Global approaches to quantitative analysis of gene-expression patterns observed by use of two-dimensional gel electrophoresis.

Anderson, N. Leigh; Hofmann, Jean-Paul; Gemmell, Anne; Taylor, John A.

doi:10.1093/clinchem/30.12.2031

Cited by 73 publications

(19 citation statements)

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“…This statistical analysis has been used for a number of years to analyse 2‐D gels to locate differential patterns of protein synthesis. Both Tarroux 43 and Anderson et al 44 have used cluster analysis to analyse protein synthesis during insect development and cellular differentiation, respectively. More recently Prahlad and Li 45 have used PCA to examine protein synthesis in iron‐starved Mycobacterium tuberculosis .…”

Section: Typing Bacteria Using 1‐d Sds‐pagementioning

confidence: 99%

Proteomics in the study of the molecular taxonomy and epidemiology of bacterial pathogens

Cash

2009

Electrophoresis

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The ability to discriminate bacterial isolates is important for a number of areas of research in Medical Microbiology, particularly in defining bacterial taxonomy and monitoring transmission in epidemiological investigations. Molecular techniques capable of typing bacteria at the level of the genome and proteome are now widely used for these investigations. This review considers two electrophoretic methods for typing bacteria on the basis of their proteomes, namely 1-D SDS-PAGE and 2-DE. The application of these two techniques for bacterial typing is described with reference to two publications that appeared in Electrophoresis [Costa, Electrophoresis 1990, 11, 382-391 and Cash et al., Electrophoresis 1997, 18, 1472-1482]. Even though these methods have been used for nearly 20 years to differentiate bacterial isolates they remain key technologies in proteome-based typing methods. The developments that have arisen from the two key papers are described in order to highlight the advantages and disadvantages in typing bacteria at the level of their proteomes. Some of the difficulties associated with electrophoretic typing methods can be overcome by using non-gel proteomic methods based on MS to provide improved sensitivity and specificity. The application of proteomic methods to investigate bacterial taxonomy, epidemiology and pathogenesis in general has significant potential in furthering our understanding of infectious diseases.

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Section: Typing Bacteria Using 1‐d Sds‐pagementioning

confidence: 99%

Proteomics in the study of the molecular taxonomy and epidemiology of bacterial pathogens

Cash

2009

Electrophoresis

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“…It is worth noting that the computational approaches reviewed here can be applied at the protein level. In the search for correlations, cDNA microarrays and gene expression levels are simply replaced by two-dimensional electrophoresis and protein spot intensities (60,104). In the few comparative studies that have been performed, important discrepancies have been noted between expression measurements made at the transcriptome versus the proteome level (104,105).…”

Section: Coordinated Gene Expression Analysis In Functional Genomicsmentioning

confidence: 99%

“…In this article, I review the concepts and methodologies involved in the interpretation of gene expression profiling experiments. Following the pioneering work of Anderson et al (60), several recent articles have discussed the bioinformatics of large-scale expression monitoring, emphasizing computational (61)(62)(63)(64)(65)(66)(67)(68)(69) or data management (70-77) aspects.…”

Section: Introductionmentioning

confidence: 99%

Computational methods for theidentification of differential and coordinated gene expression

Claverie

1999

Human Molecular Genetics

277

137

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With the first complete 'draft' of the human genome sequence expected for Spring 2000, the three basic challenges for today's bioinformatics are more than ever: (i) finding the genes; (ii) locating their coding regions; and (iii) predicting their functions. However, our capacity for interpreting vertebrate genomic and transcript (cDNA) sequences using experimental or computational means very much lags behind our raw sequencing power. If the performances of current programs in identifying internal coding exons are good, the precise 5'-->3' delineation of transcription units (and promoters) still requires additional experiments. Similarly, functional predictions made with reference to previously characterized homologues are leaving >50% of human genes unannotated or classified in uninformative categories ('kinase', 'ATP-binding', etc.). In the context of functional genomics, large-scale gene expression studies using massive cDNA tag sequencing, two-dimensional gel proteome analysis or microarray technologies are the only approaches providing genome-scale experimental information at a pace consistent with the progress of sequencing. Given the difficulty and cost of characterizing genes one by one, academic and industrial researchers are increasingly relying on those methods to prioritize their studies and choose their targets. The study of expression patterns can also provide some insight into the function, reveal regulatory pathways, indicate side effects of drugs or serve as a diagnostic tool. In this article, I review the theoretical and computational approaches used to: (i) identify genes differentially expressed (across cell types, developmental stages, pathological conditions, etc.); (ii) identify genes expressed in a coordinated manner across a set of conditions; and (iii) delineate clusters of genes sharing coherent expression features, eventually defining global biological pathways.

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“…A B Coomassie blue and image processing by the Kepler program package. Figure 2 summarizes that project, which established an early link between the enterprise of proteome research [29,30] and the molecular pharmacology of cancer. The database generated consisted of 1,014 indexed and quantitated protein spots,of which 151 were quality-controlled over all 60 cell lines and incorporated into a primary data set for analysis [28].…”

Section: Mcf-7 Master Gel Computer Imagementioning

confidence: 99%

Searching for Pharmacogenomic Markers: The Synergy between Omic and Hypothesis‐Driven Research

Weinstein

2001

Disease Markers

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With 35,000 genes and hundreds of thousands of protein states to identify, correlate, and understand, it no longer suffices to rely on studies of one gene, gene product, or process at a time. We have entered the “omic” era in biology. But large-scale omic studies of cellular molecules in aggregate rarely can answer interesting questions without the assistance of information from traditional hypothesis-driven research. The two types of science are synergistic. A case in point is the set of pharmacogenomic studies that we and our collaborators have done with the 60 human cancer cell lines of the National Cancer Institute’s drug discovery program. Those cells (the NCI-60) have been characterized pharmacologically with respect to their sensitivity to > 70,000 chemical compounds. We are further characterizing them at the DNA, RNA, protein, and functional levels. Our major aim is to identify pharmacogenomic markers that can aid in drug discovery and design, as well as in individualization of cancer therapy. The bioinformatic and chemoinformatic challenges of this study have demanded novel methods for analysis and visualization of high-dimensional data. Included are the color-coded “clustered image map” and also the MedMiner program package, which captures and organizes the biomedical literature on gene-gene and gene-drug relationships. Microarray transcript expression studies of the 60 cell lines reveal, for example, a gene-drug correlation with potential clinical implications – that between the asparagine synthetase gene and the enzyme-drug L-asparaginase in ovarian cancer cells.

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Global approaches to quantitative analysis of gene-expression patterns observed by use of two-dimensional gel electrophoresis.

Cited by 73 publications

References 0 publications

Proteomics in the study of the molecular taxonomy and epidemiology of bacterial pathogens

Proteomics in the study of the molecular taxonomy and epidemiology of bacterial pathogens

Computational methods for theidentification of differential and coordinated gene expression

Searching for Pharmacogenomic Markers: The Synergy between Omic and Hypothesis‐Driven Research

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