Automated Wormscan [version 1; referees: 2 approved, 1 approved with reservations]
There has been a recent surge of interest in computer-aided rapid data acquisition to increase the potential throughput and reduce the labour costs of large scale Caenorhabditis elegans studies. We present Automated WormScan, a low-cost, high-throughput automated system using commercial photo scanne...
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doaj-c2346c1e0bc94447b01ffaa5ac47f3e62020-11-25T03:31:46ZengF1000 Research LtdF1000Research2046-14022017-02-01610.12688/f1000research.10767.111609Automated Wormscan [version 1; referees: 2 approved, 1 approved with reservations]Timothy Puckering0Jake Thompson1Sushruth Sathyamurthy2Sinduja Sukumar3Tirosh Shapira4Paul Ebert5Plant Biosecurity Cooperative Research Centre, Canberra, ACT, 2617, AustraliaSchool of Biological Sciences, University of Queensland, St Lucia, QLD, 4072, AustraliaSchool of Biological Sciences, University of Queensland, St Lucia, QLD, 4072, AustraliaSchool of Biological Sciences, University of Queensland, St Lucia, QLD, 4072, AustraliaSchool of Biological Sciences, University of Queensland, St Lucia, QLD, 4072, AustraliaPlant Biosecurity Cooperative Research Centre, Canberra, ACT, 2617, AustraliaThere has been a recent surge of interest in computer-aided rapid data acquisition to increase the potential throughput and reduce the labour costs of large scale Caenorhabditis elegans studies. We present Automated WormScan, a low-cost, high-throughput automated system using commercial photo scanners, which is extremely easy to implement and use, capable of scoring tens of thousands of organisms per hour with minimal operator input, and is scalable. The method does not rely on software training for image recognition, but uses the generation of difference images from sequential scans to identify moving objects. This approach results in robust identification of worms with little computational demand. We demonstrate the utility of the system by conducting toxicity, growth and fecundity assays, which demonstrate the consistency of our automated system, the quality of the data relative to manual scoring methods and congruity with previously published results.https://f1000research.com/articles/6-192/v1Bioinformatics |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Timothy Puckering Jake Thompson Sushruth Sathyamurthy Sinduja Sukumar Tirosh Shapira Paul Ebert |
spellingShingle |
Timothy Puckering Jake Thompson Sushruth Sathyamurthy Sinduja Sukumar Tirosh Shapira Paul Ebert Automated Wormscan [version 1; referees: 2 approved, 1 approved with reservations] F1000Research Bioinformatics |
author_facet |
Timothy Puckering Jake Thompson Sushruth Sathyamurthy Sinduja Sukumar Tirosh Shapira Paul Ebert |
author_sort |
Timothy Puckering |
title |
Automated Wormscan [version 1; referees: 2 approved, 1 approved with reservations] |
title_short |
Automated Wormscan [version 1; referees: 2 approved, 1 approved with reservations] |
title_full |
Automated Wormscan [version 1; referees: 2 approved, 1 approved with reservations] |
title_fullStr |
Automated Wormscan [version 1; referees: 2 approved, 1 approved with reservations] |
title_full_unstemmed |
Automated Wormscan [version 1; referees: 2 approved, 1 approved with reservations] |
title_sort |
automated wormscan [version 1; referees: 2 approved, 1 approved with reservations] |
publisher |
F1000 Research Ltd |
series |
F1000Research |
issn |
2046-1402 |
publishDate |
2017-02-01 |
description |
There has been a recent surge of interest in computer-aided rapid data acquisition to increase the potential throughput and reduce the labour costs of large scale Caenorhabditis elegans studies. We present Automated WormScan, a low-cost, high-throughput automated system using commercial photo scanners, which is extremely easy to implement and use, capable of scoring tens of thousands of organisms per hour with minimal operator input, and is scalable. The method does not rely on software training for image recognition, but uses the generation of difference images from sequential scans to identify moving objects. This approach results in robust identification of worms with little computational demand. We demonstrate the utility of the system by conducting toxicity, growth and fecundity assays, which demonstrate the consistency of our automated system, the quality of the data relative to manual scoring methods and congruity with previously published results. |
topic |
Bioinformatics |
url |
https://f1000research.com/articles/6-192/v1 |
work_keys_str_mv |
AT timothypuckering automatedwormscanversion1referees2approved1approvedwithreservations AT jakethompson automatedwormscanversion1referees2approved1approvedwithreservations AT sushruthsathyamurthy automatedwormscanversion1referees2approved1approvedwithreservations AT sindujasukumar automatedwormscanversion1referees2approved1approvedwithreservations AT tiroshshapira automatedwormscanversion1referees2approved1approvedwithreservations AT paulebert automatedwormscanversion1referees2approved1approvedwithreservations |
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