- Phenome-genome Explorer
rcNet (Rank Coherence in Networks) web tool provides an online resource to predict associations between disease phenotypes and gene sets. rcNet algorithms combine known disease-gene associations in OMIM with the topological information in the disease phenotype similarity network and the gene-gene interaction network to analyze the association between a gene set and disease phenotypes. The networks provide richer and more reliable information for computing the association scores used to rank the phenotypes. reNet algorithms could be applied to validate and analyze the candidate disease gene identified in GWAS, DNA copy number analysis, and Microarray gene expression profiling.
- Net-Cox: A network-guided Cox regression model
Net-Cox (network-based Cox regression model) integrates gene network information into the Cox’s proportional hazard model to explore the co-expression or functional relation among high-dimensional gene expression features in the gene network. Net-Cox with the network information from gene co-expression or functional relations identified highly consistent signature genes across datasets, and because of the better generalization across the datasets, Net-Cox also could improve the accuracy of survival prediction over the Cox models regularized by L2-norm or L1-norm.
- Net-RSTQ: Network-based method for RNA-Seq-based Transcript Quantification.
Net-RSTQ (Network-based method for RNA-Seq-based Transcript Quantification) integrates protein domain-domain interaction network with short read alignments for transcript abundance estimation. Based on our observation that the abundances of the neighboring isoforms by domaindomain interactions in the network are positively correlated, Net-RSTQ models the expression of the neighboring transcripts as Dirichlet priors on the likelihood of the observed read alignments against the transcripts in one gene. The transcript abundances of all the genes are then jointly estimated with alternating optimization of multiple EM problems.
- SubPatCNV: Subspace Pattern-ming of Copy Number Variations.
SubPatCNV (Subspace Pattern-ming of Copy Number Variations) is a data mining tool for discovery of CNV regions that exhibit in subsets of samples larger than a support threshold. SubPatCNV is suitable for analysis of arrayCGH data of a population or a patient cohort such as HapMap data or TCGA data to answer specific questions like “Which are all the chromosomal fragments showing nearly identical deletions or insertions in more than 30% of the individuals in the HapMap population or TCGA tumor samples?”. SubPatCNV is the implementation of a variation of approximate association pattern mining algorithm under a spatial constraint on the positional CNV probe features. The implementation scales to high-density array data with hundreds of thousands features.
- BiRW: Bi-random Walks for Phenome-Genome Association Prediction.
The availability of ontologies and systematic documentations of phenotypes and their genetic associations has enabled large-scale network-based global analyses of the association between the complete collection of phenotypes (phenome) and genes. BiRW is a package designed for analysis and prediction of the phenome-genome associations. BiRW package contains a program for analysis of circular bigraphs (CBGs) and the bi-random walk (BiRW) algorithm to capture the CBG patterns in the networks for unveiling human and mouse phenome-genome association. The processed OMIM human disease phenotype-gene association network and MGI mouse phentoype-gene association network are also released together with the source code.
- Signed-NP: two signed network propagation algorithms for detecting differential gene expressions and DNA copy number variations: The algorithms consider both positive and negative relations in graphs to model gene up/down-regulation or amplification/deletion CNV events. The first algorithm (Signed-NP) integrates gene co-expressions and differential expressions for consistent and robust gene selection from microarray datasets by propagation on gene correlation graphs. The second algorithm (Signed-NPBi) identifies gene or CNV markers by propagation on sample-feature bipartite graphs to capture bi-clusters between samples and genomic features.