CiteULike: grahamc's human

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)

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.

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.

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.

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

Protein interactions in human genetic diseases

Benjamin Bockler — 2008-01-16T17:22:22-00:00

Genome Biology, Vol. 9, No. 1. (2008)

We present a novel method that combines protein structure information with protein interaction data to identify residues that form part of an interaction interface. Our prediction method can retrieve interaction hotspots with an accuracy of 60% (at a 20 false positive rate). The method was applied to all mutations in the Online Mendelian Inheritance in Man (OMIM) database, predicting 1428 mutations to be related to an interaction defect. Combining predicted and hand-curated sets, we discuss how mutations affect protein interactions in general.

The Edinburgh human metabolic network reconstruction and its functional analysis.

H Ma — 2007-09-25T21:19:33-00:00

Mol Syst Biol, Vol. 3 (2007)

A better understanding of human metabolism and its relationship with diseases is an important task in human systems biology studies. In this paper, we present a high-quality human metabolic network manually reconstructed by integrating genome annotation information from different databases and metabolic reaction information from literature. The network contains nearly 3000 metabolic reactions, which were reorganized into about 70 human-specific metabolic pathways according to their functional relationships. By analysis of the functional connectivity of the metabolites in the network, the bow-tie structure, which was found previously by structure analysis, is reconfirmed. Furthermore, the distribution of the disease related genes in the network suggests that the IN (substrates) subset of the bow-tie structure has more flexibility than other parts.

Coexpression analysis of human genes across many microarray data sets.

HK Lee — 2006-09-08T15:43:38-00:00

Genome Res, Vol. 14, No. 6. (June 2004), pp. 1085-1094.

We present a large-scale analysis of mRNA coexpression based on 60 large human data sets containing a total of 3924 microarrays. We sought pairs of genes that were reliably coexpressed (based on the correlation of their expression profiles) in multiple data sets, establishing a high-confidence network of 8805 genes connected by 220,649 "coexpression links" that are observed in at least three data sets. Confirmed positive correlations between genes were much more common than confirmed negative correlations. We show that confirmation of coexpression in multiple data sets is correlated with functional relatedness, and show how cluster analysis of the network can reveal functionally coherent groups of genes. Our findings demonstrate how the large body of accumulated microarray data can be exploited to increase the reliability of inferences about gene function.

A Map of Recent Positive Selection in the Human Genome.

Benjamin F Voight — 2006-03-08T03:25:45-00:00

PLoS Biol, Vol. 4, No. 3. (7 March 2006)

The identification of signals of very recent positive selection provides information about the adaptation of modern humans to local conditions. We report here on a genome-wide scan for signals of very recent positive selection in favor of variants that have not yet reached fixation. We describe a new analytical method for scanning single nucleotide polymorphism (SNP) data for signals of recent selection, and apply this to data from the International HapMap Project. In all three continental groups we find widespread signals of recent positive selection. Most signals are region-specific, though a significant excess are shared across groups. Contrary to some earlier low resolution studies that suggested a paucity of recent selection in sub-Saharan Africans, we find that by some measures our strongest signals of selection are from the Yoruba population. Finally, since these signals indicate the existence of genetic variants that have substantially different fitnesses, they must indicate loci that are the source of significant phenotypic variation. Though the relevant phenotypes are generally not known, such loci should be of particular interest in mapping studies of complex traits. For this purpose we have developed a set of SNPs that can be used to tag the strongest approximately 250 signals of recent selection in each population.

The human phylome

Jaime Huerta-Cepas — 2007-06-13T17:13:45-00:00

Genome Biology, Vol. 8 (13 June 2007), R109.

A human phenome-interactome network of protein complexes implicated in genetic disorders

Kasper Lage — 2007-03-08T11:22:52-00:00

Nature Biotechnology, Vol. 25, No. 3. (07 March 2007), pp. 309-316.

Comparison of Human Protein-Protein Interaction Maps.

Matthias E Futschik — 2007-02-04T20:37:00-00:00

Bioinformatics (19 January 2007)

MOTIVATION: Large-scale mappings of protein-protein interactions have started to give us new views of the complex molecular mechanisms inside a cell. After initial projects to systematically map protein interactions in model organisms such as yeast, worm and fly, researchers have begun to focus on the mapping of the human interactome. To tackle this enormous challenge, different approaches have been proposed and pursued. While several large-scale human protein interaction maps have recently been published, their quality remains to be critically assessed. RESULTS: We present here a first comparative analysis of eight currently available large-scale maps with a total of over 10000 unique proteins and 57000 interactions included. They are based either on literature search, orthology or by yeast-two-hybrid assays. Comparison reveals only a small, but statistically significant overlap. More importantly, our analysis gives clear indications that all interaction maps imply considerable selection and detection biases. These results have to be taken into account for future assembly of the human interactome. AVAILABILITY: An integrated human interaction network called Unified Human Interactome (UniHI) is made publicly accessible at http://www.mdc-berlin.de/unihi.