DeepM6ASeq: prediction and characterization of m6A-containing sequences using deep learning

METTL14 Inhibits Hematopoietic Stem/Progenitor Differentiation and Promotes Leukemogenesis via mRNA m 6 A Modification

(2018) Hengyou Weng et al.   Cell Stem Cell

m6A modulates haematopoietic stem and progenitor cell specification

(2017) Chunxia Zhang et al.   NATURE

N6-methyladenosine (m6A) recruits and repels proteins to regulate mRNA homeostasis

(2017) Raghu R Edupuganti et al.   NATURE STRUCTURAL & MOLECULAR BIOLOGY

MeT-DB V2.0: elucidating context-specific functions of N6-methyl-adenosine methyltranscriptome

(2017) Hui Liu et al.   NUCLEIC ACIDS RESEARCH

N 6-methyladenosine alters RNA structure to regulate binding of a low-complexity protein

(2017) Nian Liu et al.   NUCLEIC ACIDS RESEARCH

RSAT matrix-clustering: dynamic exploration and redundancy reduction of transcription factor binding motif collections

(2017) Jaime Abraham Castro-Mondragon et al.   NUCLEIC ACIDS RESEARCH

N6-methyladenosine (m6A) recruits and repels proteins to regulate mRNA homeostasis

(2017) Raghu R Edupuganti et al.   NATURE STRUCTURAL & MOLECULAR BIOLOGY

DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences

(2016) Daniel Quang et al.   NUCLEIC ACIDS RESEARCH

SRAMP: prediction of mammalian N6-methyladenosine (m6A) sites based on sequence-derived features

(2016) Yuan Zhou et al.   NUCLEIC ACIDS RESEARCH

Nuclear m 6 A Reader YTHDC1 Regulates mRNA Splicing

(2016) Ian A. Roundtree et al.   TRENDS IN GENETICS

iRNA-Methyl: Identifying N6-methyladenosine sites using pseudo nucleotide composition

(2015) Wei Chen et al.   ANALYTICAL BIOCHEMISTRY

N6-methyladenosine Modulates Messenger RNA Translation Efficiency

(2015) Xiao Wang et al.   CELL

m6A RNA Methylation Is Regulated by MicroRNAs and Promotes Reprogramming to Pluripotency

(2015) Tong Chen et al.   Cell Stem Cell

Deep learning

(2015) Yann LeCun et al.   NATURE

Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning

(2015) Babak Alipanahi et al.   NATURE BIOTECHNOLOGY

Predicting effects of noncoding variants with deep learning–based sequence model

(2015) Jian Zhou et al.   NATURE METHODS

Single-nucleotide-resolution mapping of m6A and m6Am throughout the transcriptome

(2015) Bastian Linder et al.   NATURE METHODS

Identification and analysis of the N6-methyladenosine in the Saccharomyces cerevisiae transcriptome

(2015) Wei Chen et al.   Scientific Reports

m6A RNA Modification Controls Cell Fate Transition in Mammalian Embryonic Stem Cells

(2014) Pedro J. Batista et al.   Cell Stem Cell

High-Resolution Mapping Reveals a Conserved, Widespread, Dynamic mRNA Methylation Program in Yeast Meiosis

(2013) Schraga Schwartz et al.   CELL

Transcriptome-wide mapping of N6-methyladenosine by m6A-seq based on immunocapturing and massively parallel sequencing

(2013) Dan Dominissini et al.   Nature Protocols

CD-HIT: accelerated for clustering the next-generation sequencing data

(2012) Limin Fu et al.   BIOINFORMATICS

Comprehensive Analysis of mRNA Methylation Reveals Enrichment in 3′ UTRs and near Stop Codons

(2012) Kate D. Meyer et al.   CELL

Topology of the human and mouse m6A RNA methylomes revealed by m6A-seq

(2012) Dan Dominissini et al.   NATURE

Model-based Analysis of ChIP-Seq (MACS)

(2008) Yong Zhang et al.   GENOME BIOLOGY