A topological data analysis (TDA)-based discovery tool for visual and analytical exploration of multi-dimensional phenomics data.
Citation: M. Kamruzzaman et al. arXiV preprint arXiV:1707.04362, 2017.
A C++ code for detecting communities in a bipartite network. There are versions of the code that can be used for strict bipartite community detection (using bipartite modularity as the objective), and for detecting communities in bipartite graphs that also have intra-type edges.
Citation: P. Pesantez, A. Kalyanaraman. IEEE TCBB, 2018, doi: 10.1109/TCBB.2017.2765319
A graph toolkit that contains multithreaded implementations for parallel graph community detection and for balanced graph coloring.
Citations: H. Lu et al. Parallel Computing, Vol.
47, pp. 19-37, 2015.
H. Lu et al. IEEE TPDS, Vol. 28, No. 5, pp. 1240-1256, 2017.
A parallel (MPI) program to construct large-scale protein sequence homology graphs on parallel distributed and shared memory computers. Given n input sequences, the goal is to identify all pairs of sequences that are highly similar (based on a user-specified set of alignment criteria). The code generates optimal alignments (using Smith-Waterman algorithm) and uses suffix trees to prune the search space prior to performing alignments. The implementation is parallel and runs on MPI clusters. It provides a scalable parallel implementation for all-against-all frameworks in bioinformatics.
Citation: J. Daily et al. JPDC, Vol. 79-80, pp. 132-142, 2015.
An older version of this method can be accessed here:
Citation: C. Wu et al. IEEE TPDS, Vol. 23, No. 10, pp. 1923-1933, 2012.
A scalable parallel software for detecting dense subgraphs
(clusters) in large-scale protein sequence homology graphs (e.g.,
Citation: C. Wu, A. Kalyanaraman, SC'08, pp. 1-10, 2008.
Serial and parallel software for de novo identification of full-length LTR retrotransposons (a class of genomic repeats).
Citation: A. Kalyanaraman, S. Aluru. JBCB, Vol. 4, No. 2, pp. 197-216, 2006.
Parallel software in C/MPI for clustering large-scale Expressed Sequence Tags (or ESTs) data. This tool was developed in the early 2000s. It can also be used for modern day RNAseq/transcriptomics data clustering although it is recommended that the new generation tools be used for that purpose.
Citation: A. Kalyanaraman et al., NAR, Vol. 31, No. 11, pp. 2963-2974, 2003.