VIGLA-M: visual gene expression data analytics

Abstract

Background The analysis of gene expression levels is used in many clinical studies to know how patients evolve or to find new genetic biomarkers that could help in clinical decision making. However, the techniques and software available for these analyses are not intended for physicians, but for geneticists. However, enabling physicians to make initial discoveries on these data would benefit in the clinical assay development. Results Melanoma is a highly immunogenic tumor. Therefore, in recent years physicians have incorporated immune system altering drugs into their therapeutic arsenal against this disease, revolutionizing the treatment of patients with an advanced stage of the cancer. This has led us to explore and deepen our knowledge of the immunology surrounding melanoma, in order to optimize the approach. Within this project we have developed a database for collecting relevant clinical information for melanoma patients, including the storage of patient gene expression levels obtained from the NanoString platform (several samples are taken from each patient). The Immune Profiling Panel is used in this case. This database is being exploited through the analysis of the different expression profiles of the patients. This analysis is being done with Python, and a parallel version of the algorithms is available with Apache Spark to provide scalability as needed. Conclusions VIGLA-M, the visual analysis tool for gene expression levels in melanoma patients is available at http://khaos.uma.es/melanoma/ . The platform with real clinical data can be accessed with a demo user account, physician , using password physician_test_7634 (if you encounter any problems, contact us at this email address: mailto: khaos@lcc.uma.es). The initial results of the analysis of gene expression levels using these tools are providing first insights into the patients’ evolution. These results are promising, but larger scale tests must be developed once new patients have been sequenced, to discover new genetic biomarkers.

Genes Involved in Immune Reinduction May Constitute Biomarkers of Response for Metastatic Melanoma Patients Treated with Targeted Therapy

“…Computer analysis of gene expression data was performed using semi-automatic model reconstruction methods based on multi-objective optimisation heuristic techniques. Additionally, to facilitate data interpretation, we designed VIGLA-M, a web service that allows clinicians to explore gene-expression data [ 18 ]. To carry out the experiments at the necessary scale, Big Data Analysis techniques were applied, using the computational resources of the Ada Byron Research Building at the University of Malaga.…”

Framing Apache Spark in life sciences

“…Table 1 reports a summary of the main features of the tools proposed in these articles. The table shows that apart from some tools that reports tests only on a multi-core workstation ( [16] , [17] , [18] , [19] ), Spark has been widely used to implement tools aimed at parallelizing the computation on a distributed computing environment. Most of these tools have been specifically devised for, or tested on, a cloud environment ( [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] [29] , [30] , [31] , [32] [33] , [34] , [35] , [36] , [37] ).…”

A Service for Flexible Management and Analysis of Heterogeneous Clinical Data

“…With the aim of showing the potential of using the new functionalities of FIMED 2.0, the tool has been tested with real-world scenarios involving patients with advanced melanoma [14], as in the previous version of FIMED. Thus, we have validated the new analytical functionalities and visualisation techniques producing appropriate analysis and visualisation in cancer research.…”

“…We demonstrated the advantages of the new functionalities of FIMED 2.0 in a practical use case using real expression data from metastatic Melanoma patients used in previous works [14,7].…”