Tools

NetSHy Network summarization via a hybrid approach leveraging topological properties. See Vu et al., 2024. Github

RCFGL Rapid Condition adaptive Fused Graphical Lasso and application to modeling brain region co-expression networks. See Seal et al., 2024. Github

Multi-omics subtyping pipeline for chronic obstructive pulmonary disease. See Gillenwater et al., 2021. Github

Correlation analysis based method for discovering (quantitative) trait-specific multi-omics networks. See Shi et al., 2019. CRAN Github

Identify pairs of features that differentially correlate between phenotypic groups, with application to -omics data. See Siska et al., 2016 and Siska & Kechris, 2017.. Bioconductor

Hierarchical mixture models for genomic data integration. see Dvorkin et al., 2013.

R package which uses a two-step approach to imputing missing data in metabolomics see Dekermanjian et al., 2022

A pathway integrated regression-based kernel association test with applications to metabolomics see Carpenter et al., 2021.

Database of metabolomics software tools and allows one to generate potential software workflows using an online interface. see Dekermanjian et al., 2021.

Reproducibility of mass spectrometry based metabolomics data.see Ghosh et al., 2021

Post processing of LC/MS metabolomic data. MsPRep Performs summarization of replicates, filtering, imputation, normalization, generates diagnostic plots and outputs final analytic datasets for downstream analysis. see Hughes et al., 2014.

Aptardi predicts polyadenylation sites in sample-specific transcriptomes using high-throughput RNA sequencing and DNA sequence see Lusk et al., 2021.

Bayesian hierarchical modeling of clustered and repeated measures RNA sequencing experiments. see Vestal et al., 2020

Pipeline for miRNA expression quantification from small RNA-seq see Russell et al., 2018

Calculate heritability of count based expression traits derived from high-throughput sequencing experiments. see Rudra, Wen, Vestal et al., 2017

Comprehensive collection of predicted and validated miRNA-target interactions and their associations with diseases and drugs. see Ru et al., 2014.

Simultaneously analyze binding dissociation constants for large repertoires of sequences based on high throughput sequencing. see Pollock et al., 2011.

c(onservation)-REDUCE. Extension of the REDUCE algorithm that incorporates conservation across multiple species to detect motifs that correlate with expression. see Kechris & Li, 2009

OR-MEME(Order Restricted MEME). Detecting DNA regulatory motifs by constraining the order of information content. see van Zwet et al., 2005

TFEM(Transcription Factor Expectation Maximization). Detecting DNA regulatory motifs by incorporating positional trends in information content. see Kechris et al., 2004