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Knowledge in biostatistics and bioinformatic analyses is valuable in medial disciplines

CCBIO/CBU recently held a workshop in ‘Basic R’, providing a great networking opportunity for CCBIO juniors in workshop a on the programming language R. CCBIO finds that increased knowledge in bioinformatic analyses and relevant analysis tools is important to fully understand the different levels of high-throughput data used in cancer research.

Collage of photos, the lecturers in front of the screen, and photos of the audience.
Lecturers Konstantina Dimitrakopoulou, Tomasz Furmanek, Kjell Petersen and Charitra Kumar Mishra, and participants at the ‘Basic R’ workshop.
Photo:
Elisabeth Wik

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By Eli Synnøve Vidhammer and Elisabeth WikUpdated: 31.08.2017 (First published: 21.08.2017)

As part of CCBIO’s strategy to strengthen the bioinformatic axis of the center, and to prepare the CCBIO scientists for the NORBIS/CCBIO 3-days seminar on networks biology, the CCBIO Bioinformatics Group (CCBIO-BIG) teamed up with the CBU support team (Kjell Petersen, Charitra Kumar Mishra and Tomasz Furmanek) for a workshop on ‘Basic R’. Konstantina Dimitrakopoulou (postdoc in Prof. Inge Jonassen and Prof. Lars A. Akslen’s groups) also contributed.    

R is a programming language, widely applicable in biostatistics and bioinformatic analyses. R is also known as a valuable resource for data plotting.

Thirty CCBIOers gathered 16th and 17th of August to learn the basic steps of applying R in biostatistics and bioinformatic analyses. Kjell Petersen and colleagues gave massive amounts of input on basic R programming to the students, who all seemed eager to learn. Additionally to learn some of the basic steps in R, an aim for the workshop was networking of CCBIOers who will be applying R as an analysis tool in biostatistics and bioinformatics.

Maybe stronger collaborations amongst the CCBIO juniors will be one of the fruits after this workshop? 

Network science meets omics data

The functioning of living organisms is controlled by highly complex networks of interacting molecular components like signal transduction, protein-protein interaction, metabolic and gene regulatory networks. Disease analysis has recently undergone a paradigm shift due to the unprecedented amount of multiple omics data produced and the introduction of network science which moved understanding of disease etiology and progression from the level of single molecules to perturbations upon the complex intracellular network.

Moreover, systems-level approaches integrate omics data from different sources as an effective means to deal with the high noise in each individual source and provide a more realistic cellular landscape. Integromics approaches have been shown to improve our comprehension of biological systems and enable more robust disease classification and discovery of biomarkers and drug targets.