Is There a Bias in Proteome Research?

Mrowka, Ralf; Patzak, Andreas; Herzel, Hanspeter

doi:10.1101/gr.206701

Cited by 199 publications

(137 citation statements)

References 10 publications

Supporting

Mentioning

131

Contrasting

Unclassified

Order By: Relevance

“…1). Use of each of these sources of information has its own drawbacks (1). For example, many current global assays of mRNA and protein abundance͞state are systematically biased toward more abundant species and measure only the average content of many thousands of cells.…”

mentioning

confidence: 99%

A data integration methodology for systems biology: Experimental verification

Hwang

Smith²,

Leslie³

et al. 2005

Proc. Natl. Acad. Sci. U.S.A.

122

View full text Add to dashboard Cite

The integration of data from multiple global assays is essential to understanding dynamic spatiotemporal interactions within cells. In a companion paper, we reported a data integration methodology, designated Pointillist, that can handle multiple data types from technologies with different noise characteristics. Here we demonstrate its application to the integration of 18 data sets relating to galactose utilization in yeast. These data include global changes in mRNA and protein abundance, genome-wide protein-DNA interaction data, database information, and computational predictions of protein-DNA and protein-protein interactions. We divided the integration task to determine three network components: key system elements (genes and proteins), protein-protein interactions, and protein-DNA interactions. Results indicate that the reconstructed network efficiently focuses on and recapitulates the known biology of galactose utilization. It also provided new insights, some of which were verified experimentally. The methodology described here, addresses a critical need across all domains of molecular and cell biology, to effectively integrate large and disparate data sets.metabolism ͉ yeast ͉ molecular network model ͉ galactose S ystems biology aims to understand the dynamic behavior of molecular networks in the context of the global cell, organ and organism state by exploiting (i) high-throughput interrogation technologies; (ii) increasingly comprehensive databases of biomolecules and their interactions; and (iii) computational predictions of molecular function and interaction ( Fig. 1). Use of each of these sources of information has its own drawbacks (1). For example, many current global assays of mRNA and protein abundance͞state are systematically biased toward more abundant species and measure only the average content of many thousands of cells. Global assays are also inherently noisy and include significant numbers of false positives and false negatives. Databases tend to combine data from different cell types, different strains of an organism, and different experimental conditions. Moreover, well studied molecules and pathways are systematically overrepresented in databases. As a result, integration of database information with a particular set of experimental data can introduce systematic biases into the modelbuilding process. Likewise, computer predictions tend to be more accurate for members of well characterized molecular families. Therefore, there is a pressing need for data integration methodologies that effectively address both random noise and systematic bias in data.In a companion paper (2), we present a data integration methodology and its software implementation to address these challenges. To present the methodology clearly, we used only simulated data in that paper. Here, we present the application of our methodology (named Pointillist) to 18 types of biological data, which we integrate to arrive at a detailed and comprehensive picture of galactose utilization in yeast. The data we integrate include in...

show abstract

mentioning

confidence: 99%

A data integration methodology for systems biology: Experimental verification

Hwang

Smith²,

Leslie³

et al. 2005

Proc. Natl. Acad. Sci. U.S.A.

122

View full text Add to dashboard Cite

show abstract

“…Even when considering multiple data sets generated by a common method, simply taking the intersection of these data sets does not remove random errors completely (6). Third, each data set includes its own systematic biases (4,7). For example, labeling-based mass spectrometry approaches (e.g., isotope-coded affinity tag) tend to favor identification of highly abundant proteins.…”

mentioning

confidence: 99%

A data integration methodology for systems biology

Hwang

Rust²,

Ramsey³

et al. 2005

Proc. Natl. Acad. Sci. U.S.A.

302

246

View full text Add to dashboard Cite

Different experimental technologies measure different aspects of a system and to differing depth and breadth. High-throughput assays have inherently high false-positive and false-negative rates. Moreover, each technology includes systematic biases of a different nature. These differences make network reconstruction from multiple data sets difficult and error-prone. Additionally, because of the rapid rate of progress in biotechnology, there is usually no curated exemplar data set from which one might estimate data integration parameters. To address these concerns, we have developed data integration methods that can handle multiple data sets differing in statistical power, type, size, and network coverage without requiring a curated training data set. Our methodology is general in purpose and may be applied to integrate data from any existing and future technologies. Here we outline our methods and then demonstrate their performance by applying them to simulated data sets. The results show that these methods select truepositive data elements much more accurately than classical approaches. In an accompanying companion paper, we demonstrate the applicability of our approach to biological data. We have integrated our methodology into a free open source software package named POINTILLIST.Fisher's method ͉ mixture distribution models S ystems biology (1, 2) aims to understand cellular behavior in terms of the spatiotemporal interactions among cellular components, such as genes, proteins, metabolites, and organelles. In systems biology, one typically perturbs a system and, with highthroughput measurements to identify all pertinent elements and their interactions, integrates them into a biological network to understand the system's behavior. As such, systems biology is predicated on the integration of experimental data from an ever increasing number of technologies, such as gene expression arrays, proteomics, and chromatin immunoprecipitation on chip assays (3). Integration achieves one of the most important imperatives of systems biology, namely it reduces the dimensionality of global data to deliver useful information about the system of interest.A major challenge in systems biology is that technologies that globally interrogate biological systems have inherently high falsepositive and false-negative rates (4); thus, each data type alone has a limited utility. The integration of data from different sources provides an effective means to deal with this issue by reinforcing bona fide observations and reducing false negatives. Moreover, because different experimental technologies provide different insights into a system, the integration of multiple data types offers the greatest information about a particular cellular process. For example, gene perturbation experiments (e.g., knockouts or RNA interference) reveal relationships between genes that may imply direct physical interactions or indirect logical interactions. In contrast, chromatin immunoprecipitation chip data can reveal direct protein-DNA interactions or cofacto...

show abstract

“…The low level of overlap may be due to differences in techniques, or perhaps not fully probing every single possible interaction, or due to a high false positive rate. One study, for example, has estimated that it is possible that the false positive rates could be as large as 44-91% of yeast twohybrid interaction data sets obtained from the studies by Ito and Uetz (61). Another study estimates up to 50% of all yeast two-hybrid interactions to be spurious (101) There are ways to overcome the problem of false positives, including introducing multiple reporters, carrying out the assay in stringent conditions so that only the strongest interactions are seen, and performing a large number of experimental repetitions.…”

Section: Introductionmentioning

confidence: 99%