Ten Simple Rules for Effective Computational Research

Osborne, James M.; Bernabéu, Miguel O.; Bruna, María; Calderhead, Ben; Cooper, Jonathan; Dalchau, Neil; Dunn, Sara-Jane; Fletcher, Alexander G.; Freeman, Robin; Groen, Derek; Knapp, Bernhard; McInerny, Greg J.; Mirams, Gary R.; Pitt-Francis, Joe; Sengupta, Biswa; Wright, David W.; Yates, Christian A.; Gavaghan, David; Emmott, Stephen; Deane, Charlotte M.

doi:10.1371/journal.pcbi.1003506

Cited by 52 publications

(63 citation statements)

References 13 publications

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“…This gap is being addressed by guidance on best practices in scientific computing, metadata are becoming more commonplace (e.g., Michener 2006, 2015, Sandve et al 2013, Osborne et al 2014, Wilson et al 2014), and initiatives such as Software Carpentry ( http://software-carpentry.org ) teach skills in scientific computing via online resources and in-person workshops.…”

Section: Why Would Ecology and Evolution Have A Reproducibility Problem?mentioning

confidence: 99%

Metaresearch for Evaluating Reproducibility in Ecology and Evolution

Fidler¹,

Chee²,

Wintle³

et al. 2017

BioScience

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Recent replication projects in other disciplines have uncovered disturbingly low levels of reproducibility, suggesting that those research literatures may contain unverifiable claims. The conditions contributing to irreproducibility in other disciplines are also present in ecology. These include a large discrepancy between the proportion of “positive” or “significant” results and the average statistical power of empirical research, incomplete reporting of sampling stopping rules and results, journal policies that discourage replication studies, and a prevailing publish-or-perish research culture that encourages questionable research practices. We argue that these conditions constitute sufficient reason to systematically evaluate the reproducibility of the evidence base in ecology and evolution. In some cases, the direct replication of ecological research is difficult because of strong temporal and spatial dependencies, so here, we propose metaresearch projects that will provide proxy measures of reproducibility.

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Section: Why Would Ecology and Evolution Have A Reproducibility Problem?mentioning

confidence: 99%

Metaresearch for Evaluating Reproducibility in Ecology and Evolution

Fidler¹,

Chee²,

Wintle³

et al. 2017

BioScience

View full text Add to dashboard Cite

show abstract

“…The project is implemented in pure Python, employs unit testing [37] and is fully documented. Graphs in our code are represented using the Python package NetworkX [38].…”

Section: Methodsmentioning

confidence: 99%

Robust cell tracking in epithelial tissues through identification of maximum common subgraphs

Kursawe

Bardenet

Zartman

et al. 2016

J. R. Soc. Interface.

Self Cite

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Tracking of cells in live-imaging microscopy videos of epithelial sheets is a powerful tool for investigating fundamental processes in embryonic development. Characterizing cell growth, proliferation, intercalation and apoptosis in epithelia helps us to understand how morphogenetic processes such as tissue invagination and extension are locally regulated and controlled. Accurate cell tracking requires correctly resolving cells entering or leaving the field of view between frames, cell neighbour exchanges, cell removals and cell divisions. However, current tracking methods for epithelial sheets are not robust to large morphogenetic deformations and require significant manual interventions. Here, we present a novel algorithm for epithelial cell tracking, exploiting the graph-theoretic concept of a ‘maximum common subgraph’ to track cells between frames of a video. Our algorithm does not require the adjustment of tissue-specific parameters, and scales in sub-quadratic time with tissue size. It does not rely on precise positional information, permitting large cell movements between frames and enabling tracking in datasets acquired at low temporal resolution due to experimental constraints such as phototoxicity. To demonstrate the method, we perform tracking on the Drosophila embryonic epidermis and compare cell–cell rearrangements to previous studies in other tissues. Our implementation is open source and generally applicable to epithelial tissues.

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“…Although many in-depth technically driven manuals can be found on each of these subjects, biologists will likely be less intimidated by the teaching examples provided by Haddock and Dunn which describes commonly encountered biological data analysis problems that can be handled with simple programming code. Osborne et al also provide a jargon-free guide for scientific researchers to developing and using software for research purposes (Osborne et al 2014). As the language is mastered, researchers can continue to expand the basic concepts to tackle more complex data analyses and develop additional software.…”

Section: Basic Programming Languages and Toolsmentioning

confidence: 99%

Incorporating computational resources in a cancer research program

2014

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Recent technological advances have transformed cancer genetics research. These advances have served as the basis for the generation of a number of richly annotated datasets relevant to the cancer geneticist. In addition, many of these technologies are now within reach of smaller laboratories to answer specific biological questions. Thus, one of the most pressing issues facing an experimental cancer biology research program in genetics is incorporating data from multiple sources to annotate, visualize, and analyze the system under study. Fortunately, there are several computational resources to aid in this process. However, a significant effort is required to adapt a molecular biology-based research program to take advantage of these datasets. Here, we discuss the lessons learned in our laboratory and share several recommendations to make this transition effectively. This article is not meant to be a comprehensive evaluation of all the available resources, but rather highlight those that we have incorporated into our laboratory and how to choose the most appropriate ones for your research program.

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Ten Simple Rules for Effective Computational Research

Abstract: 25.02.15 KB. Ok to add published version to spiral, OA pape

Cited by 52 publications

References 13 publications

Metaresearch for Evaluating Reproducibility in Ecology and Evolution

Metaresearch for Evaluating Reproducibility in Ecology and Evolution

Robust cell tracking in epithelial tissues through identification of maximum common subgraphs

Incorporating computational resources in a cancer research program

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