Computational Genomics Lab



  • Junk DNA, non-coding DNA
  • Adverse Drug Reaction / Personalized medicine
  • Next generation sequencing / Exome sequencing / Whole genome sequencing
  • Mutation detection





Computational Genomics Lab in the Neuroscience department at SISSA

Identification of genetic profiles by analyzing the exome and whole genome sequencing data with special regards to junk DNA, a new opportunity to understand the variability of the response in drug treatments.


The Computational Genomics Lab is composed of biologists and computer scientists who combine molecular biology and functional genomics with the development and analysis of bioinformatics pipelines.

The laboratory studies the organization of the genome, its transcriptional output, the activity and evolution of non-coding DNA and transposons (also called junk DNA). The focus relies on how these features shape the genomes of living organisms and are involved in the establishment of diseases and illnesses, with a strong focus on health, the nervous system and somatic variations.

The laboratory specifically develops bioinformatics pipelines able to analyze a large number of data produced by functional genomics platforms in particular sequencing machines of the latest generations.

Thanks to this expertise the laboratory can analyze the sequence of the genome of any individual and can also identify variations determined by junk DNA at different stages of life and their relation to the responses of our genes to environmental and life-style change.


Scientific Background

Remo Sanges is the coordinator of the laboratory is a molecular and computational biologist with extensive experience in development and usage of bioinformatics pipelines, data integration and harmonization, tools, methods and databases for large-scale functional genomics data analysis and more than 15 years of teaching experience.



The laboratory has developed different bioinformatics pipelines to analyze sequencing data, annotate genomic regions, finding gene clusters, mining and annotating de-novo generated transcriptomes. These tools are widely used by the community and regularly cited.

The latest developments regard the identification and prioritization of mutations/variations potentially associated to specific diseases and phenotypes. The computation genomic lab's pipeline, differently from other standard ones, is capable to identify also mutation generated by the activity of junk DNA (transposable elements) and uses ad-hoc developed prioritization modules capable to increase the rate of annotation and identification of potentially causative variants.


Advantages and Innovative features of the Solution

This expertise allows to profile DNA being capable to analyze also all those regions of the human genome which have not been considered so far (non-coding and transposons) and that can contain important information also potentially related to the variability of the response of a patient to a given drug.

Main advantages:

Complete analysis of a genetic profile

Custom bioinformatics analysis on exome and whole genome sequencing data

Combination of molecular and computational biology skills


Potential Applications

This expertise is of potential interest in the field of personalized medicine.

Variability in the response to drug treatment between patient and patient is one of the major problems in clinical practice. Individual responses to drugs vary widely.

In addition to factors such as age, sex and lifestyle, it is believed that hereditary factors have an important role in the individual response to drugs.

The clinical consequences of this can represent in the pharmacological treatment a therapeutic failure with lack or only partial efficacy of the therapy, constituted by side effects of the active principle or by serious and sometimes fatal adverse reactions.

Information about some of the specific responses of a patient could therefore be contained in his/her genetic profile. Genomics studies, along with the study of the so-called junk DNA, have the potential to predict the response of patients to certain drugs keeping into account crucial genetic information to be used by clinicians to decide the optimal therapy and personalize the dosages.

The benefits will consist in a reduced incidence of adverse reactions, in better clinical outcomes and in reduced costs.


Target Companies

Drug discovery companies interesting in a personalized medicine



Companies Contract Research Organization (CRO)