2004
DOI: 10.1002/0471250953.bi0706s05
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Integrating Whole‐Genome Expression Results into Metabolic Networks with Pathway Processor

Abstract: Genes never act alone in a biological system, but participate in a cascade of networks. As a result, analyzing microarray data from a pathway perspective leads to a new level of understanding the system. The authors' group has recently developed Pathway Processor (http://cgr.harvard.edu/cavalieri/pp.html), an automatic statistical method to determine which pathways are most affected by transcriptional changes and to map expression data from multiple whole‐genome expression experiments on metabolic pathways. Th… Show more

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Cited by 2 publications
(2 citation statements)
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“…nitrogen fixers) can be of pivotal importance (Dinsdale et al, 2008). A large-scale genomic data analysis may be combined with gene expression analysis (transcriptome) to further identify the genes associated with causal interactions between genes and traits and generate co-expression networks using a collection of DNA sequences (Cavalieri & Grosu, 2004;Ferrara et al, 2008) or for taxon-specific probing (Urisman et al, 2005). In this context, it should be important to find the microbial and enzymatic complements in different niches and how they determine community functioning.…”
Section: Interplay Of Microbial Complexity and Metagenomicsmentioning
confidence: 99%
See 1 more Smart Citation
“…nitrogen fixers) can be of pivotal importance (Dinsdale et al, 2008). A large-scale genomic data analysis may be combined with gene expression analysis (transcriptome) to further identify the genes associated with causal interactions between genes and traits and generate co-expression networks using a collection of DNA sequences (Cavalieri & Grosu, 2004;Ferrara et al, 2008) or for taxon-specific probing (Urisman et al, 2005). In this context, it should be important to find the microbial and enzymatic complements in different niches and how they determine community functioning.…”
Section: Interplay Of Microbial Complexity and Metagenomicsmentioning
confidence: 99%
“…The main task of the sequencing-based metagenomics is to reconstruct the metabolism of the organisms making up the community, and to predict their functional roles in the ecosystem. A large-scale genomic data analysis may be combined with gene expression analysis (transcriptome) to further identify the genes associated with causal interactions between genes and traits and generate co-expression networks using a collection of DNA sequences (Cavalieri & Grosu, 2004;Ferrara et al, 2008) or for taxon-specific probing (Urisman et al, 2005). However, the sequence variability of genes in organisms even belonging to the same species and the incomplete genomic data due to the complexity of communities, limit application of this method.…”
Section: Interplay Of Microbial Complexity and Metagenomicsmentioning
confidence: 99%