DNA sequence motif: a jack of all trades for ChIP-Seq data.
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Summary
This review discusses numerous tasks starting from basic DNA motif finding and motif discovery as is, further applied to explore various features of experimental data, and shows how sequence analysis of ChIP-Seq data derives novel biological knowledge on multiple levels.
- Type
- review
- Published
- 2013-01-01
- Cited by
- 18
- References
- 118
- OpenAlex
- https://openalex.org/W48443837
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:32492284
Keywords
Chromatin immunoprecipitation, Sequence motif, Computational biology, Motif (music), Chromatin
References
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- Applied bioinformatics for the identification of regulatory elements
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- Comprehensive Genome-wide Protein-DNA Interactions Detected at Single Nucleotide Resolution
- Spi-1/PU.1 activates transcription through clustered DNA occupancy in erythroleukemia
- PeakSeq: Systematic Scoring of ChIP-Seq Experiments Relative to Controls
- Q&A: ChIP-seq technologies and the study of gene regulation
Cited by
- Bioinformatics Tools for the Analysis of Gene-Phenotype Relationships Coupled with a Next Generation ChIP-Sequencing Data Analysis Pipeline
- Analysis of pattern overlaps and exact computation of P-values of pattern occurrences numbers: case of Hidden Markov Models
- HOCOMOCO: expansion and enhancement of the collection of transcription factor binding sites models
- Negative selection maintains transcription factor binding motifs in human cancer
- Hidden heterogeneity of transcription factor binding sites: A case study of SF-1
- Identifying complex motifs in massive omics data with a variable-convolutional layer in deep neural network
- A Systematic Bioinformatics Approach to Motif-Based Analysis of Human Locus Control Regions
- A comparative benchmark of classic DNA motif discovery tools on synthetic data
- The Gene Regulation Knowledge Commons: The action area of GREEKC.
- Markonv: a novel convolutional layer with inter-positional correlations modeled
- A map of cis-regulatory modules and constituent transcription factor binding sites in 80% of the mouse genome
- SEAMoD: A fully interpretable neural network for cis-regulatory analysis of differentially expressed genes
- Discovering DNA shape motifs with multiple DNA shape features: generalization, methods, and validation
- Genomic background sequences systematically outperform synthetic ones in de novo motif discovery for ChIP-seq data
- A map of cis -regulatory modules and constituent transcription factor binding sites in 80% of the mouse genome
- Additional file 2 of A map of cis-regulatory modules and constituent transcription factor binding sites in 80% of the mouse genome
- Additional file 2 of A map of cis-regulatory modules and constituent transcription factor binding sites in 80% of the mouse genome
- Identifying complex sequence patterns with a variable-convolutional layer effectively and efficiently
- Network analysis in systems nutrition
- Identifying complex sequence patterns in massive omics data with a variable-convolutional layer in deep neural network
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