Large-scale regulatory network analysis from microarray data : Application to seed biology
Basu, Anamika and Sarkar, Anasua LU
(2014)
p.60-85
- Abstract
The inference of gene networks from gene expression data is known as "reverse engineering." Elucidating genetic networks from high-throughput microarray data in seed maturation and embryo formation in plants is crucial for storage and production of cereals for human beings. Delayed seed maturation and abnormal embryo formation during storage of cereal crops degrade the quality and quantity of food grains. In this chapter, the authors perform comparative gene analysis of results of different microarray experiments in different stages of embryogenesis in Arabidopsis thaliana, and to reconstruct Gene Networks (GNs) related to various stages of plant seed maturation using reverse engineering technique. They also biologically validate the... (More)
The inference of gene networks from gene expression data is known as "reverse engineering." Elucidating genetic networks from high-throughput microarray data in seed maturation and embryo formation in plants is crucial for storage and production of cereals for human beings. Delayed seed maturation and abnormal embryo formation during storage of cereal crops degrade the quality and quantity of food grains. In this chapter, the authors perform comparative gene analysis of results of different microarray experiments in different stages of embryogenesis in Arabidopsis thaliana, and to reconstruct Gene Networks (GNs) related to various stages of plant seed maturation using reverse engineering technique. They also biologically validate the results for developing embryogenesis network on Arabidopsis thaliana with GO and pathway enrichment analysis. The biological analysis shows that different genes are over-expressed during embryogenesis related with several KEGG metabolic pathways. The large-scale microarray datasets of Arabidopsis thaliana for these genes involved in embryogenesis have been analysed in seed biology. The chapter also reveals new insight into the gene functional modules obtained from the Arabidopsis gene correlation networks in this dataset.
(Less)
- author
- Basu, Anamika
and Sarkar, Anasua
LU
- publishing date
- 2014-10-31
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- host publication
- Big Data Analytics in Bioinformatics and Healthcare
- pages
- 26 pages
- publisher
- IGI Global
- external identifiers
-
- scopus:84946398681
- ISBN
- 1466666110
- 9781466666115
- 9781466666122
- DOI
- 10.4018/978-1-4666-6611-5.ch004
- language
- English
- LU publication?
- no
- id
- e8631b36-50ac-4e90-b313-a98e47afd2bd
- date added to LUP
- 2018-10-09 09:49:59
- date last changed
- 2024-01-15 03:20:01
@inbook{e8631b36-50ac-4e90-b313-a98e47afd2bd,
abstract = {{<p>The inference of gene networks from gene expression data is known as "reverse engineering." Elucidating genetic networks from high-throughput microarray data in seed maturation and embryo formation in plants is crucial for storage and production of cereals for human beings. Delayed seed maturation and abnormal embryo formation during storage of cereal crops degrade the quality and quantity of food grains. In this chapter, the authors perform comparative gene analysis of results of different microarray experiments in different stages of embryogenesis in Arabidopsis thaliana, and to reconstruct Gene Networks (GNs) related to various stages of plant seed maturation using reverse engineering technique. They also biologically validate the results for developing embryogenesis network on Arabidopsis thaliana with GO and pathway enrichment analysis. The biological analysis shows that different genes are over-expressed during embryogenesis related with several KEGG metabolic pathways. The large-scale microarray datasets of Arabidopsis thaliana for these genes involved in embryogenesis have been analysed in seed biology. The chapter also reveals new insight into the gene functional modules obtained from the Arabidopsis gene correlation networks in this dataset.</p>}},
author = {{Basu, Anamika and Sarkar, Anasua}},
booktitle = {{Big Data Analytics in Bioinformatics and Healthcare}},
isbn = {{1466666110}},
language = {{eng}},
month = {{10}},
pages = {{60--85}},
publisher = {{IGI Global}},
title = {{Large-scale regulatory network analysis from microarray data : Application to seed biology}},
url = {{http://dx.doi.org/10.4018/978-1-4666-6611-5.ch004}},
doi = {{10.4018/978-1-4666-6611-5.ch004}},
year = {{2014}},
}