AIMC Topic: Nucleotide Motifs

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

Journal of the American Chemical Society Aug 4, 2021
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...
Machine Learning DNA Nucleotide Motifs
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MOCCA: a flexible suite for modelling DNA sequence motif occurrence combinatorics.

BMC bioinformatics May 7, 2021
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...
Algorithms Support Vector Machine Base Sequence Nucleotide Motifs Position-Specific Scoring Matrices
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CNN-Peaks: ChIP-Seq peak detection pipeline using convolutional neural networks that imitate human visual inspection.

Scientific reports May 13, 2020
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...
Computational Biology Software Chromatin Immunoprecipitation Sequencing Epigenesis, Genetic Algorithms Epigenomics Databases, Nucleic Acid Protein Binding Histones Transcription Initiation Site Humans Binding Sites Neural Networks, Computer Nucleotide Motifs
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gammaBOriS: Identification and Taxonomic Classification of Origins of Replication in Gammaproteobacteria using Motif-based Machine Learning.

Scientific reports Apr 21, 2020
The biology of bacterial cells is, in general, based on information encoded on circular chromosomes. Regulation of chromosome replication is an essential process that mostly takes place at the origin of replication (oriC), a locus unique per chromoso...
Machine Learning Software Phylogeny Consensus Sequence Gammaproteobacteria Replication Origin Models, Genetic Base Sequence Nucleotide Motifs
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Machine Learning Techniques for Classifying the Mutagenic Origins of Point Mutations.

Genetics Mar 19, 2020
There is increasing interest in developing diagnostics that discriminate individual mutagenic mechanisms in a range of applications that include identifying population-specific mutagenesis and resolving distinct mutation signatures in cancer samples....
Point Mutation Ethylnitrosourea Machine Learning Animals Mice Sequence Analysis, DNA Mutagens Nucleotide Motifs
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Deep neural networks for interpreting RNA-binding protein target preferences.

Genome research Jan 28, 2020
Deep learning has become a powerful paradigm to analyze the binding sites of regulatory factors including RNA-binding proteins (RBPs), owing to its strength to learn complex features from possibly multiple sources of raw data. However, the interpreta...
RNA-Binding Proteins Binding Sites Nucleotide Motifs Deep Learning Protein Binding Humans Neural Networks, Computer Algorithms
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Fast and accurate microRNA search using CNN.

BMC bioinformatics Dec 27, 2019
BACKGROUND: There are many different types of microRNAs (miRNAs) and elucidating their functions is still under intensive research. A fundamental step in functional annotation of a new miRNA is to classify it into characterized miRNA families, such a...
Nucleotide Motifs MicroRNAs RNA, Transfer Base Pairing Neural Networks, Computer Probability Algorithms Base Sequence
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Discovery and annotation of novel microRNAs in the porcine genome by using a semi-supervised transductive learning approach.

Genomics Dec 6, 2019
Despite the broad variety of available microRNA (miRNA) prediction tools, their application to the discovery and annotation of novel miRNA genes in domestic species is still limited. In this study we designed a comprehensive pipeline (eMIRNA) for miR...
Animals Transcriptome RNA-Seq Genomics Nucleotide Motifs Molecular Sequence Annotation Machine Learning Muscle, Skeletal Liver Genome Sus scrofa Algorithms MicroRNAs Genetic Loci Sequence Homology, Nucleic Acid RNA Precursors
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Deciphering epigenomic code for cell differentiation using deep learning.

BMC genomics Sep 12, 2019
BACKGROUND: Although DNA sequence plays a crucial role in establishing the unique epigenome of a cell type, little is known about the sequence determinants that lead to the unique epigenomes of different cell types produced during cell differentiatio...
Nucleotide Motifs Cell Lineage Conserved Sequence Cell Differentiation CD4-Positive T-Lymphocytes Humans Histone Code Deep Learning Epigenomics
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MD-SVM: a novel SVM-based algorithm for the motif discovery of transcription factor binding sites.

BMC bioinformatics May 1, 2019
BACKGROUND: Transcription factors (TFs) play important roles in the regulation of gene expression. They can activate or block transcription of downstream genes in a manner of binding to specific genomic sequences. Therefore, motif discovery of these ...
Binding Sites Protein Binding Humans Nucleotide Motifs Transcription Factors Algorithms Support Vector Machine
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