My personal and professional adventure into Science

Archives for October 2015

Day 1 summary of ICG-10 – the 10th International Conference on Genomics

ICG10 – The 10th International Conference on Genomics #DTUicg10

On Tuesday I will finally get some air under my wings again (It’s been almost 2 months since my last flight !!), as I will be heading towards Shenzhen, China. The occasion is the 10th International Conference on Genomics held by BGIGigaScience and China National Genebank. The aim of the conference is to:

… gather leaders, researchers and professionals in ‘omics’ research focusing on human health, animal and plant science, including agriculture and related fields, to share their thoughts and discuss the implications of the latest developments in genomics.


The conference is from October 22-25, 2015. With more than 80 speaker split over 16 sessions I am sure that it will be very interesting. The program can be found here: Sessions and workshops. Note that on Sunday we have a session, S16 AI, Big Data and Health. This session is chaired by Ramneek Gupta from CBS and consist of speakers from CBS, DTU and KU (see below).

If you have not signed up for the conference, I will do my best to Tweet using the hashtag #DTUicg10, and if the internet connection allows it I will also write a few blog posts, so make sure to signup for notifications from my blog as well. You can do that on the right side of this page.

If you are attending the conference, then send me a tweet, Twitter, so we can meet for a beer at the banquet 🙂

Below is the session Chaired by DTU which I can only recommend you to attend!

S16 AI, Big Data and Health (15:45-18:20)

Chair: Ramneek Gupta, Technical University of Denmark, Denmark

Life Sciences today easily fulfils the major tenets of the need for Big Data thinking: Volume of Data, Velocity of data accrual and Variability of data. Indeed, all of these offer opportunities in gathering unprecedented insights into living systems. However, how far up the Hype Curve is current Big Data thinking in Life Sciences? How do we translate insights to value in the life sciences domain ? For example, when big data approaches are mentioned for precision medicine, what kind of approaches will get closer to clinical implementation ? What kind of infrastructural developments are needed in software or hardware or organisationally to accomplish this ? How does artificial intelligence help ? What are some of the basic challenges faced by the hugely increased Volume, Velocity and Variability of data ?

This track aims to present case stories or ideas in this direction.


Ramneek Gupta15:45 – 15:55
Ramneek Gupta
Technical University of Denmark, Denmark
Track Introduction: What is Big Data, and Why is it Relevant to Life Sciences?
Søren Brunak15:55-16:20
Søren Brunak
Technical University of Denmark, Denmark
Topic: Creating Disease Trajectories from Big Biomedical Data Covering Millions of Patients
Laurent Gautier16:20-16:40
Laurent Gautier
Novartis Institute for Biomedical Research, USA
Topic: Handle Big Data in Drug Discovery and Health Care with Software Prototyping
Line Clemmensen16:40-17:00
Line Clemmensen
Technical University of Denmark, Denmark
Topic: Slimming Big Data
Thomas Sicheritz-Ponten17:00-17:20
Thomas Sicheritz-Ponten
Technical University of Denmark, Denmark
Topic: Big Planes, Big Risks and Big Data
Helle Krogh Pedersen17:20-17:40
Helle Krogh Pedersen
Technical University of Denmark, Denmark
Topic: Ranking Factors Involved in Diabetes Resolution after Bariatric Surgery: A Neural Network Approach for Integrating Clinical and Genomic Data
Ole Lund17:40-18:00
Ole Lund
Technical University of Denmark, Denmark
Topic: Genomic Epidemiology
Peter Løngreen18:00-18:20
Peter Løngreen
Technical University of Denmark, Denmark
Topic: Supercomputing and Cloud: Converging Technologies in Life Science – Get four times as much information from your genome

Are you one of the people who bought a 23andme kit, and then ever since “wanted more”? Lasse Folkersen, one of my colleagues from CBS, is one of those people, but instead of just thinking about it, he actually did something about, and now he needs help on his recent Kickstarter project to back his imputation server.


Get four times as much information from your genome

Imputation is the name of a technology that is used in almost all major genetic studies today. Simply put it is a well-validated method of ‘guessing’ or imputing large parts of a non-measured genome, based on existing measurements from genotyping microarrays, as well as large reference databases such as the 1000 genomes project. From an input of 0.7M genotyped genetic variants, one typically gets knowledge of more than 4M new genetic variants.

However, the required command-line handling can be daunting to do this on a personal scale. That’s why an interesting new service called offers an easy interface to this technology. Upload your genome-data and get four times as much information back.That’s going from a large data file to an even larger data file.

But what can it then be used for? — the answer is broadly to get up-to-date with modern genetics research. Since most modern genetics studies use this technology, the majority of new findings are not available on basic direct-to-consumer microarrays, such as the ones offered by 23andme. So the second extension of the site is of course also to provide a selection of these novel analysis. A few basic analysis modes have already been implemented to show the added, benefit – such as for example a height-predictor, using the recent GIANT-consortiums findings on genetic variation of human height, as well as the ability to actually get the imputed data for this variation. However, the future idea is to provide many more of these modules – which are easily implementable, once the general imputation framework is in place.

Or – you could of course just enjoy your own personal 4 million extra data points in excel: the choice is yours, but there is no doubt that imputation is the way forward also for personal genomics.

You can see Lasses kickstarter project here.

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