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Nucleotide Motifs

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Identifying complex motifs in massive omics data with a variable-convolutional layer in deep neural network.

Briefings in bioinformatics
Motif identification is among the most common and essential computational tasks for bioinformatics and genomics. Here we proposed a novel convolutional layer for deep neural network, named variable convolutional (vConv) layer, for effective motif ide...

Deep and accurate detection of m6A RNA modifications using miCLIP2 and m6Aboost machine learning.

Nucleic acids research
N6-methyladenosine (m6A) is the most abundant internal RNA modification in eukaryotic mRNAs and influences many aspects of RNA processing. miCLIP (m6A individual-nucleotide resolution UV crosslinking and immunoprecipitation) is an antibody-based appr...

A sequence-based deep learning approach to predict CTCF-mediated chromatin loop.

Briefings in bioinformatics
Three-dimensional (3D) architecture of the chromosomes is of crucial importance for transcription regulation and DNA replication. Various high-throughput chromosome conformation capture-based methods have revealed that CTCF-mediated chromatin loops a...

Machine Learning Aids Classification and Discrimination of Noncanonical DNA Folding Motifs by an Arrayed Host:Guest Sensing System.

Journal of the American Chemical Society
An arrayed host:guest fluorescence sensor system can discriminate among and classify multiple different noncanonical DNA structures by exploiting selective molecular recognition. The sensor is highly selective and can discriminate between folds as si...

A self-attention model for inferring cooperativity between regulatory features.

Nucleic acids research
Deep learning has demonstrated its predictive power in modeling complex biological phenomena such as gene expression. The value of these models hinges not only on their accuracy, but also on the ability to extract biologically relevant information fr...

A survey on deep learning in DNA/RNA motif mining.

Briefings in bioinformatics
DNA/RNA motif mining is the foundation of gene function research. The DNA/RNA motif mining plays an extremely important role in identifying the DNA- or RNA-protein binding site, which helps to understand the mechanism of gene regulation and managemen...

MOCCA: a flexible suite for modelling DNA sequence motif occurrence combinatorics.

BMC bioinformatics
BACKGROUND: Cis-regulatory elements (CREs) are DNA sequence segments that regulate gene expression. Among CREs are promoters, enhancers, Boundary Elements (BEs) and Polycomb Response Elements (PREs), all of which are enriched in specific sequence mot...

A machine learning-based framework for modeling transcription elongation.

Proceedings of the National Academy of Sciences of the United States of America
RNA polymerase II (Pol II) generally pauses at certain positions along gene bodies, thereby interrupting the transcription elongation process, which is often coupled with various important biological functions, such as precursor mRNA splicing and gen...

DeepCLIP: predicting the effect of mutations on protein-RNA binding with deep learning.

Nucleic acids research
Nucleotide variants can cause functional changes by altering protein-RNA binding in various ways that are not easy to predict. This can affect processes such as splicing, nuclear shuttling, and stability of the transcript. Therefore, correct modeling...

CNN-Peaks: ChIP-Seq peak detection pipeline using convolutional neural networks that imitate human visual inspection.

Scientific reports
ChIP-seq is one of the core experimental resources available to understand genome-wide epigenetic interactions and identify the functional elements associated with diseases. The analysis of ChIP-seq data is important but poses a difficult computation...