Ψ) identification and functional annotation
10.3389/fgene.2020.00088 · 2020 · External reference
iRNA-PseU: identifying RNA pseudouridine sites
10.1038/mtna.2016.37 · 2016 · External reference
PseUI: pseudouridine sites identification based on RNA sequence information
10.1186/s12859-018-2321-0 · 2018 · External reference
XG-PseU: an eXtreme Gradient Boosting based method for identifying pseudouridine sites
10.1007/s00438-019-01600-9 · 2019 · External reference
iPseU-CNN: identifying RNA pseudouridine sites using convolutional neural networks
10.1016/j.omtn.2019.03.010 · 2019 · External reference
EnsemPseU: Identifying pseudouridine sites with an ensemble approach.
10.1109/access.2020.2989469 · 2020 · External reference
iRNA-2methyl: identify RNA 2'-O-methylation sites by incorporating sequence-coupled effects into general PseKNC and ensemble classifier
2017 · External reference
10.1089/cmb.2018.0004
10.1089/cmb.2018.0004 · External reference
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