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Centre for Cancer Biomarkers CCBIO

CCBIO-BIG

CCBIO Bioinformatics Group

The CCBIO Bioinformatics Group (BIG) was established to facilitate work on bioinformatics analyses, and to increase cooperation in these matters across CCBIO research groups. David Fredman and Kjell Petersen from ELIXIR/CBU teamed up with representatives from several CCBIO research groups.

Kjell Petersen from the ELIXIR/CBU service group has been the main responsible for running the workshops, and Elisabeth Wik (CCBIO Junior Associate Investigator and postdoc in the group of Professor Akslen) has been coordinating the group.

The workshops have alternated between being structured with a lecture on the ‘topic-of-the-day’, followed by individual analysis work and support, and as plain analysis workshops. Topics covered in the structured parts of the workshops have been analyses of metadata (introduction and practical examples) and introduction to and suggestions for self-studies of the programming language R. Several workshops have covered NGS Data Analysis on NeLS Galaxy.

The seminars have been run by Kjell Petersen and Charitra Kumar Mishra from the ELIXIR/CBU support team, joint with the support workshops they run. There have been in total 6 seminars this year. Which researchers or groups from CCBIO that has taken part in the workshops has varied according to the ‘at-present’ need for this kind of support in the various CCBIO research groups.

CCBIO-BIG is aiming partly for joint workshops together with open CBU seminars (e.g. seminars and workshops on general bioinformatic topics). The group plans to do a short assessment of the estimated needs for bioinformatics support within CCBIO the coming year (2017), and will plan its activities accordingly. Additionally, CCBIO BIG has ongoing discussions with NORBIS (the National Research School in Bioinformatics, Biostatistics and Systems Biology) about developing workshops and/or courses (3-5 days) on cancer related bioinformatics, aiming to combine lectures on specific topics, and possibilities to in-depth learning of specific tools, along with hands-on training with the researchers’ own data.