Dataset Information

Genetic programming based models in plant tissue culture: An addendum to traditional statistical approach.

ABSTRACT: In this paper, we compared the efficacy of observation based modeling approach using a genetic algorithm with the regular statistical analysis as an alternative methodology in plant research. Preliminary experimental data on in vitro rooting was taken for this study with an aim to understand the effect of charcoal and naphthalene acetic acid (NAA) on successful rooting and also to optimize the two variables for maximum result. Observation-based modelling, as well as traditional approach, could identify NAA as a critical factor in rooting of the plantlets under the experimental conditions employed. Symbolic regression analysis using the software deployed here optimised the treatments studied and was successful in identifying the complex non-linear interaction among the variables, with minimalistic preliminary data. The presence of charcoal in the culture medium has a significant impact on root generation by reducing basal callus mass formation. Such an approach is advantageous for establishing in vitro culture protocols as these models will have significant potential for saving time and expenditure in plant tissue culture laboratories, and it further reduces the need for specialised background.

SUBMITTER: Mridula MR

PROVIDER: S-EPMC5828343 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Genetic programming based models in plant tissue culture: An addendum to traditional statistical approach.

Mridula Meenu R MR Nair Ashalatha S AS Kumar K Satheesh KS

PLoS computational biology 20180227 2

In this paper, we compared the efficacy of observation based modeling approach using a genetic algorithm with the regular statistical analysis as an alternative methodology in plant research. Preliminary experimental data on in vitro rooting was taken for this study with an aim to understand the effect of charcoal and naphthalene acetic acid (NAA) on successful rooting and also to optimize the two variables for maximum result. Observation-based modelling, as well as traditional approach, could i ...[more]

PMID: 29485995

Similar Datasets

Project description:BackgroundComplexity and noise in expression quantitative trait loci (eQTL) studies make it difficult to distinguish potential regulatory relationships among the many interactions. The predominant method of identifying eQTLs finds associations that are significant at a genome-wide level. The vast number of statistical tests carried out on these data make false negatives very likely. Corrections for multiple testing error render genome-wide eQTL techniques unable to detect modest regulatory effects. We propose an alternative method to identify eQTLs that builds on traditional approaches. In contrast to genome-wide techniques, our method determines the significance of an association between an expression trait and a locus with respect to the set of all associations to the expression trait. The use of this specific information facilitates identification of expression traits that have an expression profile that is characterized by a single exceptional association to a locus. Our approach identifies expression traits that have exceptional associations regardless of the genome-wide significance of those associations. This property facilitates the identification of possible false negatives for genome-wide significance. Further, our approach has the property of prioritizing expression traits that are affected by few strong associations. Expression traits identified by this method may warrant additional study because their expression level may be affected by targeting genes near a single locus.ResultsWe demonstrate our method by identifying eQTL hotspots in Plasmodium falciparum (malaria) and Saccharomyces cerevisiae (yeast). We demonstrate the prioritization of traits with few strong genetic effects through Gene Ontology (GO) analysis of Yeast. Our results are strongly consistent with results gathered using genome-wide methods and identify additional hotspots and eQTLs.ConclusionsNew eQTLs and hotspots found with this method may represent regions of the genome or biological processes that are controlled through few relatively strong genetic interactions. These points of interest warrant experimental investigation.

Dataset Information

Genetic programming based models in plant tissue culture: An addendum to traditional statistical approach.

Publications

Genetic programming based models in plant tissue culture: An addendum to traditional statistical approach.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets