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The khmer package is a freely available software library for working efficiently with fixed length DNA words, or k-mers. khmer provides implementations of a probabilistic k-mer counting data structure, a compressible De Bruijn graph representation, De Bruijn graph partitioning, and digital normalization. khmer is implemented in C++ and Python, and is freely… (More)
Many populations live in environments subject to frequent biotic and abiotic changes. Nonetheless, it is interesting to ask whether an evolving population's mean fitness can increase indefinitely, and potentially without any limit, even in a constant environment. A recent study showed that fitness trajectories of Escherichia coli populations over 50 000… (More)
In the context of Wright's adaptive landscape, genetic epistasis can yield a multipeaked or "rugged" topography. In an unstructured population, a lineage with selective access to multiple peaks is expected to fix rapidly on one, which may not be the highest peak. In a spatially structured population, on the other hand, beneficial mutations take longer to… (More)
Optimal foraging theory aims to elucidate strategies that maximize resource intake. Although traditionally used to understand animal foraging behavior, recent evolutionary experiments with viruses offer a new twist on an old idea.
Description and Objectives This graduate level survey course focuses on the fundamental elements of data analysis in the fields of ecology and evolution. Students will learn how to interpret and model biological data with computationally intensive methods for estimation and inference using the R language. Topics include probability theory, frequentist and… (More)
Memory is an essential component of intelligence as it enables an individual to make informed decisions based on past experiences. In the context of biological systems, however, what selective conditions promote the evolution of memory? Given that reliable memory is likely to be associated with costs, how much is it actually worth in different contexts? We… (More)