Posted by Erik Svensson
This forthcoming Tuesday, we will discuss two papers, one chosen by Katie and one by me, which deal with
genomics and
phenomics, respectively. The latter term is still a bit unfamiliar to many, but "phenomics" is most likely a new word you would like to know, as it will hopefully become the new "buzzword" in the future and in the postgenomic era. Hopefully, we can have a general discussion of principal interest how to connect the more traditional field of genomics and the emerging field of phenomics.
You will find Abstracts and links to the two papers below. Time and place as usual:
When: Tuesday, February 18 2015
Where: "Argumentet", 2nd floor, Ecology Building
The analysis of polymorphism data is becoming increasingly important as a complementary tool to classical genetic analyses. Nevertheless, despite plunging sequencing costs, genomic sequencing of individuals at the population scale is still restricted to a few model species. Whole-genome sequencing of pools of individuals (Pool-seq) provides a cost-effective alternative to sequencing individuals separately. With the availability of custom-tailored software tools, Pool-seq is being increasingly used for population genomic research on both model and non-model organisms. In this Review, we not only demonstrate the breadth of questions that are being addressed by Pool-seq but also discuss its limitations and provide guidelines for users.
Despite
a large and multifaceted effort to understand the vast landscape of
phenotypic data, their current form inhibits productive data analysis.
The lack of a community-wide, consensus-based, human- and
machine-interpretable language for describing phenotypes and their
genomic and environmental contexts is perhaps the most pressing
scientific bottleneck to integration across many key fields in biology,
including genomics, systems biology, development, medicine, evolution,
ecology, and systematics. Here we survey the current phenomics
landscape, including data resources and handling, and the progress that
has been made to accurately capture relevant data descriptions for
phenotypes. We present an example of the kind of integration across
domains that computable phenotypes would enable, and we call upon the
broader biology community, publishers, and relevant funding agencies to
support efforts to surmount today's data barriers and facilitate
analytical reproducibility.
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