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Ronald Jansen

Publications and source records attributed to Ronald Jansen.

14 recordsLinked to original sources
PubMed2006

Signal processing in the TGF-beta superfamily ligand-receptor network.

The TGF-beta pathway plays a central role in tissue homeostasis and morphogenesis. It transduces a variety of extracellular signals into intracellular transcriptional responses that control a plethora of cellular processes, including cell growth, apoptosis, and differentiation. We use computational modeling to show that coupling of signaling with receptor trafficking results in a highly versatile signal-processing unit, able to sense by itself absolute levels of ligand, temporal changes in ligand concentration, and ratios of multiple ligands. This coupling controls whether the response of the receptor module is transient or permanent and whether or not different signaling channels behave independently of each other. Our computational approach unifies seemingly disparate experimental observations and suggests specific changes in receptor trafficking patterns that can lead to phenotypes that favor tumor progression.

Computer Simulation↗
PubMed2004

Information assessment on predicting protein-protein interactions.

BACKGROUND: Identifying protein-protein interactions is fundamental for understanding the molecular machinery of the cell. Proteome-wide studies of protein-protein interactions are of significant value, but the high-throughput experimental technologies suffer from high rates of both false positive and false negative predictions. In addition to high-throughput experimental data, many diverse types of genomic data can help predict protein-protein interactions, such as mRNA expression, localization, essentiality, and functional annotation. Evaluations of the information contributions from different evidences help to establish more parsimonious models with comparable or better prediction accuracy, and to obtain biological insights of the relationships between protein-protein interactions and other genomic information. RESULTS: Our assessment is based on the genomic features used in a Bayesian network approach to predict protein-protein interactions genome-wide in yeast. In the special case, when one does not have any missing information about any of the features, our analysis shows that there is a larger information contribution from the functional-classification than from expression correlations or essentiality. We also show that in this case alternative models, such as logistic regression and random forest, may be more effective than Bayesian networks for predicting interactions. CONCLUSIONS: In the restricted problem posed by the complete-information subset, we identified that the MIPS and Gene Ontology (GO) functional similarity datasets as the dominating information contributors for predicting the protein-protein interactions under the framework proposed by Jansen et al. Random forests based on the MIPS and GO information alone can give highly accurate classifications. In this particular subset of complete information, adding other genomic data does little for improving predictions. We also found that the data discretizations used in the Bayesian methods decreased classification performance.

Artificial Intelligence↗
PubMed2004

Analyzing protein function on a genomic scale: the importance of gold-standard positives and negatives for network prediction.

The concept of 'protein function' is rather 'fuzzy' because it is often based on whimsical terms or contradictory nomenclature. This currently presents a challenge for functional genomics because precise definitions are essential for most computational approaches. Addressing this challenge, the notion of networks between biological entities (including molecular and genetic interaction networks as well as transcriptional regulatory relationships) potentially provides a unifying language suitable for the systematic description of protein function. Predicting the edges in protein networks requires reference sets of examples with known outcome (that is, 'gold standards'). Such reference sets should ideally include positive examples - as is now widely appreciated - but also, equally importantly, negative ones. Moreover, it is necessary to consider the expected relative occurrence of positives and negatives because this affects the misclassification rates of experiments and computational predictions. For instance, a reason why genome-wide, experimental protein-protein interaction networks have high inaccuracies is that the prior probability of finding interactions (positives) rather than non-interacting protein pairs (negatives) in unbiased screens is very small. These problems can be addressed by constructing well-defined sets of non-interacting proteins from subcellular localization data, which allows computing the probability of interactions based on evidence from multiple datasets.

Algorithms↗
PubMed2004

Analyzing cellular biochemistry in terms of molecular networks.

One way to understand cells and circumscribe the function of proteins is through molecular networks. These networks take a variety of forms including webs of protein-protein interactions, regulatory circuits linking transcription factors and targets, and complex pathways of metabolic reactions. We first survey experimental techniques for mapping networks (e.g., the yeast two-hybrid screens). We then turn our attention to computational approaches for predicting networks from individual protein features, such as correlating gene expression levels or analyzing sequence coevolution. All the experimental techniques and individual predictions suffer from noise and systematic biases. These problems can be overcome to some degree through statistical integration of different experimental datasets and predictive features (e.g., within a Bayesian formalism). Next, we discuss approaches for characterizing the topology of networks, such as finding hubs and analyzing subnetworks in terms of common motifs. Finally, we close with perspectives on how network analysis represents a preliminary step toward a systems approach for modeling cells.

Biochemical Phenomena↗
PubMed2003

A Bayesian networks approach for predicting protein-protein interactions from genomic data.

We have developed an approach using Bayesian networks to predict protein-protein interactions genome-wide in yeast. Our method naturally weights and combines into reliable predictions genomic features only weakly associated with interaction (e.g., messenger RNAcoexpression, coessentiality, and colocalization). In addition to de novo predictions, it can integrate often noisy, experimental interaction data sets. We observe that at given levels of sensitivity, our predictions are more accurate than the existing high-throughput experimental data sets. We validate our predictions with TAP (tandem affinity purification) tagging experiments. Our analysis, which gives a comprehensive view of yeast interactions, is available at genecensus.org/intint.

Bayes Theorem↗
PubMed2003

Revisiting the codon adaptation index from a whole-genome perspective: analyzing the relationship between gene expression and codon occurrence in yeast using a variety of models.

Highly expressed genes in many bacteria and small eukaryotes often have a strong compositional bias, in terms of codon usage. Two widely used numerical indices, the codon adaptation index (CAI) and the codon usage, use this bias to predict the expression level of genes. When these indices were first introduced, they were based on fairly simple assumptions about which genes are most highly expressed: the CAI was originally based on the codon composition of a set of only 24 highly expressed genes, and the codon usage on assumptions about which functional classes of genes are highly expressed in fast-growing bacteria. Given the recent advent of genome-wide expression data, we should be able to improve on these assumptions. Here, we measure, in yeast, the degree to which consideration of the current genome-wide expression data sets improves the performance of both numerical indices. Indeed, we find that by changing the parameterization of each model its correlation with actual expression levels can be somewhat improved, although both indices are fairly insensitive to the exact way they are parameterized. This insensitivity indicates a consistent codon bias amongst highly expressed genes. We also attempt direct linear regression of codon composition against genome-wide expression levels (and protein abundance data). This has some similarity with the CAI formalism and yields an alternative model for the prediction of expression levels based on the coding sequences of genes. More information is available at http://bioinfo.mbb.yale.edu/expression/codons.

Codon↗
PubMed2002

Subcellular localization of the yeast proteome.

Protein localization data are a valuable information resource helpful in elucidating eukaryotic protein function. Here, we report the first proteome-scale analysis of protein localization within any eukaryote. Using directed topoisomerase I-mediated cloning strategies and genome-wide transposon mutagenesis, we have epitope-tagged 60% of the Saccharomyces cerevisiae proteome. By high-throughput immunolocalization of tagged gene products, we have determined the subcellular localization of 2744 yeast proteins. Extrapolating these data through a computational algorithm employing Bayesian formalism, we define the yeast localizome (the subcellular distribution of all 6100 yeast proteins). We estimate the yeast proteome to encompass approximately 5100 soluble proteins and >1000 transmembrane proteins. Our results indicate that 47% of yeast proteins are cytoplasmic, 13% mitochondrial, 13% exocytic (including proteins of the endoplasmic reticulum and secretory vesicles), and 27% nuclear/nucleolar. A subset of nuclear proteins was further analyzed by immunolocalization using surface-spread preparations of meiotic chromosomes. Of these proteins, 38% were found associated with chromosomal DNA. As determined from phenotypic analyses of nuclear proteins, 34% are essential for spore viability--a percentage nearly twice as great as that observed for the proteome as a whole. In total, this study presents experimentally derived localization data for 955 proteins of previously unknown function: nearly half of all functionally uncharacterized proteins in yeast. To facilitate access to these data, we provide a searchable database featuring 2900 fluorescent micrographs at http://ygac.med.yale.edu.

Algorithms↗
PubMed2002

Bridging structural biology and genomics: assessing protein interaction data with known complexes.

Currently, there is a major effort to map protein-protein interactions on a genome-wide scale. The utility of the resulting interaction networks will depend on the reliability of the experimental methods and the coverage of the approaches. Known macromolecular complexes provide a defined and objective set of protein interactions with which to compare biochemical and genetic data for validation. Here, we show that a significant fraction of the protein-protein interactions in genome-wide datasets, as well as many of the individual interactions reported in the literature, are inconsistent with the known 3D structures of three recent complexes (RNA polymerase II, Arp2/3 and the proteasome). Furthermore, comparison among genome-wide datasets, and between them and a larger (but less well resolved) group of 174 complexes, also shows marked inconsistencies. Finally, individual interaction datasets, being inherently noisy, are best used when integrated together, and we show how simple Bayesian approaches can combine them, significantly decreasing error rate.

Actin-Related Protein 3↗
PubMed2002

Integration of genomic datasets to predict protein complexes in yeast.

The ultimate goal of functional genomics is to define the function of all the genes in the genome of an organism. A large body of information of the biological roles of genes has been accumulated and aggregated in the past decades of research, both from traditional experiments detailing the role of individual genes and proteins, and from newer experimental strategies that aim to characterize gene function on a genomic scale. It is clear that the goal of functional genomics can only be achieved by integrating information and data sources from the variety of these different experiments. Integration of different data is thus an important challenge for bioinformatics. The integration of different data sources often helps to uncover non-obvious relationships between genes, but there are also two further benefits. First, it is likely that whenever information from multiple independent sources agrees, it should be more valid and reliable. Secondly, by looking at the union of multiple sources, one can cover larger parts of the genome. This is obvious for integrating results from multiple single gene or protein experiments, but also necessary for many of the results from genome-wide experiments since they are often confined to certain (although sizable) subsets of the genome. In this paper, we explore an example of such a data integration procedure. We focus on the prediction of membership in protein complexes for individual genes. For this, we recruit six different data sources that include expression profiles, interaction data, essentiality and localization information. Each of these data sources individually contains some weakly predictive information with respect to protein complexes, but we show how this prediction can be improved by combining all of them. Supplementary information is available at http:// bioinfo.mbb.yale.edu/integrate/interactions/.

Cell Cycle↗
PubMed2002

Analysis of mRNA expression and protein abundance data: an approach for the comparison of the enrichment of features in the cellular population of proteins and transcripts.

MOTIVATION: Protein abundance is related to mRNA expression through many different cellular processes. Up to now, there have been conflicting results on how correlated the levels of these two quantities are. Given that expression and abundance data are significantly more complex and noisy than the underlying genomic sequence information, it is reasonable to simplify and average them in terms of broad proteomic categories and features (e.g. functions or secondary structures), for understanding their relationship. Furthermore, it will be essential to integrate, within a common framework, the results of many varied experiments by different investigators. This will allow one to survey the characteristics of highly expressed genes and proteins. RESULTS: To this end, we outline a formalism for merging and scaling many different gene expression and protein abundance data sets into a comprehensive reference set, and we develop an approach for analyzing this in terms of broad categories, such as composition, function, structure and localization. As the various experiments are not always done using the same set of genes, sampling bias becomes a central issue, and our formalism is designed to explicitly show this and correct for it. We apply our formalism to the currently available gene expression and protein abundance data for yeast. Overall, we found substantial agreement between gene expression and protein abundance, in terms of the enrichment of structural and functional categories. This agreement, which was considerably greater than the simple correlation between these quantities for individual genes, reflects the way broad categories collect many individual measurements into simple, robust averages. In particular, we found that in comparison to the population of genes in the yeast genome, the cellular populations of transcripts and proteins (weighted by their respective abundances, the transcriptome and what we dub the translatome) were both enriched in: (i) the small amino acids Val, Gly, and Ala; (ii) low molecular weight proteins; (iii) helices and sheets relative to coils; (iv) cytoplasmic proteins relative to nuclear ones; and (v) proteins involved in 'protein synthesis,' 'cell structure,' and 'energy production.' SUPPLEMENTARY INFORMATION: http://genecensus.org/expression/translatome

Algorithms↗
PubMed2002

Systematic learning of gene functional classes from DNA array expression data by using multilayer perceptrons.

Recent advances in microarray technology have opened new ways for functional annotation of previously uncharacterised genes on a genomic scale. This has been demonstrated by unsupervised clustering of co-expressed genes and, more importantly, by supervised learning algorithms. Using prior knowledge, these algorithms can assign functional annotations based on more complex expression signatures found in existing functional classes. Previously, support vector machines (SVMs) and other machine-learning methods have been applied to a limited number of functional classes for this purpose. Here we present, for the first time, the comprehensive application of supervised neural networks (SNNs) for functional annotation. Our study is novel in that we report systematic results for ~100 classes in the Munich Information Center for Protein Sequences (MIPS) functional catalog. We found that only ~10% of these are learnable (based on the rate of false negatives). A closer analysis reveals that false positives (and negatives) in a machine-learning context are not necessarily "false" in a biological sense. We show that the high degree of interconnections among functional classes confounds the signatures that ought to be learned for a unique class. We term this the "Borges effect" and introduce two new numerical indices for its quantification. Our analysis indicates that classification systems with a lower Borges effect are better suitable for machine learning. Furthermore, we introduce a learning procedure for combining false positives with the original class. We show that in a few iterations this process converges to a gene set that is learnable with considerably low rates of false positives and negatives and contains genes that are biologically related to the original class, allowing for a coarse reconstruction of the interactions between associated biological pathways. We exemplify this methodology using the well-studied tricarboxylic acid cycle.

Algorithms↗
PubMed2002

Relating whole-genome expression data with protein-protein interactions.

We investigate the relationship of protein-protein interactions with mRNA expression levels, by integrating a variety of data sources for yeast. We focus on known protein complexes that have clearly defined interactions between their subunits. We find that subunits of the same protein complex show significant coexpression, both in terms of similarities of absolute mRNA levels and expression profiles, e.g., we can often see subunits of a complex having correlated patterns of expression over a time course. We classify the yeast protein complexes as either permanent or transient, with permanent ones being maintained through most cellular conditions. We find that, generally, permanent complexes, such as the ribosome and proteasome, have a particularly strong relationship with expression, while transient ones do not. However, we note that several transient complexes, such as the RNA polymerase II holoenzyme and the replication complex, can be subdivided into smaller permanent ones, which do have a strong relationship to gene expression. We also investigated the interactions in aggregated, genome-wide data sets, such as the comprehensive yeast two-hybrid experiments, and found them to have only a weak relationship with gene expression, similar to that of transient complexes. (Further details on genecensus.org/expression/interactions and bioinfo.mbb.yale.edu/expression/interactions.)

Gene Expression Profiling↗