Here is a list of software I developed.

Functional noncoding variants prioritization:

• traseR

  An R package for performing GWAS trait-associated SNP enrichment analyses in given genomic intervals.

• DIVAN

  An R pipeline for prioritizing disease-specific noncoding risk variants in 45 diseases/traits using genome-wide precomputed functional scores.

• WEVar

  A Python package for improving the prediction for noncoding regulatory variants using a weighted ensemble approach by integrating precomputed functional scores from multiple existing methods.

• TIVAN

  An R pipeline for predicting noncoding regulatory variants in 44 tissues/cell types.

• TIVAN-indel

  A Python package for implementing a novel computational method for predicting noncoding regulatory small insertions and deletions.

• TLVar

  A Python package for implementing a deep transfer learning approach to improve the prediction for experimentally validated regulatory variants.

• DeepPerVar

  A Python package for implementing a multi-modal deep learning model to predict individual-level noncoding functional variants.

Microbiome data analysis:

• GMPR

  An R package for normalizing zero-inflated count data particular microbiome sequencing data.

• glmgraph

  An R package for implementing sparse generalized linear models with graph-constrained regularization.

• SICS

  An R package for implementing sparse generalized linear models with encouraging local smoothing in a phylogeny-constrained regularization for predictive modeling of microbiome data.

• glmmTree

  An R package for implementing a phylogenetic tree-based generalized mixed effects model for predictive modeling of microbiome data.

• powmic

  An R package for performing power assessment in microbiome sequencing data.

• MDeep

  A Python package for implementing a novel deep learning model to predict phenotype using microbiome data by embedding the phylogenetic tree in the deep learning model.

Other multi-omics data analysis (single-cell genomics, epigenetics, noncoding RNA):

• BAMMSC

  An R package for implementing a novel Bayesian mixture model to cluster droplet-based single cell transcriptomic data from multiple individuals.

• DeepPHiC

  A Python package for implementing a novel multi-task and transfer deep learning model to predicting promoter-centered chromatin interactions using promoter-centered Hi-C data.

• circMeta

  An R package for performing genomic feature annotation and implementing a novel Bayesian hierarchical model to detect differential expression analysis of circular RNAs.

• tfLDA

  An R package for applying state-of-the-art topic models to decipher the combinatorial binding events of multiple TFs by integrating multiple ChIP- Seq datasets.

• MTAE

  A Python package for implementing a novel multi-task deep autoencoder to predict AD progression using longitudinal DNA methylation data in peripheral blood.

• ChIPComp

  An R package for implementing a novel Bayesian hierarchical model for quantitative comparison of multiple ChIP-seq datasets.

• hmChIP

  A web server and database for cell type-specific ChIP-seq and ChIP-chip data query.