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seqArchR:Identify Different Architectures of Sequence Elements
seqArchR enables unsupervised discovery of _de novo_ clusters with characteristic sequence architectures characterized by position-specific motifs or composition of stretches of nucleotides, e.g., CG-richness. seqArchR does _not_ require any specifications w.r.t. the number of clusters, the length of any individual motifs, or the distance between motifs if and when they occur in pairs/groups; it directly detects them from the data. seqArchR uses non-negative matrix factorization (NMF) as its backbone, and employs a chunking-based iterative procedure that enables processing of large sequence collections efficiently. Wrapper functions are provided for visualizing cluster architectures as sequence logos.
Maintained by Sarvesh Nikumbh. Last updated 5 months ago.
motifdiscoverygeneregulationmathematicalbiologysystemsbiologytranscriptomicsgeneticsclusteringdimensionreductionfeatureextractiondnaseqnmfnonnegative-matrix-factorizationpromoter-sequence-architecturesscikit-learnsequence-analysissequence-architecturesunsupervised-machine-learning
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