• VIGLA-M: visual gene expressio...

Treffer: VIGLA-M: visual gene expression data analytics.

Title:
VIGLA-M: visual gene expression data analytics.
Authors:
Navas-Delgado I; Khaos Research, Universidad de Málaga, Málaga, Spain, Arquitecto Francisco Peñalosa 18, Málaga, 29071, Spain. ismael@lcc.uma.es., García-Nieto J; Khaos Research, Universidad de Málaga, Málaga, Spain, Arquitecto Francisco Peñalosa 18, Málaga, 29071, Spain., López-Camacho E; Khaos Research, Universidad de Málaga, Málaga, Spain, Arquitecto Francisco Peñalosa 18, Málaga, 29071, Spain., Rybinski M; Khaos Research, Universidad de Málaga, Málaga, Spain, Arquitecto Francisco Peñalosa 18, Málaga, 29071, Spain., Lavado R; Unidad de Oncología Intercentros, Hospitales Univesitarios Regional y Virgen de la Victoria de Málaga, Instituto de Investigaciones Biomédicas (IBIMA), Málaga, Spain, Málaga, Spain., Berciano Guerrero MÁ; Unidad de Oncología Intercentros, Hospitales Univesitarios Regional y Virgen de la Victoria de Málaga, Instituto de Investigaciones Biomédicas (IBIMA), Málaga, Spain, Málaga, Spain., Aldana-Montes JF; Khaos Research, Universidad de Málaga, Málaga, Spain, Arquitecto Francisco Peñalosa 18, Málaga, 29071, Spain.
Source:
BMC bioinformatics [BMC Bioinformatics] 2019 Apr 18; Vol. 20 (Suppl 4), pp. 150. Date of Electronic Publication: 2019 Apr 18.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: BioMed Central Country of Publication: England NLM ID: 100965194 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2105 (Electronic) Linking ISSN: 14712105 NLM ISO Abbreviation: BMC Bioinformatics Subsets: MEDLINE
Imprint Name(s):
Original Publication: [London] : BioMed Central, 2000-
MeSH Terms:
Algorithms* , Data Science* , Gene Expression Regulation*, Cluster Analysis ; Databases, Factual ; Gene Expression Profiling ; Gene Regulatory Networks ; Humans ; Melanoma/genetics
References:
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Contributed Indexing:
Keywords: Gene Expression Level Analysis; Metastatic Melanoma; Nanostring Immune Profiling Panel
Entry Date(s):
Date Created: 20190420 Date Completed: 20190614 Latest Revision: 20200225
Update Code:
20250114
PubMed Central ID:
PMC6472185
DOI:
10.1186/s12859-019-2695-7
PMID:
30999846
Database:
MEDLINE

Weitere Informationen

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.