Abstract
Biology as a whole has entered a new era in which data analysis plays a prominent role; but in the field of evolutionary genomics, data analysis has so far yielded little of value. This relative failure has been due in large part to methodological problems. Frequently, researchers have not sufficiently considered alternative hypotheses, leading to a kind of “computer-assisted storytelling”. Moreover, there has been widespread use of model-based statistical methods that depend heavily on assumptions regarding evolutionary processes of which we have little knowledge. The field of evolutionary genomics would benefit from a greater use of “sturdy statistics” that are model-free and make few assumptions about processes we do not understand.
Keywords: Drosophila, polyploidization, major histocompatibility complex (MHC), Assumption-Dependent Methods, robustness
Current Genomics
Title: The Future of Data Analysis in Evolutionary Genomics
Volume: 7 Issue: 4
Author(s): Austin L. Hughes, Robert Friedman and Nancy L. Glenn
Affiliation:
Keywords: Drosophila, polyploidization, major histocompatibility complex (MHC), Assumption-Dependent Methods, robustness
Abstract: Biology as a whole has entered a new era in which data analysis plays a prominent role; but in the field of evolutionary genomics, data analysis has so far yielded little of value. This relative failure has been due in large part to methodological problems. Frequently, researchers have not sufficiently considered alternative hypotheses, leading to a kind of “computer-assisted storytelling”. Moreover, there has been widespread use of model-based statistical methods that depend heavily on assumptions regarding evolutionary processes of which we have little knowledge. The field of evolutionary genomics would benefit from a greater use of “sturdy statistics” that are model-free and make few assumptions about processes we do not understand.
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Cite this article as:
Hughes L. Austin, Friedman Robert and Glenn L. Nancy, The Future of Data Analysis in Evolutionary Genomics, Current Genomics 2006; 7 (4) . https://dx.doi.org/10.2174/138920206778426942
DOI https://dx.doi.org/10.2174/138920206778426942 |
Print ISSN 1389-2029 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5488 |
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