Campus Units

Statistics

Document Type

Article

Publication Version

Published Version

Publication Date

2015

Journal or Book Title

PLoS Computational Biology

Volume

11

Issue

3

First Page

1

Last Page

11

DOI

10.1371/journal.pcbi.1004140

Abstract

Computation has become a critical component of research in biology. A risk has emerged that computational and programming challenges may limit research scope, depth, and quality. We review various solutions to common computational efficiency problems in ecological and evolutionary research. Our review pulls together material that is currently scattered across many sources and emphasizes those techniques that are especially effective for typical ecological and environmental problems. We demonstrate how straightforward it can be to write efficient code and implement techniques such as profiling or parallel computing. We supply a newly developed R package (aprof) that helps to identify computational bottlenecks in R code and determine whether optimization can be effective. Our review is complemented by a practical set of examples and detailed Supporting Information material (S1S3 Texts) that demonstrate large improvements in computational speed (ranging from 10.5 times to 14,000 times faster). By improving computational efficiency, biologists can feasibly solve more complex tasks, ask more ambitious questions, and include more sophisticated analyses in their research.

Comments

This is an article from PLoS Computational Biology 11 (2015):1, doi:10.1371/journal.pcbi.1004140. Posted with permission.

Rights

This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

Language

en

File Format

application/pdf

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