phylogram - Dendrograms for Evolutionary Analysis

Contains functions for developing phylogenetic trees as deeply-nested lists ("dendrogram" objects). Enables bi-directional conversion between dendrogram and "phylo" objects (see Paradis et al (2004) <doi:10.1093/bioinformatics/btg412>), and features several tools for command-line tree manipulation and import/export via Newick parenthetic text.

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peer-reviewed

8.74 score 12 stars 10 dependents 259 scripts 1.2k downloads

kmer - Fast K-Mer Counting and Clustering for Biological Sequence Analysis

Contains tools for rapidly computing distance matrices and clustering large sequence datasets using fast alignment-free k-mer counting and recursive k-means partitioning. See Vinga and Almeida (2003) <doi:10.1093/bioinformatics/btg005> for a review of k-mer counting methods and applications for biological sequence analysis.

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cpp

8.49 score 29 stars 7 dependents 101 scripts 706 downloads

aphid - Analysis with Profile Hidden Markov Models

Designed for the development and application of hidden Markov models and profile HMMs for biological sequence analysis. Contains functions for multiple and pairwise sequence alignment, model construction and parameter optimization, file import/export, implementation of the forward, backward and Viterbi algorithms for conditional sequence probabilities, tree-based sequence weighting, and sequence simulation. Features a wide variety of potential applications including database searching, gene-finding and annotation, phylogenetic analysis and sequence classification. Based on the models and algorithms described in Durbin et al (1998, ISBN: 9780521629713).

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cpp

7.09 score 22 stars 4 dependents 47 scripts 592 downloads

insect - Informatic Sequence Classification Trees

Provides tools for probabilistic taxon assignment with informatic sequence classification trees. See Wilkinson et al (2018) <doi:10.7287/peerj.preprints.26812v1>.

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6.72 score 15 stars 2 dependents 117 scripts 893 downloads

dclust - Divisive Hierarchical Clustering

Contains a single function 'dclust' for divisive hierarchical clustering based on recursive k-means partitioning (k = 2). Useful for clustering large datasets where computation of a n x n distance matrix is not feasible (e.g. n > 10,000 records). For further information see Steinbach M, Karypis G, Kumar V (2000) A Comparison of Document Clustering Techniques. Proceedings of World Text Mining Conference, KDD2000, Boston.

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2.00 score 1 stars 2 scripts 224 downloads