Searching and mining visually observed phenotypes of maize mutants

Chi Ren Shyu, Jaturon Harnsomburana, Jason Green, Adrian Sorin Barb, Toni Kazic, Mary Schaeffer, Ed Coe

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

There are thousands of maize mutants, which are invaluable resources for plant research. Geneticists use them to study underlying mechanisms of biochemistry, cell biology, cell development, and cell physiology. To streamline the understanding of such complex processes, researchers need the most current versions of genetic and physical maps, tools with the ability to recognize novel phenotypes or classify known phenotypes, and an intimate knowledge of the biochemical processes generating physiological and phenotypic effects. They must also know how all of these factors change and differ among species, diverse alleles, germplasms, and environmental conditions. While there are robust databases, such as MaizeGDB, for some of these types of raw data, other crucial components are missing. Moreover, the management of visually observed mutant phenotypes is still in its infant stage, let alone the complex query methods that can draw upon high-level and aggregated information to answer the questions of geneticists. In this paper, we address the scientific challenge and propose to develop a robust framework for managing the knowledge of visually observed phenotypes, mining the correlation of visual characteristics with genetic maps, and discovering the knowledge relating to cross-species conservation of visual and genetic patterns. The ultimate goal of this research is to allow a geneticist to submit phenotypic and genomic information on a mutant to a knowledge base and ask, "What genes or environmental factors cause this visually observed phenotype?".

Original languageEnglish (US)
Pages (from-to)1193-1213
Number of pages21
JournalJournal of Bioinformatics and Computational Biology
Volume5
Issue number6
DOIs
StatePublished - Dec 1 2007

Fingerprint

Zea mays
Cytology
Phenotype
Biochemistry
Physiology
Conservation
Genes
Biochemical Phenomena
Cell Physiological Phenomena
Knowledge Bases
Research
Cell Biology
Alleles
Research Personnel
Databases

All Science Journal Classification (ASJC) codes

  • Biochemistry
  • Molecular Biology
  • Computer Science Applications

Cite this

Shyu, Chi Ren ; Harnsomburana, Jaturon ; Green, Jason ; Barb, Adrian Sorin ; Kazic, Toni ; Schaeffer, Mary ; Coe, Ed. / Searching and mining visually observed phenotypes of maize mutants. In: Journal of Bioinformatics and Computational Biology. 2007 ; Vol. 5, No. 6. pp. 1193-1213.
@article{699df3914ff24515bd51712d5ec4397b,
title = "Searching and mining visually observed phenotypes of maize mutants",
abstract = "There are thousands of maize mutants, which are invaluable resources for plant research. Geneticists use them to study underlying mechanisms of biochemistry, cell biology, cell development, and cell physiology. To streamline the understanding of such complex processes, researchers need the most current versions of genetic and physical maps, tools with the ability to recognize novel phenotypes or classify known phenotypes, and an intimate knowledge of the biochemical processes generating physiological and phenotypic effects. They must also know how all of these factors change and differ among species, diverse alleles, germplasms, and environmental conditions. While there are robust databases, such as MaizeGDB, for some of these types of raw data, other crucial components are missing. Moreover, the management of visually observed mutant phenotypes is still in its infant stage, let alone the complex query methods that can draw upon high-level and aggregated information to answer the questions of geneticists. In this paper, we address the scientific challenge and propose to develop a robust framework for managing the knowledge of visually observed phenotypes, mining the correlation of visual characteristics with genetic maps, and discovering the knowledge relating to cross-species conservation of visual and genetic patterns. The ultimate goal of this research is to allow a geneticist to submit phenotypic and genomic information on a mutant to a knowledge base and ask, {"}What genes or environmental factors cause this visually observed phenotype?{"}.",
author = "Shyu, {Chi Ren} and Jaturon Harnsomburana and Jason Green and Barb, {Adrian Sorin} and Toni Kazic and Mary Schaeffer and Ed Coe",
year = "2007",
month = "12",
day = "1",
doi = "10.1142/S0219720007003181",
language = "English (US)",
volume = "5",
pages = "1193--1213",
journal = "Journal of Bioinformatics and Computational Biology",
issn = "0219-7200",
publisher = "World Scientific Publishing Co. Pte Ltd",
number = "6",

}

Shyu, CR, Harnsomburana, J, Green, J, Barb, AS, Kazic, T, Schaeffer, M & Coe, E 2007, 'Searching and mining visually observed phenotypes of maize mutants', Journal of Bioinformatics and Computational Biology, vol. 5, no. 6, pp. 1193-1213. https://doi.org/10.1142/S0219720007003181

Searching and mining visually observed phenotypes of maize mutants. / Shyu, Chi Ren; Harnsomburana, Jaturon; Green, Jason; Barb, Adrian Sorin; Kazic, Toni; Schaeffer, Mary; Coe, Ed.

In: Journal of Bioinformatics and Computational Biology, Vol. 5, No. 6, 01.12.2007, p. 1193-1213.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Searching and mining visually observed phenotypes of maize mutants

AU - Shyu, Chi Ren

AU - Harnsomburana, Jaturon

AU - Green, Jason

AU - Barb, Adrian Sorin

AU - Kazic, Toni

AU - Schaeffer, Mary

AU - Coe, Ed

PY - 2007/12/1

Y1 - 2007/12/1

N2 - There are thousands of maize mutants, which are invaluable resources for plant research. Geneticists use them to study underlying mechanisms of biochemistry, cell biology, cell development, and cell physiology. To streamline the understanding of such complex processes, researchers need the most current versions of genetic and physical maps, tools with the ability to recognize novel phenotypes or classify known phenotypes, and an intimate knowledge of the biochemical processes generating physiological and phenotypic effects. They must also know how all of these factors change and differ among species, diverse alleles, germplasms, and environmental conditions. While there are robust databases, such as MaizeGDB, for some of these types of raw data, other crucial components are missing. Moreover, the management of visually observed mutant phenotypes is still in its infant stage, let alone the complex query methods that can draw upon high-level and aggregated information to answer the questions of geneticists. In this paper, we address the scientific challenge and propose to develop a robust framework for managing the knowledge of visually observed phenotypes, mining the correlation of visual characteristics with genetic maps, and discovering the knowledge relating to cross-species conservation of visual and genetic patterns. The ultimate goal of this research is to allow a geneticist to submit phenotypic and genomic information on a mutant to a knowledge base and ask, "What genes or environmental factors cause this visually observed phenotype?".

AB - There are thousands of maize mutants, which are invaluable resources for plant research. Geneticists use them to study underlying mechanisms of biochemistry, cell biology, cell development, and cell physiology. To streamline the understanding of such complex processes, researchers need the most current versions of genetic and physical maps, tools with the ability to recognize novel phenotypes or classify known phenotypes, and an intimate knowledge of the biochemical processes generating physiological and phenotypic effects. They must also know how all of these factors change and differ among species, diverse alleles, germplasms, and environmental conditions. While there are robust databases, such as MaizeGDB, for some of these types of raw data, other crucial components are missing. Moreover, the management of visually observed mutant phenotypes is still in its infant stage, let alone the complex query methods that can draw upon high-level and aggregated information to answer the questions of geneticists. In this paper, we address the scientific challenge and propose to develop a robust framework for managing the knowledge of visually observed phenotypes, mining the correlation of visual characteristics with genetic maps, and discovering the knowledge relating to cross-species conservation of visual and genetic patterns. The ultimate goal of this research is to allow a geneticist to submit phenotypic and genomic information on a mutant to a knowledge base and ask, "What genes or environmental factors cause this visually observed phenotype?".

UR - http://www.scopus.com/inward/record.url?scp=38049003715&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=38049003715&partnerID=8YFLogxK

U2 - 10.1142/S0219720007003181

DO - 10.1142/S0219720007003181

M3 - Article

C2 - 18172925

AN - SCOPUS:38049003715

VL - 5

SP - 1193

EP - 1213

JO - Journal of Bioinformatics and Computational Biology

JF - Journal of Bioinformatics and Computational Biology

SN - 0219-7200

IS - 6

ER -