The increasing relevance of Big Data forms one of the grand challenges in modern life sciences. Analysing large data sets is consequently emerging to be one of the scientific key techniques in the post-genomic era. Recently, the growing need for the analysis of massive data has further accelerated by the advent and fast development of high-throughput next-generation sequencing technologies and the necessarily increasing cohort size of biomedical studies. Still, the data analysis bottleneck limits the rate with which technological advances in genome-scale experimental platforms can actually provide new medical and biological insights.

CAMDA focuses on the analysis of massive data in the life sciences. It introduces and evaluates new approaches and solutions to the Big Data challenge. The conference presents new techniques in the field of bioinformatics, data analysis, and statistics for the handling and processing of large data sets, the combination of multiple data sources, and effective computational inference.

An essential part of CAMDA is its open-ended data analysis challenge of complex data sets, often featuring novel technological platforms, exceptionally large cohorts, and heterogeneous data sources and types. Academic and industrial researchers worldwide alike are invited to take the CAMDA challenge. Accepted contributions are presented in short talks, and the results of analyses are discussed and compared at the CAMDA conference. Both contestants and other interested researchers are welcome at the meeting. Posters can provide an additional opportunity for presenting and discussing work.

CAMDA has a track record as a well-recognized annual conference going back to the year 2000. It soon received considerable attention from high impact journals like Nature (ref. 1, 2) and was featured in an editorial in Nature Methods in 2008 (ref. 3). Recently called the ‘Olympics for Genomics’, this allusion indicates the ambitious and wide-ranging nature of the contest. The meeting has regularly been supported by high-profile organizations like the FDA and NIST.

Keynotes by leading researchers in the field provide further focus points for discussion at the meeting and have recently included talks by Atul Butte and Sandrine Dudoit (Stanford), Rita Colwell (Maryland), Mark Gerstein (Yale), Curtis Huttenhower (Harvard), Nicos Kyrpides (DEO JGI)John Quackenbush (Dana Farber Cancer Institute, Boston), Chris Sander (Memorial Sloan Kettering Cancer Center, New York), Eran Segal (Weizmann Institute, Israel), John Storey and Olga Troyanskaya (Princeton), and Terry Speed (Berkeley & WEHI).

Come join us this year, we look forward to your participation!

• a low-volume announcements mailing list,
• an open forum for general discussions , including ideas and the development of future of contest data sets and challenges,
• an open forum for contest related discussions on metagenomics, including this year’s AMR prediction and tracking challenge,
• an open forum for contest related discussions on high dimensional data integration,
• an open forum for contest related discussions on clinical toxicity results in responses to drugs.

References:

1. Johnson, K.F. and Lin, S.M. (2001). Call to work together on microarray data analysis. Nature 411, 885.
2. Tilstone, C. (2003). Vital Statistics. Nature 424, 610 (link to journal)
3. Editorial feature (2008). Going for algorithm gold, Nature Methods 5, 659. (link to journal, link to article)
4. Mangul, S. (2019). Systematic benchmarking of omics computational tools, Nature Communications 10, 1393 (link to article)