scholarly journals Statistical designs for two-color microarray experiments involving technical replication

2006 ◽  
Vol 51 (3) ◽  
pp. 2078-2090 ◽  
Author(s):  
Shin-Fu Tsai ◽  
Chen-Tuo Liao ◽  
Feng-Shun Chai
Keyword(s):  
Microarray Experiments ◽  
Statistical Designs ◽  
Technical Replication

Statistical Designs for Two-Color Spotted Microarray Experiments

2007 ◽  
Vol 49 (2) ◽  
pp. 259-271 ◽  
Author(s):  
Feng-Shun Chai ◽  
Chen-Tuo Liao ◽  
Shin-Fu Tsai
Keyword(s):  
Microarray Experiments ◽  
Statistical Designs

scholarly journals Genomics and breeding innovations for enhancing genetic gain for climate resilience and nutrition traits

2021 ◽  
Author(s):  
Pallavi Sinha ◽  
Vikas K. Singh ◽  
Abhishek Bohra ◽  
Arvind Kumar ◽  
Jochen C. Reif ◽  
...  
Keyword(s):  
Statistical Methods ◽  
Genetic Gain ◽  
Crop Improvement ◽  
Breeding Population ◽  
Breeding Cycle ◽  
Climate Resilience ◽  
Heritability Estimation ◽  
Improvement Programs ◽  
Statistical Designs ◽  
Novel Alleles

Abstract Key message Integrating genomics technologies and breeding methods to tweak core parameters of the breeder’s equation could accelerate delivery of climate-resilient and nutrient rich crops for future food security. Abstract Accelerating genetic gain in crop improvement programs with respect to climate resilience and nutrition traits, and the realization of the improved gain in farmers’ fields require integration of several approaches. This article focuses on innovative approaches to address core components of the breeder’s equation. A prerequisite to enhancing genetic variance (σ2g) is the identification or creation of favorable alleles/haplotypes and their deployment for improving key traits. Novel alleles for new and existing target traits need to be accessed and added to the breeding population while maintaining genetic diversity. Selection intensity (i) in the breeding program can be improved by testing a larger population size, enabled by the statistical designs with minimal replications and high-throughput phenotyping. Selection priorities and criteria to select appropriate portion of the population too assume an important role. The most important component of breeder′s equation is heritability (h2). Heritability estimates depend on several factors including the size and the type of population and the statistical methods. The present article starts with a brief discussion on the potential ways to enhance σ2g in the population. We highlight statistical methods and experimental designs that could improve trait heritability estimation. We also offer a perspective on reducing the breeding cycle time (t), which could be achieved through the selection of appropriate parents, optimizing the breeding scheme, rapid fixation of target alleles, and combining speed breeding with breeding programs to optimize trials for release. Finally, we summarize knowledge from multiple disciplines for enhancing genetic gains for climate resilience and nutritional traits.

scholarly journals Answer to the comments of K. Dobbin, J. Shih and R. Simon on the paper 'Evaluation of the gene-specific dye-bias in cDNA microarray experiments'

2005 ◽  
Vol 21 (14) ◽  
pp. 3065-3065 ◽  
Author(s):  
M.-L. Martin-Magniette ◽  
J. Aubert ◽  
E. Cabannes ◽  
J.-J. Daudin
Keyword(s):  
Cdna Microarray ◽  
Microarray Experiments

Computational Strategies for Analyzing Data in Gene Expression Microarray Experiments

2003 ◽  
Vol 01 (03) ◽  
pp. 541-586 ◽  
Author(s):  
Tero Aittokallio ◽  
Markus Kurki ◽  
Olli Nevalainen ◽  
Tuomas Nikula ◽  
Anne West ◽  
...  
Keyword(s):  
Gene Expression ◽  
Data Analysis ◽  
Microarray Data ◽  
Microarray Data Analysis ◽  
Biological Research ◽  
Microarray Experiments ◽  
Dna Microarray Data ◽  
Open Questions ◽  
Analysis Technique ◽  
Wide Range

Microarray analysis has become a widely used method for generating gene expression data on a genomic scale. Microarrays have been enthusiastically applied in many fields of biological research, even though several open questions remain about the analysis of such data. A wide range of approaches are available for computational analysis, but no general consensus exists as to standard for microarray data analysis protocol. Consequently, the choice of data analysis technique is a crucial element depending both on the data and on the goals of the experiment. Therefore, basic understanding of bioinformatics is required for optimal experimental design and meaningful interpretation of the results. This review summarizes some of the common themes in DNA microarray data analysis, including data normalization and detection of differential expression. Algorithms are demonstrated by analyzing cDNA microarray data from an experiment monitoring gene expression in T helper cells. Several computational biology strategies, along with their relative merits, are overviewed and potential areas for additional research discussed. The goal of the review is to provide a computational framework for applying and evaluating such bioinformatics strategies. Solid knowledge of microarray informatics contributes to the implementation of more efficient computational protocols for the given data obtained through microarray experiments.

Sign in / Sign up

Export Citation Format

Share Document