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Article ; Online: DiffCorr: an R package to analyze and visualize differential correlations in biological networks.

Fukushima, Atsushi

2013 Volume 518, Issue 1, Page(s) 209–214

Abstract: Large-scale "omics" data, such as microarrays, can be used to infer underlying cellular regulatory networks in organisms, enabling us to better understand the molecular basis of disease and important traits. Correlation approaches, such as a hierarchical ...

Abstract	Large-scale "omics" data, such as microarrays, can be used to infer underlying cellular regulatory networks in organisms, enabling us to better understand the molecular basis of disease and important traits. Correlation approaches, such as a hierarchical cluster analysis, have been widely used to analyze omics data. In addition to the changes in the mean levels of molecules in the omics data, it is important to know about the changes in the correlation relationship among molecules between 2 experimental conditions. The development of a tool to identify differential correlation patterns in omics data in an efficient and unbiased manner is therefore desirable. We developed the DiffCorr package, a simple method for identifying pattern changes between 2 experimental conditions in correlation networks, which builds on a commonly used association measure, such as Pearson's correlation coefficient. DiffCorr calculates correlation matrices for each dataset, identifies the first principal component-based "eigen-molecules" in the correlation networks, and tests differential correlation between the 2 groups based on Fisher's z-test. We illustrated its utility by demonstrating biologically relevant, differentially correlated molecules in transcriptome coexpression and metabolite-to-metabolite correlation networks. DiffCorr can explore differential correlations between 2 conditions in the context of post-genomics data types, namely transcriptomics and metabolomics. DiffCorr is simple to use in calculating differential correlations and is suitable for the first step towards inferring causal relationships and detecting biomarker candidates. The package can be downloaded from the following website: http://diffcorr.sourceforge.net/.
MeSH term(s)	Arabidopsis/genetics ; Arabidopsis/metabolism ; Cluster Analysis ; Computational Biology/methods ; Flavonoids/metabolism ; Gene Expression Profiling ; Leukemia, Myeloid, Acute/genetics ; Leukemia, Myeloid, Acute/metabolism ; Metabolomics ; Precursor Cell Lymphoblastic Leukemia-Lymphoma/genetics ; Precursor Cell Lymphoblastic Leukemia-Lymphoma/metabolism ; Software
Chemical Substances	Flavonoids
Language	English
Publishing date	2013-04-10
Publishing country	Netherlands
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	391792-7
ISSN	1879-0038 ; 0378-1119
ISSN (online)	1879-0038
ISSN	0378-1119
DOI	10.1016/j.gene.2012.11.028
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Article: DiffCorr: An R package to analyze and visualize differential correlations in biological networks

Fukushima, Atsushi

Gene. 2013 Apr. 10, v. 518, no. 1

2013

Abstract: Large-scale “omics” data, such as microarrays, can be used to infer underlying cellular regulatory networks in organisms, enabling us to better understand the molecular basis of disease and important traits. Correlation approaches, such as a hierarchical ...

Abstract	Large-scale “omics” data, such as microarrays, can be used to infer underlying cellular regulatory networks in organisms, enabling us to better understand the molecular basis of disease and important traits. Correlation approaches, such as a hierarchical cluster analysis, have been widely used to analyze omics data. In addition to the changes in the mean levels of molecules in the omics data, it is important to know about the changes in the correlation relationship among molecules between 2 experimental conditions. The development of a tool to identify differential correlation patterns in omics data in an efficient and unbiased manner is therefore desirable. We developed the DiffCorr package, a simple method for identifying pattern changes between 2 experimental conditions in correlation networks, which builds on a commonly used association measure, such as Pearson's correlation coefficient. DiffCorr calculates correlation matrices for each dataset, identifies the first principal component-based “eigen-molecules” in the correlation networks, and tests differential correlation between the 2 groups based on Fisher's z-test. We illustrated its utility by demonstrating biologically relevant, differentially correlated molecules in transcriptome coexpression and metabolite-to-metabolite correlation networks. DiffCorr can explore differential correlations between 2 conditions in the context of post-genomics data types, namely transcriptomics and metabolomics. DiffCorr is simple to use in calculating differential correlations and is suitable for the first step towards inferring causal relationships and detecting biomarker candidates. The package can be downloaded from the following website: http://diffcorr.sourceforge.net/.
Keywords	Internet ; biomarkers ; cluster analysis ; correlation ; data collection ; metabolomics ; microarray technology ; transcriptome ; transcriptomics
Language	English
Dates of publication	2013-0410
Size	p. 209-214.
Publishing place	Elsevier B.V.
Document type	Article
ZDB-ID	391792-7
ISSN	1879-0038 ; 0378-1119
ISSN (online)	1879-0038
ISSN	0378-1119
DOI	10.1016/j.gene.2012.11.028
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: High Impact Gene Discovery: Simple Strand-Specific mRNA Library Construction and Differential Regulatory Analysis Based on Gene Co-Expression Network.

Ichihashi, Yasunori / Fukushima, Atsushi / Shibata, Arisa / Shirasu, Ken

Methods in molecular biology (Clifton, N.J.)

2018 Volume 1830, Page(s) 163–189

Abstract: Plant transcription factors have potential to behave as hubs in gene regulatory networks through altering the expression of many downstream genes, and identification of such hub transcription factors strongly enhances our understating of biological ... ...

Abstract	Plant transcription factors have potential to behave as hubs in gene regulatory networks through altering the expression of many downstream genes, and identification of such hub transcription factors strongly enhances our understating of biological processes. Transcriptome analysis has become a staple of gene expression analyses. In addition to current advances in Next Generation Sequencing (NGS) technology, various methods for mRNA library construction and downstream data analyses have been enthusiastically developed. Here, we describe Breath Adapter Directional sequencing (BrAD-seq), a simple strand-specific mRNA library preparation for the Illumina platform, allowing easy scaling of transcriptome experiments with low reagent and labor costs. This protocol includes our recent modifications and the detailed practical procedure for BrAD-seq. Because extracting biological meanings from large-scale transcriptome data presents a significant challenge, we also describe a new analytical method that goes beyond differential expression. Differential regulatory analysis (DRA) is based on a gene co-expression network to address which regulatory factor or factors have the ability to predict the abundance of differentially expressed genes between two groups or conditions. This protocol provides a ready-to-use informatics pipeline from raw sequence data to DRA for plant transcriptome datasets.
MeSH term(s)	Computational Biology ; DNA, Complementary/genetics ; DNA, Plant/genetics ; DNA, Plant/isolation & purification ; Gene Expression Regulation, Plant ; Gene Library ; Gene Regulatory Networks ; Genetic Association Studies ; High-Throughput Nucleotide Sequencing ; Molecular Biology/methods ; RNA, Messenger/genetics ; RNA, Messenger/metabolism ; Transcriptome/genetics
Chemical Substances	DNA, Complementary ; DNA, Plant ; RNA, Messenger
Language	English
Publishing date	2018-07-24
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1940-6029
ISSN (online)	1940-6029
DOI	10.1007/978-1-4939-8657-6_11
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Article ; Online: A network perspective on nitrogen metabolism from model to crop plants using integrated 'omics' approaches.

Fukushima, Atsushi / Kusano, Miyako

Journal of experimental botany

2014 Volume 65, Issue 19, Page(s) 5619–5630

Abstract: Nitrogen (N), as an essential element in amino acids, nucleotides, and proteins, is a key factor in plant growth and development. Omics approaches such as metabolomics and transcriptomics have become a promising way to inspect complex network ... ...

Abstract	Nitrogen (N), as an essential element in amino acids, nucleotides, and proteins, is a key factor in plant growth and development. Omics approaches such as metabolomics and transcriptomics have become a promising way to inspect complex network interactions in N metabolism and can be used for monitoring the uptake and regulation, translocation, and remobilization of N. In this review, the authors highlight recent progress in omics approaches, including transcript profiling using microarrays and deep sequencing, and show recent technical developments in metabolite profiling for N studies. Further, network analysis studies including network inference methods with correlations, information-theoretic measures, and a network concept to examine gene expression clusters in relation to N regulatory systems in plants are introduced, and integrating network inference methods and integrated networks using multiple omics data are discussed. Finally, this review summarizes recent omics application examples using metabolite and/or transcript profiling analysis to elucidate the regulation of N metabolism and signalling and the coordination of N and carbon metabolism in model plants (Arabidopsis and rice), crops (tomato, maize, and legumes), and trees (Populus).
MeSH term(s)	Amino Acids/metabolism ; Carbon/metabolism ; Crops, Agricultural/metabolism ; Gene Expression Profiling ; Metabolomics ; Nitrogen/metabolism ; Plants/metabolism
Chemical Substances	Amino Acids ; Carbon (7440-44-0) ; Nitrogen (N762921K75)
Language	English
Publishing date	2014-10
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Review
ZDB-ID	2976-2
ISSN	1460-2431 ; 0022-0957
ISSN (online)	1460-2431
ISSN	0022-0957
DOI	10.1093/jxb/eru322
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Article: Factors associated with fluctuations in repeated measurements of intraocular pressure using the Goldmann applanation tonometer in Japanese patients with primary open-angle glaucoma.

Yaoeda, Kiyoshi / Fukushima, Atsushi / Shirakashi, Motohiro / Miki, Atsushi / Fukuchi, Takeo

Clinical ophthalmology (Auckland, N.Z.)

2018 Volume 12, Page(s) 1473–1478

Abstract: Purpose: The aim of this study was to determine whether fluctuations in intraocular pressure (IOP) occur as a result of the order of IOP measurements or successive IOP measurements in patients with glaucoma and, if so, identify the factors causing these ...

Abstract	Purpose: The aim of this study was to determine whether fluctuations in intraocular pressure (IOP) occur as a result of the order of IOP measurements or successive IOP measurements in patients with glaucoma and, if so, identify the factors causing these fluctuations. Patients and methods: Four hundred twenty-eight eyes of 214 Japanese patients with primary open-angle glaucoma (POAG) were enrolled. Patients treated with beta-blockers or prostaglandin analogs alone were included. Additionally, in the IOP measurements by noncontact tonometer, the same cases of IOP of the right and left eyes prior to this study were included in this study. Four successive IOP measurements were carried out using a Goldmann applanation tonometer as follows: IOP was measured in the first eye (right or left) and then in the fellow eye and IOP was again measured in the first eye and then in the fellow eye. Repeated-measures analysis of variance was used to test the differences in IOP between successive measurements. Generalized linear mixed models were used to test differences in IOP measurements between the right and the left eyes on repeated applanation tonometry and according to the order of measurement. Conditional binomial logistic regression analysis was used to identify factors associated with fluctuating repeated applanation tonometry measurements. A Results: IOP values decreased significantly according to the number of measurements (13.8-13.0; Conclusion: IOP measured in the first eye, either right or left, was higher than that measured in the fellow eye in Japanese patients with POAG. The use of a prostaglandin analog may be associated with fluctuating IOP on repeated applanation tonometry.
Language	English
Publishing date	2018-08-17
Publishing country	New Zealand
Document type	Journal Article
ISSN	1177-5467
ISSN	1177-5467
DOI	10.2147/OPTH.S174277
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Article: A network perspective on nitrogen metabolism from model to crop plants using integrated ‘omics’ approaches

Fukushima, Atsushi / Kusano, Miyako

Journal of experimental botany. 2014 Oct., v. 65, no. 19

2014

Abstract: This review presents recent progress and applications of transcript and metabolite profiling for nitrogen studies. Time-series “omics” datasets are useful for constructing a stoichiometric metabolic model in future studies. ... Nitrogen (N), as an ... ...

Abstract	This review presents recent progress and applications of transcript and metabolite profiling for nitrogen studies. Time-series “omics” datasets are useful for constructing a stoichiometric metabolic model in future studies. Nitrogen (N), as an essential element in amino acids, nucleotides, and proteins, is a key factor in plant growth and development. Omics approaches such as metabolomics and transcriptomics have become a promising way to inspect complex network interactions in N metabolism and can be used for monitoring the uptake and regulation, translocation, and remobilization of N. In this review, the authors highlight recent progress in omics approaches, including transcript profiling using microarrays and deep sequencing, and show recent technical developments in metabolite profiling for N studies. Further, network analysis studies including network inference methods with correlations, information-theoretic measures, and a network concept to examine gene expression clusters in relation to N regulatory systems in plants are introduced, and integrating network inference methods and integrated networks using multiple omics data are discussed. Finally, this review summarizes recent omics application examples using metabolite and/or transcript profiling analysis to elucidate the regulation of N metabolism and signalling and the coordination of N and carbon metabolism in model plants (Arabidopsis and rice), crops (tomato, maize, and legumes), and trees (Populus).
Keywords	crops ; data collection ; metabolites ; nitrogen ; nitrogen metabolism
Language	English
Dates of publication	2014-10
Size	p. 5619-5630.
Publishing place	Oxford University Press [etc.]
Document type	Article
ZDB-ID	2976-2
ISSN	1460-2431 ; 0022-0957
ISSN (online)	1460-2431
ISSN	0022-0957
DOI	10.1093/jxb/eru322
Database	NAL-Catalogue (AGRICOLA)

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Article: Recent progress in the development of metabolome databases for plant systems biology.

Fukushima, Atsushi / Kusano, Miyako

Frontiers in plant science

2013 Volume 4, Page(s) 73

Abstract: Metabolomics has grown greatly as a functional genomics tool, and has become an invaluable diagnostic tool for biochemical phenotyping of biological systems. Over the past decades, a number of databases involving information related to mass spectra, ... ...

Abstract	Metabolomics has grown greatly as a functional genomics tool, and has become an invaluable diagnostic tool for biochemical phenotyping of biological systems. Over the past decades, a number of databases involving information related to mass spectra, compound names and structures, statistical/mathematical models and metabolic pathways, and metabolite profile data have been developed. Such databases complement each other and support efficient growth in this area, although the data resources remain scattered across the World Wide Web. Here, we review available metabolome databases and summarize the present status of development of related tools, particularly focusing on the plant metabolome. Data sharing discussed here will pave way for the robust interpretation of metabolomic data and advances in plant systems biology.
Language	English
Publishing date	2013-04-04
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2613694-6
ISSN	1664-462X
ISSN	1664-462X
DOI	10.3389/fpls.2013.00073
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Article ; Online: Development of RIKEN Plant Metabolome MetaDatabase.

Fukushima, Atsushi / Takahashi, Mikiko / Nagasaki, Hideki / Aono, Yusuke / Kobayashi, Makoto / Kusano, Miyako / Saito, Kazuki / Kobayashi, Norio / Arita, Masanori

Plant & cell physiology

2021 Volume 63, Issue 3, Page(s) 433–440

Abstract: The advancement of metabolomics in terms of techniques for measuring small molecules has enabled the rapid detection and quantification of numerous cellular metabolites. Metabolomic data provide new opportunities to gain a deeper understanding of plant ... ...

Abstract	The advancement of metabolomics in terms of techniques for measuring small molecules has enabled the rapid detection and quantification of numerous cellular metabolites. Metabolomic data provide new opportunities to gain a deeper understanding of plant metabolism that can improve the health of both plants and humans that consume them. Although major public repositories for general metabolomic data have been established, the community still has shortcomings related to data sharing, especially in terms of data reanalysis, reusability and reproducibility. To address these issues, we developed the RIKEN Plant Metabolome MetaDatabase (RIKEN PMM, http://metabobank.riken.jp/pmm/db/plantMetabolomics), which stores mass spectrometry-based (e.g. gas chromatography-MS-based) metabolite profiling data of plants together with their detailed, structured experimental metadata, including sampling and experimental procedures. Our metadata are described as Linked Open Data based on the Resource Description Framework using standardized and controlled vocabularies, such as the Metabolomics Standards Initiative Ontology, which are to be integrated with various life and biomedical science data using the World Wide Web. RIKEN PMM implements intuitive and interactive operations for plant metabolome data, including raw data (netCDF format), mass spectra (NIST MSP format) and metabolite annotations. The feature is suitable not only for biologists who are interested in metabolomic phenotypes, but also for researchers who would like to investigate life science in general through plant metabolomic approaches.
MeSH term(s)	Databases, Factual ; Gas Chromatography-Mass Spectrometry ; Metabolome ; Metabolomics/methods ; Plants/metabolism ; Reproducibility of Results
Language	English
Publishing date	2021-11-30
Publishing country	Japan
Document type	Journal Article
ZDB-ID	208907-5
ISSN	1471-9053 ; 0032-0781
ISSN (online)	1471-9053
ISSN	0032-0781
DOI	10.1093/pcp/pcab173
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Article: Current challenges and future potential of tomato breeding using omics approaches.

Kusano, Miyako / Fukushima, Atsushi

Breeding science

2013 Volume 63, Issue 1, Page(s) 31–41

Abstract: As tomatoes are one of the most important vegetables in the world, improvements in the quality and yield of tomato are strongly required. For this purpose, omics approaches such as metabolomics and transcriptomics are used not only for basic research to ... ...

Abstract	As tomatoes are one of the most important vegetables in the world, improvements in the quality and yield of tomato are strongly required. For this purpose, omics approaches such as metabolomics and transcriptomics are used not only for basic research to understand relationships between important traits and metabolism but also for the development of next generation breeding strategies of tomato plants, because an increase in the knowledge improves the taste and quality, stress resistance and/or potentially health-beneficial metabolites and is connected to improvements in the biochemical composition of tomatoes. Such omics data can be applied to network analyses to potentially reveal unknown cellular regulatory networks in tomato plants. The high-quality tomato genome that was sequenced in 2012 will likely accelerate the application of omics strategies, including next generation sequencing for tomato breeding. In this review, we highlight the current studies of omics network analyses of tomatoes and other plant species, in particular, a gene coexpression network. Key applications of omics approaches are also presented as case examples to improve economically important traits for tomato breeding.
Language	English
Publishing date	2013-03-01
Publishing country	Japan
Document type	Journal Article
ZDB-ID	1190370-3
ISSN	1344-7610 ; 0536-3683
ISSN	1344-7610 ; 0536-3683
DOI	10.1270/jsbbs.63.31
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Article: Comparison of intraocular pressure adjusted by central corneal thickness or corneal biomechanical properties as measured in glaucomatous eyes using noncontact tonometers and the Goldmann applanation tonometer.

Yaoeda, Kiyoshi / Fukushima, Atsushi / Shirakashi, Motohiro / Fukuchi, Takeo

Clinical ophthalmology (Auckland, N.Z.)

2016 Volume 10, Page(s) 829–834

Abstract: Purpose: To investigate the correlation coefficients between intraocular pressure (IOP) before and after adjusting for central corneal thickness (CCT) and corneal biomechanical properties.: Patients and methods: A total of 218 eyes of 218 patients ... ...

Abstract	Purpose: To investigate the correlation coefficients between intraocular pressure (IOP) before and after adjusting for central corneal thickness (CCT) and corneal biomechanical properties. Patients and methods: A total of 218 eyes of 218 patients with primary open-angle glaucoma (mean age =71.5 years; mean spherical equivalent =-0.51 D; mean deviation determined by Humphrey visual field analyzer =-3.22 dB) were included in this study. The tIOP and tIOPCCT, which were adjusted by the CCT (with tIOP meaning IOP not adjusted by CCT, as determined using the CT-1P; and tIOPCCT meaning IOP adjusted by CCT, as determined using the CT-1P), were determined using a noncontact tonometer. The IOPg and IOPCCT, which were adjusted by CCT, and IOPcc adjusted by corneal biomechanical properties were determined using a Reichert 7CR (with IOPg meaning IOP not adjusted by CCT or corneal biomechanical properties, as determined using the Reichert 7CR; IOPCCT meaning IOP adjusted by CCT, as determined using the Reichert 7CR; and IOPcc meaning IOP adjusted by corneal biomechanical properties, as determined using the Reichert 7CR). The GT and GTCCT adjusted by CCT were determined using a Goldmann applanation tonometer (with GT meaning IOP not adjusted by CCT, as determined using the Goldmann applanation tonometer; and with GTCCT meaning IOP adjusted by CCT, as determined using the GAT). Pearson's correlation coefficients among the IOPs were calculated and compared. P-values <0.05 were considered as statistically significant. Results: The tIOP, tIOPCCT, IOPg, IOPCCT, IOPcc, GT, and GTCCT were 14.8±2.5, 15.0±2.4, 13.1±3.2, 13.3±3.1, 13.7±2.9, 13.2±2.4, and 13.4±2.3 mmHg (mean ± standard deviation), respectively. The correlation coefficient between tIOPCCT and tIOP (r=0.979) was significantly higher than that between tIOPCCT and the other IOPs (r=0.668-0.852; P<0.001, respectively). The correlation coefficient between IOPCCT and IOPg (r=0.994) or IOPcc and IOPg (r=0.892) was significantly higher than that between IOPCCT or IOPcc and the other IOPs (r=0.669-0.740; P<0.001, respectively). The correlation coefficient between GTCCT and GT (r=0.989) was significantly higher than that between GTCCT and the other IOPs (r=0.669-0.740; P<0.001, respectively). Conclusion: The IOP adjusted by CCT or corneal biomechanical properties depends on the measurement instrument itself, rather than the adjustment methods, for eyes of patients with primary open-angle glaucoma.
Language	English
Publishing date	2016-05-11
Publishing country	New Zealand
Document type	Journal Article
ISSN	1177-5467
ISSN	1177-5467
DOI	10.2147/OPTH.S106836
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