LAb Tools

1. TCGAbiolinks

allows you to :

  • retrieve GDC open-access data
  • prepare the data using the appropriate pre-processing strategies
  • carry out different molecular (differential) analyses
  • reproduce earlier research results

The package provides multiple methods for analysis (e.g., differential expression analysis, identifying differentially methylated regions) and methods for visualization (e.g., survival plots, volcano plots, starburst plots) in a complete analysis pipeline.

We provide a detail workflow to get started. Visit here and here.

2. TCGAbiolinksGUI

A Graphical User Interface of TCGAbiolinks to analyze cancer molecular and clinical data. A demo version of GUI is found here 

3. GliomaBiolinks

GliomaBiolinks calculates

  1. IDH mutation status
  2. Epigenomic subtype (additionally presented as an interactive plot)
  3. Copy number alterations represented as a CNV plot (highlighted genes include CDKN2A/B deletion, MET amplification and others.)
  4. Risk of progression from G-CIMP-high to G-CIMP-low
  5. MGMT promoter status.


ELMER is designed to use DNA methylation and gene expression from a large n

umber of samples to infere regulatory element landscape and transcription factor network in primary tissue.

5. FunciSNP

FunciSNP integrates information from GWAS, 1000genomes and chromatin feature to identify functional SNP in coding or non-coding regions.

6. SpidermiR

The aims of SpidermiR are :

  1. facilitate the network open-access data retrieval from GeneMania data
  2. prepare the data using the appropriate gene nomenclature
  3. integration of miRNA data in a specific network
  4. provide different standard analyses
  5. allow the user to visualize the results.

The package provides multiple methods for query, prepare and download network data (GeneMania), and the integration with validated and predicted miRNA data (mirWalk, miRTarBase, miRandola, Miranda, PicTar and TargetScan). Furthermore, we also present a statistical test to identify pharmaco-mir relationships using the gene-drug interactions derived by DGIdb and MAT

ADOR database.


We propose a generic framework for gene regulatory network (GRN) inference approached as a feature selection problem. GRNs obtained using Machine Learning techniques are often dense, whereas real GRNs are rather sparse. We use a Tikonov regularization inspired optimal L-curve criterion that utilizes the edge weight distribution for a given target gene to determine the optimal set of TFs associated with it. Our proposed framework allows to incorporate a mechanistic active biding network based on cis-regulatory motif analysis.