miRinGO: Prediction of GO terms indirectly targeted by human microRNAs

Master's project - identification of trans-eQTL clusters resulting from changes in transcription factor binding site preference.

R package to predict gene feed forward loops using mediation analysis. Analyses integrate observed miRNA and mRNA expression data and database information on gene interactions.

An R package to access, manage and visualize regulome (microRNA/transcription factors)-gene correlations in cancer

ForkedTF is an R-library that introduces Forked-PMW (FPMW) and Forked-Sequence Logos (F-Logos) to provide a more comprehensive depiction of the sequence affinity of a Transcription factor (TF) of interest, including its DNA sequence and DNA methylation level, along with a segregated list of partner TFs.

Shiny app for screening for core promoter occurrences close to expressed transposons TSSs.

Build SQLite tables of microRNAs and Transcription Factors-gene Correlations

After getting the bed file from the Peak calling, we can annotate and create all downstream analysis of two sets of data

Research compendium for the preprint "Hierarchical Regulation of Autophagy During Adipocyte Differentiation"

CMTCN: A web tool for investigating cancer-specific microRNA and transcription factor co-regulatory networks

Code for the BMC Genomics paper (Integrating binding and expression data to predict transcription factors combined function)

Analysis of Single Molecule Footprinting (SMF) data for the analysis of DNA methylation, chromatin accessibility and TF binding. The repository contains all primary code to reproduce the main analyses for the publication "Single molecule footprinting identifies context-dependent regulation of enhancers by DNA methylation" (Kreibich et al., 2023)

Code for the PeerJ paper (cRegulome: an R package for accessing microRNA and transcription factor-gene expression correlations in cancer)

An R package to identify plant transcription factors from protein sequence data and classify them in families

This repository enables the reproducible analysis of an important developmental neuroscience dataset on hypothalamus neuron differentiation. It performs quality control, clustering, trajectory analysis, and identification of marker genes to characterize the differentiation trajectories of glutamatergic and GABAergic Onecut3+ neuronal subtypes.

Scripts for reproducing the poster: Co-regulation of RKIP and autophagy genes by VEZF1 and ERCC6 in prostate cancer

geneXtendeR analysis on 547 ENCODE ChIP-seq datasets for both proximal and distal transcription factor (TF) binding peaks for all cell types

A research compendium: Transcriptional regulation of autophagy during adipocyte differentiation

Ligand-Receptor Interaction map based on scRNA-seq and pathway enrichment

ATAC-Seq Transcription Factor Footprint Discovery and Analysis