ArticleBMC bioinformatics2011
A motif-independent metric for DNA sequence specificity.
Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 24 citations in OpenAlex.
- Analyzing big datasets of genomic sequences: fast and scalable collection of k-mer statistics.BMC bioinformatics · 2019Article
- Deep learning architectures for prediction of nucleosome positioning from sequences data.BMC bioinformatics · 2018Article
- Deep learning models for bacteria taxonomic classification of metagenomic data.BMC bioinformatics · 2018Article
- Applications of alignment-free methods in epigenomics.Briefings in bioinformatics · 2014Review
- Analysis of chromatin-state plasticity identifies cell-type-specific regulators of H3K27me3 patterns.Proceedings of the National Academy of Sciences of the United States of America · 2014Article
- Linking genome to epigenome.Wiley interdisciplinary reviews. Systems biology and medicineReview
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundGenome-wide mapping of protein-DNA interactions has been widely used to investigate biological functions of the genome. An important question is to what extent such interactions are regulated at the DNA sequence level. However, current investigation is hampered by the lack of computational methods for systematic evaluating sequence specificity.
resultsWe present a simple, unbiased quantitative measure for DNA sequence specificity called the Motif Independent Measure (MIM). By analyzing both simulated and real experimental data, we found that the MIM measure can be used to detect sequence specificity independent of presence of transcription factor (TF) binding motifs. We also found that the level of specificity associated with H3K4me1 target sequences is highly cell-type specific and highest in embryonic stem (ES) cells. We predicted H3K4me1 target sequences by using the N- score model and found that the prediction accuracy is indeed high in ES cells.The software to compute the MIM is freely available at: https://github.com/lucapinello/mim.
conclusionsOur method provides a unified framework for quantifying DNA sequence specificity and serves as a guide for development of sequence-based prediction models.
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