Research groups

High Content Genomics and Bioinformatics (HCGB)

The main goal of the High Content Genomics and Bioinformatics Laboratory at the IGTP is to provide the institution with state of the art genomics and bioinformatics technologies to give support to the activities of its researchers. We offer support in experimental design, sample quality control, NGS library preparation and sequencing, microarray assay execution, and bioinformatic data analysis. We specialized in the discovery and typing of novel molecular markers for cancer and other disease prediction, centering on the use of DNA polymorphisms (SNPs and CNVs), differential gene expression (specifically of miRNAs and other non-coding RNAs), and chromatin modifications (DNA methylation), and the generation of predictors based on genomic data that can have an application on cancer preventive medicine.



Bioinformatics expertise:

Group Leader:

Lauro Sumoy

Group Leader


High Content Genomics and Bioinformatics Platform

Bioinformatics services offered

  • High Content Genomics and Bioinformatics Platform

    Bioinformatics Support Experimental design Sample size estimations Raw data quality control (overall assessment, outlier and batch effect detection) Data preparation (reformatting, reannotation, metadata curation) qPCR data analysis (efficiency correction, normalizer selection, relative quantification) Microarray data pre-processing (background correction, normalization) NGS data pre-processing (demultiplexing, adapter and base quality trimming) NGS data mapping to reference genome (duplicate removal, coverage analysis, read counting by biotype, gene, region, promoter, enhancer, chromosome segment) NGS de novo assembly (whole genome, transcriptome) Sequence variant calling and annotation (SNV/indels in genomes, exomes, panels, transcriptomes) Chromosomal location analysis (structural variations, LOH, chromatin peak, gene fusions, chromatin interactions) Differential statistical analysis (expression, methylation, copy number) Functional genomic analysis (GO, pathway, TF or miRNA binding, resistance, pathogenicity) Classification and predictor building Survival analysis Viral insertion analysis RNA soform analysis (splicing, editing, isomiR) Genetic variation analysis (Genome wide association analysis, microbial typing, viral quasipecies typing, epidemic outbreak phylogeny) Metagenomic analysis (taxa distribution, metabolic function enrichment) Genomic data visualization (genome browser tracks, graphics) Assistance for publication and data submission to repositories Assistance for accessing protected datasets