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Data Wisdom in Computational Genomics Research

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Abstract

All fields of science are now inundated with massive amounts of data, which have the potential to answer fundamental questions. Genomics is one particular example, exploring questions like: How does the human genome work? What genome variants make us more prone to diseases? To find answers to these questions, it is crucial to develop statistical and machine learning methods that can scale up, particularly through efficient data storage and communication. Equally crucial, but less emphasized, is the possession of data wisdom—a rebranding of the best elements of applied statistics in a recent note at ODBMS.org (http://www.odbms.org/2015/04/data-wisdom-for-data-science/). The note at ODBMS.org contains ten sets of questions a practitioner can ask to cultivate data wisdom. Although there has been much recent excitement about big data, having enough data relevant to the problem is the key to gaining meaningful answers in genomics. Data wisdom gives us the insight into how these data would look, how much information a dataset really contains, and how to extract it. In this paper, we expand on the ten sets of questions and illustrate where and how data wisdom can be integrated into computational genomics research.

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Notes

  1. http://www.odbms.org/2015/04/data-wisdom-for-data-science/

  2. www.arabidopsis.org/portals/expression/microarray/ATGenExpress.jsp.

  3. Sulfur-rich amino acid-containing compounds which become active in response to tissue damage, and believed to offer a protective function

  4. Compounds of diverse biological activities such as anti-oxidants, functioning in UV protection, in defense, in auxin transport inhibition, and in flower coloring

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Acknowledgements

The authors thank Christine Ho, Elizabeth Purdom, Terry Speed, Karl Kumbier, Rachel Wang and Courtney Schiffman for their constructive comments and help on revising the manuscript. The work is partly supported by NIH U01-HG007031 and NSF DMS-1160319. This work is also supported in part by the Center for Science of Information (CSoI), an NSF Science and Technology Center, under grant agreement CCF-0939370.

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Authors and Affiliations

  1. Department of Statistics, University of California, Berkeley, USA

    Haiyan Huang & Bin Yu

  2. Department of Electrical Engineering and Computer Science, University of California, Berkeley, USA

    Bin Yu

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  1. Haiyan Huang
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  2. Bin Yu
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Correspondence to Haiyan Huang or Bin Yu.

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Huang, H., Yu, B. Data Wisdom in Computational Genomics Research. Stat Biosci 9, 646–661 (2017). https://doi.org/10.1007/s12561-016-9173-9

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