DOI: 10.18129/B9.bioc.seqArchR  

Identify Different Architectures of Sequence Elements

Bioconductor version: Release (3.17)

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.

Author: Sarvesh Nikumbh [aut, cre, cph]

Maintainer: Sarvesh Nikumbh <sarvesh.nikumbh at>

Citation (from within R, enter citation("seqArchR")):


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HTML R Script Example usage of _seqArchR_ on simulated DNA sequences
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biocViews Clustering, DNASeq, DimensionReduction, FeatureExtraction, GeneRegulation, Genetics, MathematicalBiology, MotifDiscovery, Software, SystemsBiology, Transcriptomics
Version 1.4.0
In Bioconductor since BioC 3.15 (R-4.2) (1.5 years)
License GPL-3 | file LICENSE
Depends R (>= 4.2.0)
Imports utils, graphics, cvTools (>= 0.3.2), MASS, Matrix, methods, stats, cluster, matrixStats, fpc, cli, prettyunits, reshape2 (>= 1.4.3), reticulate (>= 1.22), BiocParallel, Biostrings, grDevices, ggplot2 (>= 3.1.1), ggseqlogo (>= 0.1)
Suggests cowplot, hopach(>= 2.42.0), BiocStyle, knitr (>= 1.22), rmarkdown (>= 1.12), testthat (>= 3.0.2), covr, vdiffr (>= 0.3.0)
SystemRequirements Python (>= 3.5), scikit-learn (>= 0.21.2), packaging
Depends On Me
Imports Me seqArchRplus
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