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Computer aided data acquisition tool for high-throughput phenotyping of plant populations

BACKGROUND: The data generated during a course of a biological experiment/study can be sometimes be massive and its management becomes quite critical for the success of the investigation undertaken. The accumulation and analysis of such large datasets often becomes tedious for biologists and lab tec...

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Autores principales: Vankadavath, Raju Naik, Hussain, Appibhai Jakir, Bodanapu, Reddaiah, Kharshiing, Eros, Basha, Pinjari Osman, Gupta, Soni, Sreelakshmi, Yellamaraju, Sharma, Rameshwar
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2796657/
https://www.ncbi.nlm.nih.gov/pubmed/20003250
http://dx.doi.org/10.1186/1746-4811-5-18
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author Vankadavath, Raju Naik
Hussain, Appibhai Jakir
Bodanapu, Reddaiah
Kharshiing, Eros
Basha, Pinjari Osman
Gupta, Soni
Sreelakshmi, Yellamaraju
Sharma, Rameshwar
author_facet Vankadavath, Raju Naik
Hussain, Appibhai Jakir
Bodanapu, Reddaiah
Kharshiing, Eros
Basha, Pinjari Osman
Gupta, Soni
Sreelakshmi, Yellamaraju
Sharma, Rameshwar
author_sort Vankadavath, Raju Naik
collection PubMed
description BACKGROUND: The data generated during a course of a biological experiment/study can be sometimes be massive and its management becomes quite critical for the success of the investigation undertaken. The accumulation and analysis of such large datasets often becomes tedious for biologists and lab technicians. Most of the current phenotype data acquisition management systems do not cater to the specialized needs of large-scale data analysis. The successful application of genomic tools/strategies to introduce desired traits in plants requires extensive and precise phenotyping of plant populations or gene bank material, thus necessitating an efficient data acquisition system. RESULTS: Here we describe newly developed software "PHENOME" for high-throughput phenotyping, which allows researchers to accumulate, categorize, and manage large volume of phenotypic data. In this study, a large number of individual tomato plants were phenotyped with the "PHENOME" application using a Personal Digital Assistant (PDA) with built-in barcode scanner in concert with customized database specific for handling large populations. CONCLUSION: The phenotyping of large population of plants both in the laboratory and in the field is very efficiently managed using PDA. The data is transferred to a specialized database(s) where it can be further analyzed and catalogued. The "PHENOME" aids collection and analysis of data obtained in large-scale mutagenesis, assessing quantitative trait loci (QTLs), raising mapping population, sampling of several individuals in one or more ecological niches etc.
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spelling pubmed-27966572009-12-22 Computer aided data acquisition tool for high-throughput phenotyping of plant populations Vankadavath, Raju Naik Hussain, Appibhai Jakir Bodanapu, Reddaiah Kharshiing, Eros Basha, Pinjari Osman Gupta, Soni Sreelakshmi, Yellamaraju Sharma, Rameshwar Plant Methods Software BACKGROUND: The data generated during a course of a biological experiment/study can be sometimes be massive and its management becomes quite critical for the success of the investigation undertaken. The accumulation and analysis of such large datasets often becomes tedious for biologists and lab technicians. Most of the current phenotype data acquisition management systems do not cater to the specialized needs of large-scale data analysis. The successful application of genomic tools/strategies to introduce desired traits in plants requires extensive and precise phenotyping of plant populations or gene bank material, thus necessitating an efficient data acquisition system. RESULTS: Here we describe newly developed software "PHENOME" for high-throughput phenotyping, which allows researchers to accumulate, categorize, and manage large volume of phenotypic data. In this study, a large number of individual tomato plants were phenotyped with the "PHENOME" application using a Personal Digital Assistant (PDA) with built-in barcode scanner in concert with customized database specific for handling large populations. CONCLUSION: The phenotyping of large population of plants both in the laboratory and in the field is very efficiently managed using PDA. The data is transferred to a specialized database(s) where it can be further analyzed and catalogued. The "PHENOME" aids collection and analysis of data obtained in large-scale mutagenesis, assessing quantitative trait loci (QTLs), raising mapping population, sampling of several individuals in one or more ecological niches etc. BioMed Central 2009-12-10 /pmc/articles/PMC2796657/ /pubmed/20003250 http://dx.doi.org/10.1186/1746-4811-5-18 Text en Copyright ©2009 Vankadavath et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Software
Vankadavath, Raju Naik
Hussain, Appibhai Jakir
Bodanapu, Reddaiah
Kharshiing, Eros
Basha, Pinjari Osman
Gupta, Soni
Sreelakshmi, Yellamaraju
Sharma, Rameshwar
Computer aided data acquisition tool for high-throughput phenotyping of plant populations
title Computer aided data acquisition tool for high-throughput phenotyping of plant populations
title_full Computer aided data acquisition tool for high-throughput phenotyping of plant populations
title_fullStr Computer aided data acquisition tool for high-throughput phenotyping of plant populations
title_full_unstemmed Computer aided data acquisition tool for high-throughput phenotyping of plant populations
title_short Computer aided data acquisition tool for high-throughput phenotyping of plant populations
title_sort computer aided data acquisition tool for high-throughput phenotyping of plant populations
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2796657/
https://www.ncbi.nlm.nih.gov/pubmed/20003250
http://dx.doi.org/10.1186/1746-4811-5-18
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