CiteULike: grahamc's library [372 articles]

Discovery of a Cytokine and Its Receptor by Functional Screening of the Extracellular Proteome

Haishan Lin — 2008-05-13T01:59:36-00:00

Science, Vol. 320, No. 5877. (9 May 2008), pp. 807-811.

To understand the system of secreted proteins and receptors involved in cell-cell signaling, we produced a comprehensive set of recombinant secreted proteins and the extracellular domains of transmembrane proteins, which constitute most of the protein components of the extracellular space. Each protein was tested in a suite of assays that measured metabolic, growth, or transcriptional responses in diverse cell types. The pattern of responses across assays was analyzed for the degree of functional selectivity of each protein. One of the highly selective proteins was a previously undescribed ligand, designated interleukin-34 (IL-34), which stimulates monocyte viability but does not affect responses in a wide spectrum of other assays. In a separate functional screen, we used a collection of extracellular domains of transmembrane proteins to discover the receptor for IL-34, which was a known cytokine receptor, colony-stimulating factor 1 (also called macrophage colony-stimulating factor) receptor. This systematic approach is thus useful for discovering new ligands and receptors and assessing the functional selectivity of extracellular regulatory proteins. 10.1126/science.1154370

An in Vivo Map of the Yeast Protein Interactome

Kirill Tarassov — 2008-05-12T00:30:55-00:00

Science (8 May 2008), 1153878.

Protein interactions regulate the systems-level behavior of cells, thus, deciphering the structure and dynamics of protein interaction networks in their cellular context is a central goal in biology. We have performed a genome-wide in vivo screen for protein-protein interactions (PPIs) in Saccharomyces cerevisiae by means of a protein-fragment complementation assay (PCA). We identified 2,770 interactions among 1,124 endogenously expressed proteins. Comparison with previous studies confirms known interactions, but most are new, revealing a previously unknown sub-space of the yeast protein interactome. PCA detects structural and topological relationships between proteins, providing an 8-nanometer resolution map of dynamically interacting complexes in vivo and extended networks that provide insights into fundamental cellular processes, including cell polarization and autophagy, pathways that are evolutionarily conserved and central to both development and human health. 10.1126/science.1153878

Wound repair and regeneration

Geoffrey Gurtner — 2008-05-15T23:29:38-00:00

Nature, Vol. 453, No. 7193. (14 May 2008), pp. 314-321.

A map of human protein interactions derived from co-expression of human mRNAs and their orthologs

Arun Ramani — 2008-04-15T19:18:30-00:00

Mol Syst Biol, Vol. 4 (15 April 2008)

Gene expression profiling of human prostate cancer stem cells reveals a pro-inflammatory phenotype and the importance of extracellular matrix interactions

Richard Birnie — 2008-05-20T14:26:53-00:00

Genome Biology, Vol. 9 (20 May 2008), R83.

Mapping proteins to disease terminologies: from UniProt to MeSH.

A Mottaz — 2008-05-11T03:23:53-00:00

BMC bioinformatics, Vol. 9 Suppl 5 (2008)

BACKGROUND: Although the UniProt KnowledgeBase is not a medical-oriented database, it contains information on more than 2,000 human proteins involved in pathologies. However, these annotations are not standardized, which impairs the interoperability between biological and clinical resources. In order to make these data easily accessible to clinical researchers, we have developed a procedure to link diseases described in the UniProtKB/Swiss-Prot entries to the MeSH disease terminology. RESULTS: We mapped disease names extracted either from the UniProtKB/Swiss-Prot entry comment lines or from the corresponding OMIM entry to the MeSH. Different methods were assessed on a benchmark set of 200 disease names manually mapped to MeSH terms. The performance of the retained procedure in term of precision and recall was 86% and 64% respectively. Using the same procedure, more than 3,000 disease names in Swiss-Prot were mapped to MeSH with comparable efficiency. CONCLUSIONS: This study is a first attempt to link proteins in UniProtKB to the medical resources. The indexing we provided will help clinicians and researchers navigate from diseases to genes and from genes to diseases in an efficient way. The mapping is available at: http://research.isb-sib.ch/unimed.

Gene Ontology annotations: what they mean and where they come from.

DP Hill — 2008-05-11T16:21:46-00:00

BMC bioinformatics, Vol. 9 Suppl 5 (2008)

To address the challenges of information integration and retrieval, the computational genomics community increasingly has come to rely on the methodology of creating annotations of scientific literature using terms from controlled structured vocabularies such as the Gene Ontology (GO). Here we address the question of what such annotations signify and of how they are created by working biologists. Our goal is to promote a better understanding of how the results of experiments are captured in annotations, in the hope that this will lead both to better representations of biological reality through annotation and ontology development and to more informed use of GO resources by experimental scientists.

Cytoscape ESP: simple search of complex biological networks.

Maital Ashkenazi — 2008-05-04T12:03:46-00:00

Bioinformatics (Oxford, England) (28 April 2008)

SUMMARY: Cytoscape ESP enables searching complex biological networks on multiple attribute fields using logical operators and wildcards. Queries use an intuitive syntax and simple search line interface. ESP is implemented as a Cytoscape plugin and complements existing search functions in the Cytoscape network visualization and analysis software, allowing users to easily identify nodes, edges and subgraphs of interest, even for very large networks. AVAILABILITY: http://conklinwolf.ucsf.edu/genmappwiki/Google_Summer_of_Code_2007/Maital CONTACT: ashkenaz@agri.huji.ac.il.

UniProtJAPI: A remote API for accessing UniProt data.

Samuel Patient — 2008-04-09T16:06:28-00:00

Bioinformatics (Oxford, England) (4 April 2008)

SUMMARY: Programmatic access to the UniProt Knowledgebase is essential for many bioinformatics applications dealing with protein data. We have created a Java library named UniProtJAPI, which facilitates the integration of UniProt data into Java-based software applications. The library supports queries and similarity searches that return UniProt Knowledgebase entries in the form of Java objects. These objects contain functional annotations or sequence information associated with a UniProt entry. Here, we briefly describe the UniProtJAPI and demonstrate its usage. AVAILABILITY: http://www.ebi.ac.uk/uniprot/remotingAPI CONTACT: spatient@ebi.ac.uk, apweiler@ebi.ac.uk.

Analyzing Protein Interaction Networks Using Structural Information

Christina Kiel — 2008-03-17T11:09:05-00:00

Annual Review of Biochemistry, Vol. 77, No. 1. (2008)

Determining protein interaction networks and predicting network changes in time and space are crucial to understanding and modeling a biological system. In the past few years, the combination of experimental and computational tools has allowed great progress toward reaching this goal. Experimental methods include the large-scale determination of protein interactions using two-hybrid or pull-down analysis as well as proteomics. The latter one is especially valuable when changes in protein concentrations over time are recorded. Computational tools include methods to predict and validate protein interactions on the basis of structural information and bioinformatics tools that analyze and integrate data for the same purpose. In this review, we focus on the use of structural information in combination with computational tools to predict new protein interactions, to determine which interactions are compatible with each other, to obtain some functional insight into single and multiple mutations, and to estimate equilibrium and kinetic parameters. Finally, we discuss the importance of establishing criteria to biologically validate protein interactions. Expected final online publication date for the Annual Review of Biochemistry Volume 77 is June 02, 2008. Please see http://www.annualreviews.org/catalog/pubdates.aspx for revised estimates.

Protein complexes and functional modules in molecular networks.

V Spirin — 2004-11-22T00:17:30-00:00

Proc Natl Acad Sci U S A, Vol. 100, No. 21. (14 October 2003), pp. 12123-12128.

Proteins, nucleic acids, and small molecules form a dense network of molecular interactions in a cell. Molecules are nodes of this network, and the interactions between them are edges. The architecture of molecular networks can reveal important principles of cellular organization and function, similarly to the way that protein structure tells us about the function and organization of a protein. Computational analysis of molecular networks has been primarily concerned with node degree [Wagner, A. & Fell, D. A. (2001) Proc. R. Soc. London Ser. B 268, 1803-1810; Jeong, H., Tombor, B., Albert, R., Oltvai, Z. N. & Barabasi, A. L. (2000) Nature 407, 651-654] or degree correlation [Maslov, S. & Sneppen, K. (2002) Science 296, 910-913], and hence focused on single/two-body properties of these networks. Here, by analyzing the multibody structure of the network of protein-protein interactions, we discovered molecular modules that are densely connected within themselves but sparsely connected with the rest of the network. Comparison with experimental data and functional annotation of genes showed two types of modules: (i) protein complexes (splicing machinery, transcription factors, etc.) and (ii) dynamic functional units (signaling cascades, cell-cycle regulation, etc.). Discovered modules are highly statistically significant, as is evident from comparison with random graphs, and are robust to noise in the data. Our results provide strong support for the network modularity principle introduced by Hartwell et al. [Hartwell, L. H., Hopfield, J. J., Leibler, S. & Murray, A. W. (1999) Nature 402, C47-C52], suggesting that found modules constitute the "building blocks" of molecular networks.

A new way to explore the world of extracellular protein interactions

Christopher Sanderson — 2008-04-03T12:57:27-00:00

Genome Res., Vol. 18, No. 4. (1 April 2008), pp. 517-520.

Eukaryotic genomes encode large numbers of proteins that are either secreted or have exposed extracellular domains. It is highly likely that these proteins facilitate many important biological processes: however, as yet, most remain uncharacterized. Progress in this area of research has been impaired by the lack of a robust screening system that can be used to investigate interactions between large numbers of different extracellular proteins. In this issue, Bushell et al. introduce AVEXIS (avidity-based extracellular interaction screen), a high-throughput screening procedure, which can be used to identify even weak extracellular protein interactions with extremely high confidence. This assay represents an important development in the field of network biology. By combining data from the AVEXIS system with data produced by classical or variant yeast two-hybrid methods, it will be possible to assemble binary protein interaction networks that connect extracellular and intracellular processes. This information will dramatically increase our ability to understand a wide range of physiological processes and facilitate the development of better therapeutic strategies. 10.1101/gr.074583.107

Flexible web-based integration of distributed large-scale human protein interaction maps

Gautam Chaurasia — 2008-04-23T16:12:45-00:00

Journal of Integrative Bioinformatics, Vol. 4, No. 1. (2007), pp. 51-61.

Protein-protein interactions constitute the backbone of many molecular processes. This has motivated the recent construction of several large-scale human protein-protein interaction maps. Although these maps clearly offer a wealth of information, their use is challenging: complexity, rapid growth, and fragmentation of interaction data hamper their usability. To overcome these hurdles, we have developed a publicly accessible database termed UniHI (Unified Human Interactome) for integration of human protein-protein interaction data. This database is designed to provide biomedical researchers a common platform for exploring previously disconnected human interaction maps. UniHI offers researchers flexible integrated tools for accessing comprehensive information about the human interactome. Several features included in the UniHI allow users to perform various types of network-oriented and functional analysis. At present, UniHI contains over 160,000 distinct interactions between 17,000 unique proteins from ten major interaction maps derived by both computational and experimental approaches. Here we describe the details of the implementation and maintenance of UniHI and discuss the challenges that have to be addressed for a successful integration of interaction data.

UniHI: an entry gate to the human protein interactome.

G Chaurasia — 2007-01-15T21:33:59-00:00

Nucleic Acids Res, Vol. 35, No. Database issue. (January 2007)

Systematic mapping of protein-protein interactions has become a central task of functional genomics. To map the human interactome, several strategies have recently been pursued. The generated interaction datasets are valuable resources for scientists in biology and medicine. However, comparison reveals limited overlap between different interaction networks. This divergence obstructs usability, as researchers have to interrogate numerous heterogeneous datasets to identify potential interaction partners for proteins of interest. To facilitate direct access through a single entry gate, we have started to integrate currently available human protein interaction data in an easily accessible online database. It is called UniHI (Unified Human Interactome) and is available at http://www.mdc-berlin.de/unihi. At present, it is based on 10 major interaction maps derived by computational and experimental methods. It includes more than 150,000 distinct interactions between more than 17 000 unique human proteins. UniHI provides researchers with a flexible integrated tool for finding and using comprehensive information about the human interactome.

A tree kernel to analyse phylogenetic profiles.

JP Vert — 2005-11-09T10:44:21-00:00

Bioinformatics, Vol. 18 Suppl 1 (2002)

MOTIVATION: The phylogenetic profile of a protein is a string that encodes the presence or absence of the protein in every fully sequenced genome. Because proteins that participate in a common structural complex or metabolic pathway are likely to evolve in a correlated fashion, the phylogenetic profiles of such proteins are often 'similar' or at least 'related' to each other. The question we address in this paper is the following: how to measure the 'similarity' between two profiles, in an evolutionarily relevant way, in order to develop efficient function prediction methods? RESULTS: We show how the profiles can be mapped to a high-dimensional vector space which incorporates evolutionarily relevant information, and we provide an algorithm to compute efficiently the inner product in that space, which we call the tree kernel. The tree kernel can be used by any kernel-based analysis method for classification or data mining of phylogenetic profiles. As an application a Support Vector Machine (SVM) trained to predict the functional class of a gene from its phylogenetic profile is shown to perform better with the tree kernel than with a naive kernel that does not include any information about the phylogenetic relationships among species. Moreover a kernel principal component analysis (KPCA) of the phylogenetic profiles illustrates the sensitivity of the tree kernel to evolutionarily relevant variations.

Topology-Bayes versus Clade-Bayes in Phylogenetic Analysis

Ward Wheeler — 2007-12-16T04:42:09-00:00

Mol Biol Evol (12 December 2007), msm274.

Several features of currently used Bayesian methods in phylogenetic analysis are discussed. The distinction between Clade-Bayes and Topology-Bayes is presented and illustrated with an empirical example. Three problems with "Bayesian" phylogenetic methods|exaggerated clade support, inconsistently biased priors, and the impossibility of hypothesis testing of cladograms|are shown to be the result of using a Clade-based Bayesian approach. Topology-based Bayesian methods do not share these shortcomings. 10.1093/molbev/msm274

Consolidating the set of known human protein-protein interactions in preparation for large-scale mapping of the human interactome.

AK Ramani — 2005-05-31T15:52:59-00:00

Genome Biol, Vol. 6, No. 5. (2005)

BACKGROUND: Extensive protein interaction maps are being constructed for yeast, worm, and fly to ask how the proteins organize into pathways and systems, but no such genome-wide interaction map yet exists for the set of human proteins. To prepare for studies in humans, we wished to establish tests for the accuracy of future interaction assays and to consolidate the known interactions among human proteins. RESULTS: We established two tests of the accuracy of human protein interaction datasets and measured the relative accuracy of the available data. We then developed and applied natural language processing and literature-mining algorithms to recover from Medline abstracts 6,580 interactions among 3,737 human proteins. A three-part algorithm was used: first, human protein names were identified in Medline abstracts using a discriminator based on conditional random fields, then interactions were identified by the co-occurrence of protein names across the set of Medline abstracts, filtering the interactions with a Bayesian classifier to enrich for legitimate physical interactions. These mined interactions were combined with existing interaction data to obtain a network of 31,609 interactions among 7,748 human proteins, accurate to the same degree as the existing datasets. CONCLUSION: These interactions and the accuracy benchmarks will aid interpretation of current functional genomics data and provide a basis for determining the quality of future large-scale human protein interaction assays. Projecting from the approximately 15 interactions per protein in the best-sampled interaction set to the estimated 25,000 human genes implies more than 375,000 interactions in the complete human protein interaction network. This set therefore represents no more than 10% of the complete network.

Towards a comprehensive atlas of the physical interactome of Saccharomyces cerevisiae.

Sean R Collins — 2007-02-05T14:08:02-00:00

Mol Cell Proteomics (2 January 2007)

Defining protein complexes is critical to virtually all aspects of cell biology. Two recent affinity purification/mass spectrometry studies in Saccharomyces cerevisiae have vastly increased the available protein interaction data. The practical utility of such high-throughput interaction sets, however, is substantially decreased by the presence of false positives. Here we create a novel probabilistic metric that takes advantage of the high density of these data, including both the presence and absence of individual associations, to provide a measure of the relative confidence of each potential protein-protein interaction. This analysis largely overcomes the noise inherent in high-throughput immunoprecipitation experiments. For example, of the 12,122 binary interactions in the general repository of interaction data (BioGRID) derived from these two studies, we mark 7,504 as being of substantially lower confidence. Additionally, applying our metric and a stringent cutoff identifies a set of 9,074 interactions (including 4,456 which were not among the 12,122 interactions) with accuracy comparable to that of conventional small-scale methodologies. Finally, we organize proteins into coherent multi-subunit complexes using hierarchical clustering. This work thus provides a highly accurate physical interaction map of yeast in a format that is readily accessible to the biological community.

A probabilistic view of gene function.

AG Fraser — 2005-02-22T16:31:45-00:00

Nat Genet, Vol. 36, No. 6. (June 2004), pp. 559-564.

Cells are controlled by the complex and dynamic actions of thousands of genes. With the sequencing of many genomes, the key problem has shifted from identifying genes to knowing what the genes do; we need a framework for expressing that knowledge. Even the most rigorous attempts to construct ontological frameworks describing gene function (e.g., the Gene Ontology project) ultimately rely on manual curation and are thus labor-intensive and subjective. But an alternative exists: the field of functional genomics is piecing together networks of gene interactions, and although these data are currently incomplete and error-prone, they provide a glimpse of a new, probabilistic view of gene function. We outline such a framework, which revolves around a statistical description of gene interactions derived from large, systematically compiled data sets. In this probabilistic view, pleiotropy is implicit, all data have errors and the definition of gene function is an iterative process that ultimately converges on the correct functions. The relationships between the genes are defined by the data, not by hand. Even this comprehensive view fails to capture key aspects of gene function, not least their dynamics in time and space, showing that there are limitations to the model that must ultimately be addressed.

Comparative assessment of large-scale data sets of protein-protein interactions.

C von Mering — 2004-11-22T00:17:30-00:00

Nature, Vol. 417, No. 6887. (23 May 2002), pp. 399-403.

Comprehensive protein protein interaction maps promise to reveal many aspects of the complex regulatory network underlying cellular function. Recently, large-scale approaches have predicted many new protein interactions in yeast. To measure their accuracy and potential as well as to identify biases, strengths and weaknesses, we compare the methods with each other and with a reference set of previously reported protein interactions.

Insights into Protein-Protein Interfaces using a Bayesian Network Prediction Method

James Bradford — 2006-08-23T08:14:01-00:00

Journal of Molecular Biology, Vol. In Press, Corrected Proof

Identifying the interface between two interacting proteins provides important clues to the function of a protein, and is becoming increasing relevant to drug discovery. Here, surface patch analysis was combined with a Bayesian network to predict protein-protein binding sites with a success rate of 82% on a benchmark dataset of 180 proteins, improving by 6% on previous work and well above the 36% that would be achieved by a random method. A comparable success rate was achieved even when evolutionary information was missing, a further improvement on our previous method which was unable to handle incomplete data automatically. In a case study of the Mog1p family, we showed that our Bayesian network method can aid the prediction of previously uncharacterised binding sites and provide important clues to protein function. On Mog1p itself a putative binding site involved in the SLN1-SKN7 signal transduction pathway was detected, as was a Ran binding site, previously characterized solely by conservation studies, even though our automated method operated without using homologous proteins. On the remaining members of the family (two structural genomics targets, and a protein involved in the photosystem II complex in higher plants) we identified novel binding sites with little correspondence to those on Mog1p. These results suggest that members of the Mog1p family bind to different proteins and probably have different functions despite sharing the same overall fold. We also demonstrated the applicability of our method to drug discovery efforts by successfully locating a number of binding sites involved in the protein-protein interaction network of papilloma virus infection. In a separate study, we attempted to distinguish between the two types of binding site, obligate and non-obligate, within our dataset using a second Bayesian network. This proved difficult although some separation was achieved on the basis of patch size, electrostatic potential and conservation. Such was the similarity between the two interacting patch types, we were able to use obligate binding site properties to predict the location of non-obligate binding sites and vice versa.

Computational analysis of human protein interaction networks.

F Ramírez — 2008-01-18T02:09:25-00:00

Proteomics, Vol. 7, No. 15. (August 2007), pp. 2541-2552.

Large amounts of human protein interaction data have been produced by experiments and prediction methods. However, the experimental coverage of the human interactome is still low in contrast to predicted data. To gain insight into the value of publicly available human protein network data, we compared predicted datasets, high-throughput results from yeast two-hybrid screens, and literature-curated protein-protein interactions. This evaluation is not only important for further methodological improvements, but also for increasing the confidence in functional hypotheses derived from predictions. Therefore, we assessed the quality and the potential bias of the different datasets using functional similarity based on the Gene Ontology, structural iPfam domain-domain interactions, likelihood ratios, and topological network parameters. This analysis revealed major differences between predicted datasets, but some of them also scored at least as high as the experimental ones regarding multiple quality measures. Therefore, since only small pair wise overlap between most datasets is observed, they may be combined to enlarge the available human interactome data. For this purpose, we additionally studied the influence of protein length on data quality and the number of disease proteins covered by each dataset. We could further demonstrate that protein interactions predicted by more than one method achieve an elevated reliability.

Comprehensive curation and analysis of global interaction networks in Saccharomyces cerevisiae

Teresa Reguly — 2006-06-13T14:14:14-00:00

Journal of Biology, Vol. 5, No. 4. (08 June 2006), 11.

BACKGROUND:The study of complex biological networks and prediction of gene function has been enabled by high-throughput (HTP) methods for detection of genetic and protein interactions. Sparse coverage in HTP datasets may, however, distort network properties and confound predictions. Although a vast number of well substantiated interactions are recorded in the scientific literature, these data have not yet been distilled into networks that enable system-level inference.RESULTS:We describe here a comprehensive database of genetic and protein interactions, and associated experimental evidence, for the budding yeast Saccharomyces cerevisiae, as manually curated from over 31,793 abstracts and online publications. This literature-curated (LC) dataset contains 33,311 interactions, on the order of all extant HTP datasets combined. Surprisingly, HTP protein-interaction datasets currently achieve only around 14% coverage of the interactions in the literature. The LC network nevertheless shares attributes with HTP networks, including scale-free connectivity and correlations between interactions, abundance, localization, and expression. We find that essential genes or proteins are enriched for interactions with other essential genes or proteins, suggesting that the global network may be functionally unified. This interconnectivity is supported by a substantial overlap of protein and genetic interactions in the LC dataset. We show that the LC dataset considerably improves the predictive power of network-analysis approaches. The full LC dataset is available at the BioGRID (http://www.thebiogrid.org) and SGD (http://www.yeastgenome.org/) databases.CONCLUSION:Comprehensive datasets of biological interactions derived from the primary literature provide critical benchmarks for HTP methods, augment functional prediction, and reveal system-level attributes of biological networks.

Discovery of biological networks from diverse functional genomic data.

CL Myers — 2008-03-28T20:41:18-00:00

Genome Biol, Vol. 6, No. 13. (2005)

We have developed a general probabilistic system for query-based discovery of pathway-specific networks through integration of diverse genome-wide data. This framework was validated by accurately recovering known networks for 31 biological processes in Saccharomyces cerevisiae and experimentally verifying predictions for the process of chromosomal segregation. Our system, bioPIXIE, a public, comprehensive system for integration, analysis, and visualization of biological network predictions for S. cerevisiae, is freely accessible over the worldwide web.

Hierarchical multi-label prediction of gene function.

Zafer Barutcuoglu — 2006-01-24T06:53:31-00:00

Bioinformatics (12 January 2006)

MOTIVATION: Assigning functions for unknown genes based on diverse large-scale data is a key task in functional genomics. Previous work on gene function prediction has addressed this problem using independent classifiers for each function. However, such an approach ignores the structure of functional class taxonomies such as the Gene Ontology. Over a hierarchy of functional classes, a group of independent classifiers where each one predicts gene membership to a particular class can produce a hierarchically inconsistent set of predictions, where for a given gene a specific class may be predicted positive while its inclusive parent class is predicted negative. Taking the hierarchical structure into account resolves such inconsistencies, and provides an opportunity for leveraging all classifiers in the hierarchy to achieve higher specificity of predictions. RESULTS: We developed a Bayesian framework for combining multiple classifiers based on the functional taxonomy constraints. Using a hierarchy of support vector machine (SVM) classifiers trained on multiple data types, we combined predictions in our Bayesian framework to obtain the most probable consistent set of predictions. Experiments show that over a 105-node subhierarchy of the Gene Ontology, our Bayesian framework improves predictions for 93 nodes. As an additional benefit, our method also provides implicit calibration of SVM margin outputs to probabilities. Using this method, we make function predictions for multiple proteins, and experimentally confirm predictions for proteins involved in mitosis. SUPPLEMENTARY INFORMATION: Results for the 105 selected GO classes and predictions for 1059 unknown genes are available at: http://function.princeton.edu/genesite/.

Finding function: evaluation methods for functional genomic data

Chad Myers — 2006-07-25T17:50:31-00:00

BMC Genomics, Vol. 7 (25 July 2006), 187.

Context-sensitive data integration and prediction of biological networks

Chad Myers — 2007-09-12T01:19:20-00:00

Bioinformatics, Vol. 23, No. 17. (1 September 2007), pp. 2322-2330.

Motivation: Several recent methods have addressed the problem of heterogeneous data integration and network prediction by modeling the noise inherent in high-throughput genomic datasets, which can dramatically improve specificity and sensitivity and allow the robust integration of datasets with heterogeneous properties. However, experimental technologies capture different biological processes with varying degrees of success, and thus, each source of genomic data can vary in relevance depending on the biological process one is interested in predicting. Accounting for this variation can significantly improve network prediction, but to our knowledge, no previous approaches have explicitly leveraged this critical information about biological context. Results: We confirm the presence of context-dependent variation in functional genomic data and propose a Bayesian approach for context-sensitive integration and query-based recovery of biological process-specific networks. By applying this method to Saccharomyces cerevisiae, we demonstrate that leveraging contextual information can significantly improve the precision of network predictions, including assignment for uncharacterized genes. We expect that this general context-sensitive approach can be applied to other organisms and prediction scenarios. Availability: A software implementation of our approach is available on request from the authors. Contact: ogt@genomics.princeton.edu Supplementary information: Supplementary data are available at http://avis.princeton.edu/contextPIXIE/ 10.1093/bioinformatics/btm332

Effect of sampling on topology predictions of protein-protein interaction networks

Jing-Dong Han — 2005-07-07T22:27:11-00:00

Nature Biotechnology, Vol. 23, No. 7. (07 July 2005), pp. 839-844.

Computational systems biology.

H Kitano — 2004-11-22T00:17:30-00:00

Nature, Vol. 420, No. 6912. (14 November 2002), pp. 206-210.

To understand complex biological systems requires the integration of experimental and computational research -- in other words a systems biology approach. Computational biology, through pragmatic modelling and theoretical exploration, provides a powerful foundation from which to address critical scientific questions head-on. The reviews in this Insight cover many different aspects of this energetic field, although all, in one way or another, illuminate the functioning of modular circuits, including their robustness, design and manipulation. Computational systems biology addresses questions fundamental to our understanding of life, yet progress here will lead to practical innovations in medicine, drug discovery and engineering.

Genomic analysis of essentiality within protein networks.

H Yu — 2006-04-12T08:33:22-00:00

Trends Genet, Vol. 20, No. 6. (June 2004), pp. 227-231.

In this article, we introduce the notion of 'marginal essentiality' through combining quantitatively the results from large-scale phenotypic experiments (e.g. growth rate inhibition from knockouts). We find that this quantity relates to many of the topological characteristics of protein-protein interaction networks. In particular, proteins with a greater degree of marginal essentiality tend to be network hubs (i.e. with many interactions) and tend to have a shorter characteristic path length to their neighbors. We extend our network analysis to encompass transcriptional regulatory networks. Although transcription factors with many targets tend to be essential, surprisingly, we find that genes that are regulated by many transcription factors are usually not essential.

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

R Jansen — 2005-09-29T13:29:57-00:00

Curr Opin Microbiol, Vol. 7, No. 5. (October 2004), pp. 535-545.

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.

PROTEOMICS: Enhanced: Integrating Interactomes

Mark Gerstein — 2006-02-28T19:59:24-00:00

Science, Vol. 295, No. 5553. (11 January 2002), pp. 284-287.

Integrating 'omic' information: a bridge between genomics and systems biology.

H Ge — 2005-08-30T18:04:53-00:00

Trends Genet, Vol. 19, No. 10. (October 2003), pp. 551-560.

The availability of genome sequences for several organisms, including humans, and the resulting first-approximation lists of genes, have allowed a transition from molecular biology to 'modular biology'. In modular biology, biological processes of interest, or modules, are studied as complex systems of functionally interacting macromolecules. Functional genomic and proteomic ('omic') approaches can be helpful to accelerate the identification of the genes and gene products involved in particular modules, and to describe the functional relationships between them. However, the data emerging from individual omic approaches should be viewed with caution because of the occurrence of false-negative and false-positive results and because single annotations are not sufficient for an understanding of gene function. To increase the reliability of gene function annotation, multiple independent datasets need to be integrated. Here, we review the recent development of strategies for such integration and we argue that these will be important for a systems approach to modular biology.

Integrating interactome, phenome, and transcriptome mapping data for the C. elegans germline.

AJ Walhout — 2008-03-28T20:21:24-00:00

Curr Biol, Vol. 12, No. 22. (19 November 2002), pp. 1952-1958.

By integrating functional genomic and proteomic mapping approaches, biological hypotheses should be formulated with increasing levels of confidence. For example, yeast interactome and transcriptome data can be correlated in biologically meaningful ways. Here, we combine interactome mapping data generated for a multicellular organism with data from both large-scale phenotypic analysis ("phenome mapping") and transcriptome profiling. First, we generated a two-hybrid interactome map of the Caenorhabditis elegans germline by using 600 transcripts enriched in this tissue. We compared this map to a phenome map of the germline obtained by RNA interference (RNAi) and to a transcriptome map obtained by clustering worm genes across 553 expression profiling experiments. In this dataset, we find that essential proteins have a tendency to interact with each other, that pairs of genes encoding interacting proteins tend to exhibit similar expression profiles, and that, for approximately 24% of germline interactions, both partners show overlapping embryonic lethal or high incidence of males RNAi phenotypes and similar expression profiles. We propose that these interactions are most likely to be relevant to germline biology. Similar integration of interactome, phenome, and transcriptome data should be possible for other biological processes in the nematode and for other organisms, including humans.

Interrelating different types of genomic data, from proteome to secretome: 'oming in on function.

D Greenbaum — 2008-03-28T20:17:44-00:00

Genome Res., Vol. 11, No. 9. (September 2001), pp. 1463-1468.

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 http://bioinfo.mbb.yale.edu/what-is-it.

NETWORK BIOLOGY: UNDERSTANDING THE CELL'S FUNCTIONAL ORGANIZATION

Albert-Laszlo Barabasi — 2005-11-18T20:15:35-00:00

Nat Rev Genet, Vol. 5, No. 2. (February 2004), pp. 101-113.

A key aim of postgenomic biomedical research is to systematically catalogue all molecules and their interactions within a living cell. There is a clear need to understand how these molecules and the interactions between them determine the function of this enormously complex machinery, both in isolation and when surrounded by other cells. Rapid advances in network biology indicate that cellular networks are governed by universal laws and offer a new conceptual framework that could potentially revolutionize our view of biology and disease pathologies in the twenty-first century.

The Importance of Bottlenecks in Protein Networks: Correlation with Gene Essentiality and Expression Dynamics

Haiyuan Yu — 2007-04-21T04:51:32-00:00

PLoS Computational Biology, Vol. preprint, No. 2007. (1 February 2007), e59.eor.

It has been a long-standing goal in systems biology to find relations between the topological properties and functional features of protein networks. However, most of the focus in network studies has been on highly connected proteins (�??hubs�??). As a complementary notion, we define bottlenecks as proteins with a high betweenness centrality, i.e., network nodes that have a high number of shortest paths going through them. We found that bottlenecks are key connector proteins with surprising functional and dynamic properties. In particular, bottlenecks are more likely to be essential proteins. In fact, in regulatory and other directed networks, betweenness (i.e., "bottleneck-ness") is a much more significant indicator of essentiality than degree (i.e., "hub-ness"). Furthermore, bottlenecks correspond to the dynamic components of the interaction network - they are significantly less well co-expressed with their neighbors than non-bottlenecks, implying that expression dynamics is wired into the network topology.

Positive selection at the protein network periphery: Evaluation in terms of structural constraints and cellular context

Philip Kim — 2007-12-14T00:28:18-00:00

Proceedings of the National Academy of Sciences (12 December 2007), 0710183104.

Because of recent advances in genotyping and sequencing, human genetic variation and adaptive evolution in the primate lineage have become major research foci. Here, we examine the relationship between genetic signatures of adaptive evolution and network topology. We find a striking tendency of proteins that have been under positive selection (as compared with the chimpanzee) to be located at the periphery of the interaction network. Our results are based on the analysis of two types of genome evolution, both in terms of intra- and interspecies variation. First, we looked at single-nucleotide polymorphisms and their fixed variants, single-nucleotide differences in the human genome relative to the chimpanzee. Second, we examine fixed structural variants, specifically large segmental duplications and their polymorphic precursors known as copy number variants. We propose two complementary mechanisms that lead to the observed trends. First, we can rationalize them in terms of constraints imposed by protein structure: We find that positively selected sites are preferentially located on the exposed surface of proteins. Because central network proteins (hubs) are likely to have a larger fraction of their surface involved in interactions, they tend to be constrained and under negative selection. Conversely, we show that the interaction network roughly maps to cellular organization, with the periphery of the network corresponding to the cellular periphery (i.e., extracellular space or cell membrane). This suggests that the observed positive selection at the network periphery may be due to an increase of adaptive events on the cellular periphery responding to changing environments. 10.1073/pnas.0710183104

Relating three-dimensional structures to protein networks provides evolutionary insights.

PM Kim — 2006-12-27T12:42:51-00:00

Science, Vol. 314, No. 5807. (22 December 2006), pp. 1938-1941.

Most studies of protein networks operate on a high level of abstraction, neglecting structural and chemical aspects of each interaction. Here, we characterize interactions by using atomic-resolution information from three-dimensional protein structures. We find that some previously recognized relationships between network topology and genomic features (e.g., hubs tending to be essential proteins) are actually more reflective of a structural quantity, the number of distinct binding interfaces. Subdividing hubs with respect to this quantity provides insight into their evolutionary rate and indicates that additional mechanisms of network growth are active in evolution (beyond effective preferential attachment through gene duplication).

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

R Jansen — 2006-07-18T03:19:59-00:00

Genome Res, Vol. 12, No. 1. (January 2002), pp. 37-46.

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.)

Systems biology: a brief overview.

H Kitano — 2004-11-22T00:17:30-00:00

Science, Vol. 295, No. 5560. (1 March 2002), pp. 1662-1664.

To understand biology at the system level, we must examine the structure and dynamics of cellular and organismal function, rather than the characteristics of isolated parts of a cell or organism. Properties of systems, such as robustness, emerge as central issues, and understanding these properties may have an impact on the future of medicine. However, many breakthroughs in experimental devices, advanced software, and analytical methods are required before the achievements of systems biology can live up to their much-touted potential.

Correlation between transcriptome and interactome mapping data from Saccharomyces cerevisiae.

H Ge — 2005-08-31T21:46:20-00:00

Nat Genet, Vol. 29, No. 4. (December 2001), pp. 482-486.

Genomic and proteomic approaches can provide hypotheses concerning function for the large number of genes predicted from genome sequences. Because of the artificial nature of the assays, however, the information from these high-throughput approaches should be considered with caution. Although it is possible that more meaningful hypotheses could be formulated by integrating the data from various functional genomic and proteomic projects, it has yet to be seen to what extent the data can be correlated and how such integration can be achieved. We developed a 'transcriptome-interactome correlation mapping' strategy to compare the interactions between proteins encoded by genes that belong to common expression-profiling clusters with those between proteins encoded by genes that belong to different clusters. Using this strategy with currently available data sets for Saccharomyces cerevisiae, we provide the first global evidence that genes with similar expression profiles are more likely to encode interacting proteins. We show how this correlation between transcriptome and interactome data can be used to improve the quality of hypotheses based on the information from both approaches. The strategy described here may help to integrate other functional genomic and proteomic data, both in yeast and in higher organisms.

Getting connected: analysis and principles of biological networks.

X Zhu — 2007-05-21T01:14:42-00:00

Genes Dev, Vol. 21, No. 9. (1 May 2007), pp. 1010-1024.

The execution of complex biological processes requires the precise interaction and regulation of thousands of molecules. Systematic approaches to study large numbers of proteins, metabolites, and their modification have revealed complex molecular networks. These biological networks are significantly different from random networks and often exhibit ubiquitous properties in terms of their structure and organization. Analyzing these networks provides novel insights in understanding basic mechanisms controlling normal cellular processes and disease pathologies.

Understanding Network Concepts in Modules

Jun Dong — 2007-06-05T04:41:35-00:00

BMC Systems Biology, Vol. 1, No. 1. (2007)

BACKGROUND:Network concepts are increasingly used in biology and genetics. For example, the clustering coefficient has been used to understand network architecture; the connectivity (also known as degree) has been used to screen for cancer targets; and the topological overlap matrix has been used to define modules and to annotate genes. Dozens of potentially useful network concepts are known from graph theory.RESULTS:Here we study network concepts in special types of networks, which we refer to as approximately factorizable networks. In these networks, the pairwise connection strength (adjacency) between 2 network nodes can be factored into node specific contributions, named node 'conformity'. The node conformity turns out to be highly related to the connectivity. To provide a formalism for relating network concepts to each other, we define three types of network concepts: fundamental-, conformity-based-, and approximate conformity-based concepts. Fundamental concepts include the standard definitions of connectivity, density, centralization, heterogeneity, clustering coefficient, and topological overlap. The approximate conformity-based analogs of fundamental network concepts have several theoretical advantages. First, they allow one to derive simple relationships between seemingly disparate networks concepts. For example, we derive simple relationships between the clustering coefficient, the heterogeneity, the density, the centralization, and the topological overlap. The second advantage of approximate conformity-based network concepts is that they allow one to show that fundamental network concepts can be approximated by simple functions of the connectivity in module networks.CONCLUSIONS:Using protein-protein interaction, gene co-expression, and simulated data, we show that a) many networks comprised of module nodes are approximately factorizable and b) in these types of networks, simple relationships exist between seemingly disparate network concepts. Our results are implemented in freely available R software code, which can be downloaded from the following webpage: http://www.genetics.ucla.edu/labs/horvath/ModuleConformity/ModuleNetworks

What is a gene, post-ENCODE? History and updated definition.

MB Gerstein — 2007-06-15T21:49:06-00:00

Genome Res, Vol. 17, No. 6. (June 2007), pp. 669-681.

While sequencing of the human genome surprised us with how many protein-coding genes there are, it did not fundamentally change our perspective on what a gene is. In contrast, the complex patterns of dispersed regulation and pervasive transcription uncovered by the ENCODE project, together with non-genic conservation and the abundance of noncoding RNA genes, have challenged the notion of the gene. To illustrate this, we review the evolution of operational definitions of a gene over the past century-from the abstract elements of heredity of Mendel and Morgan to the present-day ORFs enumerated in the sequence databanks. We then summarize the current ENCODE findings and provide a computational metaphor for the complexity. Finally, we propose a tentative update to the definition of a gene: A gene is a union of genomic sequences encoding a coherent set of potentially overlapping functional products. Our definition sidesteps the complexities of regulation and transcription by removing the former altogether from the definition and arguing that final, functional gene products (rather than intermediate transcripts) should be used to group together entities associated with a single gene. It also manifests how integral the concept of biological function is in defining genes.

An encyclopaedia of interactions

Ekat Kritikou — 2007-06-22T09:30:49-00:00

Nat Rev Mol Cell Biol, Vol. 8, No. 7. (July 2007), pp. 512-512.

Genome Transplantation in Bacteria: Changing One Species to Another

Carole Lartigue — 2007-06-30T05:37:35-00:00

Science (28 June 2007), 1144622.

As a step toward propagation of synthetic genomes, we completely replaced the genome of a bacterial cell with one from another species by transplanting a whole genome as naked DNA. Intact genomic DNA from Mycoplasma mycoides large colony (LC), virtually free of protein, was transplanted into Mycoplasma capricolum cells by polyethylene glycol-mediated transformation. Cells selected for tetracycline resistance, carried by the M. mycoides LC chromosome, contain the complete donor genome and are free of detectable recipient genomic sequences. These cells that result from genome transplantation are phenotypically identical to the M. mycoides LC donor strain as judged by several criteria. 10.1126/science.1144622

Key biology databases go wiki

Jim Giles — 2007-02-14T19:39:33-00:00

Nature, Vol. 445, No. 7129. (14 February 2007), pp. 691-691.

The human disease network

Kwang-Il Goh — 2007-05-23T08:39:54-00:00

PNAS, Vol. 104, No. 21. (22 May 2007), pp. 8685-8690.

A network of disorders and disease genes linked by known disorder-gene associations offers a platform to explore in a single graph-theoretic framework all known phenotype and disease gene associations, indicating the common genetic origin of many diseases. Genes associated with similar disorders show both higher likelihood of physical interactions between their products and higher expression profiling similarity for their transcripts, supporting the existence of distinct disease-specific functional modules. We find that essential human genes are likely to encode hub proteins and are expressed widely in most tissues. This suggests that disease genes also would play a central role in the human interactome. In contrast, we find that the vast majority of disease genes are nonessential and show no tendency to encode hub proteins, and their expression pattern indicates that they are localized in the functional periphery of the network. A selection-based model explains the observed difference between essential and disease genes and also suggests that diseases caused by somatic mutations should not be peripheral, a prediction we confirm for cancer genes. 10.1073/pnas.0701361104

Genetics of global gene expression

Matthew Rockman — 2006-10-18T20:14:27-00:00

Nature Reviews Genetics, Vol. 7, No. 11., pp. 862-872.