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Jenssen, Tor Kristian; Lægreid, Astrid; Komorowski, Jan; Hovig, Eivind

doi:10.1038/88213

Cited by 417 publications

(124 citation statements)

References 22 publications

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“…In addition to these high-throughput techniques, another interesting tactic is to aggregate the results of individual experiments through comprehensive literature searches. Although there clearly are difficulties with differing experimental conditions and varying interpretations, preliminary results have shown this to be an effective method (Jenssen et al 2001;Marcotte et al 2001;Ono et al 2001). …”

Section: (Epstein and Butow 2000)mentioning

confidence: 99%

Interrelating Different Types of Genomic Data, from Proteome to Secretome: 'Oming in on Function

Greenbaum¹,

Luscombe²,

Jansen³

et al. 2001

Genome Res.

158

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With the completion of genome sequences, the current challenge for biology is to determine the functions of all gene products and to understand how they contribute in making an organism viable. For the first time, biological systems can be viewed as being finite, with a limited set of molecular parts. However, the full range of biological processes controlled by these parts is extremely complex. Thus, a key approach in genomic research is to divide the cellular contents into distinct sub-populations, which are often given an "-omic" term. For example, the proteome is the full complement of proteins encoded by the genome, and the secretome is the part of it secreted from the cell. Carrying this further, we suggest the term "translatome" to describe the members of the proteome weighted by their abundance, and the "functome" to describe all the functions carried out by these. Once the individual sub-populations are defined and analyzed, we can then try to reconstruct the full organism by interrelating them, eventually allowing for a full and dynamic view of the cell. All this is, of course, made possible because of the increasing amount of large-scale data resulting from functional genomics experiments. However, there are still many difficulties resulting from the noisiness and complexity of the information. To some degree, these can be overcome through averaging with broad proteomic categories such as those implicit in functional and structural classifications. For illustration, we discuss one example in detail, interrelating transcript and cellular protein populations (transcriptome and translatome). Further information is available at

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Section: (Epstein and Butow 2000)mentioning

confidence: 99%

Interrelating Different Types of Genomic Data, from Proteome to Secretome: 'Oming in on Function

Greenbaum¹,

Luscombe²,

Jansen³

et al. 2001

Genome Res.

158

View full text Add to dashboard Cite

show abstract

“…We used PubGene web tools to identify groups of genes that had co-occurred in the literature and whose expression pattern may shed light on signaling pathways and critical effectors of these genes (Jenssen et al, 2001). Recently, it has been demonstrated that gene pairs with network neighborhoods occurring more frequently together in article abstracts can be functionally related.…”

Section: Discussionmentioning

confidence: 99%

“…To determine the biological relationship between the 109 differentially expressed genes and to identify coexpressed groups of genes associated in the literature, we used PubGene web tools (http://www.pubgene.org/ tools/Network/Subset.cgi) (Jenssen et al, 2001). We identified 40 genes which co-occurred in the literature with at least one other gene of the CT8-ps (Figure 4).…”

Section: Functional Profiling Of Differentially Expressed Genesmentioning

confidence: 99%

A network of clinically and functionally relevant genes is involved in the reversion of the tumorigenic phenotype of MDA-MB-231 breast cancer cells after transfer of human chromosome 8

et al. 2004

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Several investigations have supposed that tumor suppressor genes might be located on human chromosome 8. We used microcell-mediated transfer of chromosome 8 into MDA-MB-231 breast cancer cells and generated independent hybrids with strongly reduced tumorigenic potential. Loss of the transferred chromosome results in reappearance of the malignant phenotype. Expression analysis identified a set of 109 genes (CT8-ps) differentially expressed in microcell hybrids as compared to the tumorigenic MDA-MB-231 and rerevertant cells. Of these, 44.9% are differentially expressed in human breast tumors. The expression pattern of CT8-ps was associated with prognostic factors such as tumor size and grading as well as loss of heterozygosity at the short arm of chromosome 8. We identified CT8-ps networks suggesting that these genes act cooperatively to cause reversion of tumorigenicity in MDA-MB-231 cells. Our findings provide a conceptual basis and experimental system to identify and evaluate genes and gene networks involved in the development and/or progression of breast cancer.

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“…Natural language processing or text mining tools like PubGene [171], iHOP [172], CoPubMapper [173], or the Agilent Literature Search tool [174] can be used to automatically scan the scientific literature in order to identify co-occurrence of genes and proteins. However, manual checking of interactions derived on this level is mandatory; otherwise the number of false positive interactions will be high.…”

Section: Regulatory Context Analysismentioning

confidence: 99%

Transforming omics data into context: Bioinformatics on genomics and proteomics raw data

et al. 2006

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Differential gene expression analysis and proteomics have exerted significant impact on the elucidation of concerted cellular processes, as simultaneous measurement of hundreds to thousands of individual objects on the level of RNA and protein ensembles became technically feasible. The availability of such data sets has promised a profound understanding of phenomena on an aggregate level, expressed as the phenotypic response (observables) of cells, e.g., in the presence of drugs, or characterization of cells and tissue displaying distinct patho-physiological states. However, the step of transforming these data into context, i.e., linking distinct expression or abundance patterns with phenotypic observables -and furthermore enabling a sound biological interpretation on the level of reaction networks and concerted pathways, is still a major shortcoming. This finding is certainly based on the enormous complexity embedded in cellular reaction networks, but a variety of computational approaches have been developed over the last few years to overcome these issues. This review provides an overview on computational procedures for analysis of genomic and proteomic data introducing a sequential analysis workflow: Explorative statistics for deriving a first, from the purely statistical viewpoint, relevant candidate gene/protein list, followed by co-regulation and network analysis to biologically expand this core list toward functional networks and pathways. The review on these procedures is complemented by example applications tailored at identification of disease-associated proteins. Optimization of computational procedures involved, in conjunction with the continuous increase in additional biological data, clearly has the potential of boosting our understanding of processes on a cell-wide level.

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Untitled

Cited by 417 publications

References 22 publications

Interrelating Different Types of Genomic Data, from Proteome to Secretome: 'Oming in on Function

Interrelating Different Types of Genomic Data, from Proteome to Secretome: 'Oming in on Function

A network of clinically and functionally relevant genes is involved in the reversion of the tumorigenic phenotype of MDA-MB-231 breast cancer cells after transfer of human chromosome 8

Transforming omics data into context: Bioinformatics on genomics and proteomics raw data

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