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A data analysis framework for biomedical big data: Application on mesoderm differentiation of human pluripotent stem cells
University of Skövde, School of Bioscience. University of Skövde, The Systems Biology Research Centre. (Bioinformatik, Bioinformatics)ORCID iD: 0000-0001-9242-4852
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0003-2973-3112
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0003-2900-9335
Takara Bio Europe AB, Gothenburg, Sweden.
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2017 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 12, no 6, e0179613Article in journal (Refereed) Published
Abstract [en]

The development of high-throughput biomolecular technologies has resulted in generation of vast omics data at an unprecedented rate. This is transforming biomedical research into a big data discipline, where the main challenges relate to the analysis and interpretation of data into new biological knowledge. The aim of this study was to develop a framework for biomedical big data analytics, and apply it for analyzing transcriptomics time series data from early differentiation of human pluripotent stem cells towards the mesoderm and cardiac lineages. To this end, transcriptome profiling by microarray was performed on differentiating human pluripotent stem cells sampled at eleven consecutive days. The gene expression data was analyzed using the five-stage analysis framework proposed in this study, including data preparation, exploratory data analysis, confirmatory analysis, biological knowledge discovery, and visualization of the results. Clustering analysis revealed several distinct expression profiles during differentiation. Genes with an early transient response were strongly related to embryonic-and mesendoderm development, for example CER1 and NODAL. Pluripotency genes, such as NANOG and SOX2, exhibited substantial downregulation shortly after onset of differentiation. Rapid induction of genes related to metal ion response, cardiac tissue development, and muscle contraction were observed around day five and six. Several transcription factors were identified as potential regulators of these processes, e.g. POU1F1, TCF4 and TBP for muscle contraction genes. Pathway analysis revealed temporal activity of several signaling pathways, for example the inhibition of WNT signaling on day 2 and its reactivation on day 4. This study provides a comprehensive characterization of biological events and key regulators of the early differentiation of human pluripotent stem cells towards the mesoderm and cardiac lineages. The proposed analysis framework can be used to structure data analysis in future research, both in stem cell differentiation, and more generally, in biomedical big data analytics.

Place, publisher, year, edition, pages
2017. Vol. 12, no 6, e0179613
National Category
Bioinformatics and Systems Biology Bioinformatics (Computational Biology)
Research subject
Bioinformatics; Skövde Artificial Intelligence Lab (SAIL)
Identifiers
URN: urn:nbn:se:his:diva-14015DOI: 10.1371/journal.pone.0179613ISI: 000404541500020PubMedID: 28654683Scopus ID: 2-s2.0-85021324072OAI: oai:DiVA.org:his-14015DiVA: diva2:1134983
Available from: 2017-08-22 Created: 2017-08-22 Last updated: 2017-09-14Bibliographically approved

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Ulfenborg, BenjaminKarlsson, AlexanderRiveiro, MariaSartipy, PeterSynnergren, Jane
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