Software by Katerina J. Kechris
- MCMSeq (R package: B. Vestal, C. Moore)
GitHub
Bayesian hierarchical modeling of clustered and repeated measures RNA sequencing experiments. see Vestal et al., 2020
- tidyMicro (R package: C. Carpenter)
GitHub
CRAN
A pipeline for microbiome data analysis and visualization using the tidyverse in R. (in press BMC Bioinformatics)
- SmCCNet (R package: J. Shi)
CRAN
GitHub
Correlation analysis based method for discovering (quantitative) trait-specific multi-omics networks. see Shi et al., 2019
- miR-MaGiC (Java/Snakemake pipeline: P. Russell)
GitHub
Pipeline for miRNA expression quantification from small RNA-seq see Russell et al., 2018
- HeritSeq (R package: J. Shi, P. Rudra, B. Vestal, P. Russell)
CRAN
Calculate heritability of count based expression traits derived from high-throughput sequencing experiments. see Rudra, Wen, Vestal et al., 2017
- Discordant (R package: C. Siska)
Bioconductor
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.
- multiMiR (R package: Y. Ru) Link to webserver
GitHub Bioconductor
Comprehensive collection of predicted and validated miRNA-target interactions and their associations with diseases and drugs. see Ru et al., 2014.
- MSPrep (R package: M. McGrath, G. Hughes)
Link
to Bioconductor
Post processing of LC/MS metabolomic data: Performs summarization of replicates, filtering, imputation, normalization, generates diagnostic plots and outputs final analytic datasets for downstream analysis. see Hughes et al., 2014.
- lcmix (R package: D. Dvorkin) R-Forge
Hierarchical mixture models for genomic data integration. see Dvorkin et al., 2013.
- comb-p (Python code: B. Pedersen) GitHub
Combining genome-wide p-values using a modified Stouffer-Liptak test corrected for spatial correlations. see Kechris et al., 2010 and Pedersen et al., 2012
- SULDEX (ANSI C++ code) Link to SULDEX Software at Pollock Lab
Simultaneously analyze binding dissociation constants for large repertoires of sequences based on high throughput sequencing. see Pollock et al., 2011
- c-REDUCE (ANSI C code: assisted by D. Dvorkin) Available upon request
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 (ANSI C code: M. Richards) Available upon request
OR-MEME (Order Restricted MEME): Detecting DNA regulatory motifs by constraining the order of information content. see van Zwet et al., 2005
- TFEM (ANSI C code: M. Richards) Available upon request
TFEM (Transcription Factor Expectation Maximization): Detecting DNA regulatory motifs by incorporating positional trends in information content. see Kechris et al., 2004