ArticleAlgorithms for molecular biology : AMB2011
WordCluster: detecting clusters of DNA words and genomic elements.
Article in Algorithms for molecular biology : AMB, 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, 20 citations in OpenAlex.
- PCGIMA: developing the web server for human position-defined CpG islands methylation analysis.Frontiers in genetics · 2024Article
- Transfer Learning Allows Accurate RBP Target Site Prediction with Limited Sample Sizes.Biology · 2023Article
- A review of computational algorithms for CpG islands detection.Journal of biosciences · 2019Review
- Genome-Wide Profiling of DNA Methyltransferases in Mammalian Cells.Methods in molecular biology (Clifton, N.J.) · 2018Article
- Distinguishing Functional DNA Words; A Method for Measuring Clustering Levels.Scientific reports · 2017Article
- CpGislandEVO: a database and genome browser for comparative evolutionary genomics of CpG islands.BioMed research international · 2013Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMany k-mers (or DNA words) and genomic elements are known to be spatially clustered in the genome. Well established examples are the genes, TFBSs, CpG dinucleotides, microRNA genes and ultra-conserved non-coding regions. Currently, no algorithm exists to find these clusters in a statistically comprehensible way. The detection of clustering often relies on densities and sliding-window approaches or arbitrarily chosen distance thresholds.
resultsWe introduce here an algorithm to detect clusters of DNA words (k-mers), or any other genomic element, based on the distance between consecutive copies and an assigned statistical significance. We implemented the method into a web server connected to a MySQL backend, which also determines the co-localization with gene annotations. We demonstrate the usefulness of this approach by detecting the clusters of CAG/CTG (cytosine contexts that can be methylated in undifferentiated cells), showing that the degree of methylation vary drastically between inside and outside of the clusters. As another example, we used WordCluster to search for statistically significant clusters of olfactory receptor (OR) genes in the human genome.
conclusionsWordCluster seems to predict biological meaningful clusters of DNA words (k-mers) and genomic entities. The implementation of the method into a web server is available at http://bioinfo2.ugr.es/wordCluster/wordCluster.php including additional features like the detection of co-localization with gene regions or the annotation enrichment tool for functional analysis of overlapped genes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.