The somatic mutation identifier

David Torrents Arenales

Barcelona Supercomputing Centre, Spain

Research

The fast and accurate analysis of genomes will be required as the basis for the upcoming personalised healthcare system, in which the genetic information of each patient will be included in their clinical record. Within the Computational Genomics Group at the BSC, the project leader has recently developed Smufin.

This programme identifies mutations in disease genomes, from which doctors will soon be able to determine the precise diagnosis and prescribe the specific prognosis and treatment protocols for each patient. The innovative underlying principle of Smufin makes possible to identify all types of genomic variations in a single execution and in ten hours (compared to other methods that currently exist, which take four to six days), making it the first realistic solution for a fast, reliable and soon routine analysis of genomes within a personalised medical context.

Aim

To determine and solve potential bottlenecks in upcoming clinical protocols so specific treatments may be prescribed.

Problem to Solve

In the field of biomedicine, the identification of genomic alterations associated with disease has become one of the most important and limiting steps for the genetic-based classification and diagnosis of patients, as well as for the prescription of specific treatment protocols, which is the basis of the upcoming era of the personalised medicine.

Unfortunately, the process involved in current genome identification systems lasts four to seven days, and available solutions are complex and not accessible for the majority of the biomedical community. This results in a growing demand that is difficult to meet, even using some of the solutions available for genome analysis.

The fast and accurate analysis of genomes will be required as the basis for the upcoming personalised healthcare system, in which the genetic information of each patient will be included in their clinical record. This would allow patients to be treated using therapies that adapt to their specific genetic background.

Innovation

Smufin is a computer protocol designed for the identification of disease-causing mutations from disease genomes. This method is currently the only realistic solution for large scale and routine genomic analysis; first for research, but soon for public healthcare systems so increasingly efficient and personalised medicine may be implemented in modern societies.

Level of Innovation

The ideal use of genomic sequencing should involve the identification of all variants in order to correctly diagnose and select the best therapy. For clinical purposes, it is important to perform these computational processes in a short amount of time. However, a simple sequencing experiment typically takes days of analysis. As a result, the analysis of genomes for diagnostic and therapeutic purposes is still a major challenge, both in the design of efficient algorithms and at a computing performance level.

Smufin’s capabilities show significant improvements at various levels to overcome most of the past limitations associated with this process. These improvements range from the identification of previously undetected mutations, to the computing versatility and performance that are needed for an accurate and fast analysis of disease genomes.

Unlike other methodologies that currently exist, Smufin does not depend on the alignment of reads onto a reference genome. It makes possible to quickly and easily identify nearly all types of disease-causing mutations with high specificity in pathological genomes, including those that remain undetectable by other methods. All the limitations inherent to the use of the reference genome that apply to existing methods are overcome by Smufin.

Smufin has shown its ability to analyse hundreds of genomes within days and to generate results with a high clinical impact. All of this is proving that Smufin is ready to be commercialized as a service and that it is currently the most accurate, extensive and realistic option for the large-scale, integrated and routine analysis of disease genomes.