Over 3,000 human diseases are known to be linked to heritable genetic variation, mapping to over 1,700 unique genes. Dating of the evolutionary age of these disease-associated genes have suggested that they have a tendency to be ancient, specifically coming into existence with early metazoan. The approach taken by past studies, however, assumes that the age of a disease is the same as the age of its common ancestor, ignoring the fundamental contribution of duplication events in the evolution of new genes and function. Here, we date both the common ancestor and the duplication history of known human diseaseassociated genes. We find that the majority of disease genes (80%) are genes that have been duplicated in their evolutionary history. Periods for which there are more disease-associated genes, for example, at the origins of bony vertebrates, are explained by the emergence of more genes at that time, and the majority of these (80%) are duplicates inferred to have arisen by whole-genome duplication. These relationships are similar for different disease types and the disease-associated gene's cellular function. This indicates that the emergence of duplication-associated diseases has been ongoing and approximately constant (relative to the retention of duplicate genes) throughout the evolution of life. This continued until approximately 390Ma fromwhich time relatively fewer novel genes came into existence on the human lineage, let alone are disease genes. For single-copy genes associated with disease, we find that the numbers of disease genes decreases with recency. For the majority of duplicates (87%), the disease-associatedmutation is associated with just one of the duplicate copies. A universal explanation for heritable disease is, thus, it is merely a by-product of the evolutionary process; the evolution of new genes (de novo or by duplication) results in the potential for new diseases to emerge.

Human immunodeficiency virus type 1 (HIV-1) exploits a diverse array of host cell functions in order to replicate. This is mediated through a network of virus-host interactions. A variety of recent studies have catalogued this information. In particular the HIV-1, Human Protein Interaction Database (HHPID) has provided a unique depth of protein interaction detail. However, as a map of HIV-1 infection, the HHPID is problematic as it contains curation error and redundancy, in addition, it is based on a heterogeneous set of experimental methods. Based on identifying shared patterns of HIV-host interaction, we have developed a novel methodology to delimit the core set of host-cellular functions and their associated perturbation from the HHPID. Initially, using biclustering, we identify 279 significant sets of host proteins that undergo the same types of interaction. The functional cohesiveness of these protein sets was validated using a human protein-protein interaction network, gene ontology annotation and sequence similarity. Next, using a distance measure, we group host protein sets in order to identify 37 distinct higher-level subsystems. We further demonstrate biological significance of these subsystems by cross-referencing with global siRNA screens that have been used to detect host factors necessary for HIV-1 replication, and investigate the seemingly small intersect between these data sets. Our results highlight significant host-cell subsystems that are perturbed during the course of HIV-1 infection. Moreover, we characterise the patterns of interaction that contribute to these perturbations. Thus, our work disentangles the complex set of HIV-1 host protein interactions in the HHPID, reconciles these with siRNA screens and provides an accessible and interpretable map of infection.

Background In order to replicate, HIV, like all viruses, needs to invade a host cell and hijack it for its own use, a process that involves multiple protein interactions between virus and host. The HIV-1, Human Protein Interaction Database available at NCBI's website captures this information from the primary literature, containing over 2,500 unique interactions. We investigate the general properties and biological context of these interactions and, thus, explore the molecular specificity of the HIV-host perturbation. In particular, we investigate (i) whether HIV preferentially interacts with highly connected and 'central' proteins, (ii) known phenotypic properties of host proteins inferred from essentiality and disease-association data, and (iii) biological context (molecular function, processes and location) of the host proteins to identify attributes most strongly associated with specific HIV interactions.
Results After correcting for ascertainment bias in the literature, we demonstrate a significantly greater propensity for HIV to interact with highly connected and central host proteins. Unexpectedly, we find there are no associations between HIV interaction and inferred essentiality. Similarly, we find a tendency for HIV not to interact with proteins encoded by genes associated with disease. Crucially, we find that functional categories over-represented in HIV-host interactions are innately enriched for highly connected and central proteins in the host system.
Conclusions Our results imply that HIV's propensity to interact with highly connected and central proteins is a consequence of interactions with particular cellular functions, rather than being a direct effect of network topological properties. The lack of a propensity for interactions with phenotypically essential proteins suggests a selective pressure to minimise virulence in retroviral evolution. Thus, the specificity of HIV-host interactions is complex, and only superficially explained by network properties.

We have identified a new human immunodeficiency virus in a Cameroonian woman. It is closely related to gorilla simian immunodeficiency virus (SIVgor) and shows no evidence of recombination with other HIV-1 lineages. This new virus seems to be the prototype of a new HIV-1 lineage that is distinct from HIV-1 groups M, N and O. We propose to designate it HIV-1 group P.

This paper reports on a system developed for the BioNLP'09 shared task on detection and characterisation of biomedical events. Event triggers and types were recognised using a conditional random field classifier and a set of rules, while event participants were identified using a rule-based system that relied on relative distances between candidate entities and the trigger in the associated parse tree. The results on previously unseen test data were encouraging: for non-regulatory events, the Fscore was almost 50% (with precision above 60%), with the overall F-score of around 30% (49% precision). The performance on more complex regulatory events was poor (F-measure of 7%). Among the 24 teams submitting the test results, our results were ranked 12th for the overall F-score and 8th for the F-score of non-regulation events.

Since the 1980s, the rapid progression of the HIV/AIDS pandemic has prompted a major international research effort. As a result, the current knowledge on HIV biology, its evolution, and origins exceeds that of many, if not all, other viruses. One of the most important areas of HIV research is the detailed understanding of HIV replication. As with all viruses, HIV must exploit the host's cellular machinery and metabolism to copy its genetic material, synthesize viral proteins, and assemble new virions. The viral replication cycle is thus dependent on an intricate network of direct and indirect protein interactions: between the viral proteins, between the virus and the host, and ultimately between the various host proteins that constitute the subverted cellular systems. When we also take into account the host's immune response and intrinsic antiviral factors, there are clearly a large number of host'pathogen relationships that are important to our full understanding of HIV biology. However, until recently, this valuable information has remained 'locked' in the published literature, making it time-consuming to study by individual researchers and inaccessible to computational analysis, thus hindering the progress of research. Here we highlight new developments in the area of host'pathogen systems biology that in our opinion will provide helpful insights to the HIV research community.

Although many interactions between HIV-1 and human proteins have been reported in the scientific literature, no publicly accessible source for efficiently reviewing this information was available. Therefore, a project was initiated in an attempt to catalogue all published interactions between HIV-1 and human proteins. HIV-related articles in PubMed were used to develop a database containing names, Entrez GeneIDs, and RefSeq protein accession numbers of interacting proteins. Furthermore, brief descriptions of the interactions, PubMed identification numbers of articles describing the interactions, and keywords for searching the interactions were incorporated. Over 100,000 articles were reviewed, resulting in the identification of 1448 human proteins that interact with HIV-1 comprising 2589 unique HIV-1-to-human protein interactions. Preliminary analysis of the extracted data indicates 32% were direct physical interactions (e.g., binding) and 68% were indirect interactions (e.g., upregulation through activation of signaling pathways). Interestingly, 37% of human proteins in the database were found to interact with more than one HIV-1 protein. For example, the signaling protein mitogen-activated protein kinase 1 has a surprising range of interactions with 10 different HIV-1 proteins. Moreover, large numbers of interactions were published for the HIV-1 regulatory protein Tat and envelope proteins: 30% and 33% of total interactions identified, respectively. The database is accessible at http://www.ncbi.nlm.nih.gov/RefSeq/HIVInteractions/ and is cross-linked to other National Center for Biotechnology Information databases and programs via Entrez Gene. This database represents a unique and continuously updated scientific resource for understanding HIV-1 replication and pathogenesis to assist in accelerating the development of effective therapeutic and vaccine interventions.

HIV exploits host cells via an intricate network of interactions. The HIV-1 Human Protein Interaction Database (a public resource available at the NCBI website) captures this information from the published literature. All published interactions involving HIV-1 and human proteins have been catalogued. Here we characterise the general properties of the HIV interacting host proteins and the nature of the HIV-host perturbation. In particular, we investigate (i) whether HIV preferentially interacts with key (highly connected and highly central) proteins, (ii) known phenotypic properties of host proteins inferred from essentiality and disease-association data, and (iii) molecular function of the host proteins (using the Gene Ontology, GO) to identify those host processes most strongly associated with specific HIV interactions. We demonstrate a significantly greater propensity for HIV to interact with highly connected and highly central host proteins. Unexpectedly, we find there are no general associations between HIV interaction and inferred essentiality, despite highly connected and highly central proteins having a tendency to be lethal mouse knock-outs. Similarly, we find a tendency for HIV to avoid interactions with proteins linked to non-lethal deleterious mutations as inferred by disease-associated genes. This suggests that the perturbation of the host system by retroviruses has evolved to use key proteins and there is a selection pressure to minimise interactions with 'essential' host proteins. We hypothesise HIV must exploit specific host proteins to achieve its biology, irrespective of their connectivity and centrality. Focussing on detailed biological functions (from GO) demonstrates the directionality and complexity of both pro-host (promoting HIV's replication cycle) and pro-pathogen (the host response to infection) interactions with specific cellular functions. Multiple functions, e.g. the immune response and apoptosis, are targeted by multiple viral proteins, being both up- and down-regulated. Crucially, we find that these functional categories are enriched for highly connected and highly central proteins in the host. Thus, HIV's propensity to interact with highly connected and highly central proteins is most probably a consequence of interactions with particular cellular functions rather than related to global network properties. Thus, the specificity of HIV-host interactions is intricate and not fully explained by the network. This detailed mapping demonstrates that HIV biology is involved in a complex 'arms race' and highlights the ancient association of retroviruses with their hosts.

HIV exploits the cell via an intricate network of interactions. Here we characterise the general properties of HIV interacting proteins and the nature of the HIV-host perturbation. We demonstrate a greater propensity for HIV to interact with highly connected and central host proteins. Unexpectedly, we find there are no associations between HIV interaction and inferred essentiality, despite highly connected proteins having a tendency to be lethal knock-outs. This suggests that the perturbation of the host system by retroviruses has evolved to use key proteins and there is a selection pressure to minimise interactions with 'essential' host proteins. We hypothesise HIV must exploit specific host proteins to achieve its biology, irrespective of their connectivity and centrality. Focussing on detailed biological functions demonstrates multiple functions are targeted by multiple viral proteins. Crucially, we find that functional categories are enriched for highly connected and central proteins in the host. Thus, HIV's propensity to interact with highly connected and central proteins is most probably a consequence of interactions with particular cellular functions rather than related to global network properties. This detailed mapping demonstrates that HIV biology is involved in a complex 'arms race' and highlights the ancient association of retroviruses with their hosts.

Network biology is concerned with representing cellular interactions and associated biological systems as networks. Substantial amounts of interaction information are curated from the scientific literature. The interests and expertise of researchers, their peers and collaborators, funding streams, public interest and nascent protocols and equipment all guide contemporary research. As such, there is undoubtedly bias in scientific research and hence the resulting literature. Robust analyses are reliant upon accurate data, for instance, in calculating the degree of a particular protein in a protein-interaction network. However, it is feasible that this protein, and hence its interactions, are affected by ascertainment bias. Specifically, if highly studied, it is likely that more interactions involving the protein will be known, thus distorting the observed network structure. We have previously evaluated node-level bias based on the proteins' publication counts in PubMed. Here we expand this work and present a novel method to evaluate ascertainment bias on interaction networks as a whole. We use rejection sampling to generate randomised sets of protein-protein interactions with the same pattern of bias as the literature-curated yeast interaction network. Applying this method to evaluate classic network properties, we find that rejection sampling performs favourably compared to naïve random sampling. Ultimately, this method will not only greatly facilitate the assessment of bias in literature-based interactomes, it will also enable a more rigorous insight into the significance of biological network properties.

HIV's exploitation of the human host for replication involves highly specific interactions with the cellular system. The persistence of the virus is dependent on an intricate network of biological interactions. There is presently extensive knowledge of molecular interactions within the scientific literature. However, manual curation of these would be an enormously time-consuming task. Here we present a procedure for automated host-pathogen focused data extraction. We focus on mining HIV-1-host protein interactions, using the gold-standard HIV-1 Human Protein Interaction Database, containing 5,127 interactions manually extracted from 14,312 references, as a benchmark for evaluation. Our process involves employing a combination of named entity recognisers (such as BANNER) and the highest performing event extraction tools (e.g. from the BioNLP 2009 shared task). We replicated the original dataset to an acceptable degree, however with compromises devised to conform to the design of tools presently available. Notably, event extraction tools are limited to obtaining interactions from 9 event types, which are not compatible with all of the interactions in HIV biology. Thus, we have developed an appropriate ontology to encompass the full spectrum of host-pathogen interactions and demonstrate improved coverage. As a result, we demonstrate the effective use of sophisticated text mining tools to functionally recreate a large database of host-pathogen based interactions. This provides a platform for full-scale automated extraction of host-pathogen interactions from all the available literature (20 million+ online references). Not only will such an approach improve our understanding of host-pathogen biology, it may also permit informed development of pharmaceutical interventions.

Over the last ten years, the study of the properties of protein interaction networks (PINs) has become a major field of computational biology, having impact on a wide range of important biological questions, most notably in regard to the function and evolution of systems. Although deficiencies in the data are often recognized, adequate controls for bias in both high-throughput and literature-curated data are usually absent, rendering many studies of the topological properties of PINs inconclusive. Both noise and incompleteness are known to influence network properties, and sampling schemes capable of incorporating additional information have been proposed. However, null models to deal appropriately with the effects of ascertainment bias on network data are not currently available. Here we present a method to correct for ascertainment bias in literature-curated protein interaction networks, incorporating a statistical model of an idealized gene selection/ experiment system. The performance of the model is evaluated on synthetic data sets, and contrasted with the results obtained using a naive uniform random selection null model. Under the bemodel, well-studied proteins are reported to be associated with significantly higher degree, even when such an association is not present in the real network. However, when ascertainment bias is estimated by fitting the observed data to our model, a more appropriate hypothesis test can be implemented to correctly assess whether such an association is really present. With the growth of text mining as a source of primary data for applications.

Substantial amounts of information are curated from scientific literature. However, there is undoubtedly bias in scientific research and hence the resulting literature. analyses are reliant upon accurate data to draw conclusions; for instance, calculating the degree of a particular protein in a protein-interaction network. However, it is feasible that this protein, and hence its interactions, are affected by bias. Specifically, if highly studied, it is likely that more interactions will be known, distorting the observed network structure. To compensate, we devised a novel method to evaluate ascertainment bias based on a gene's publication count in PubMed. If a gene has a high publication count, it is inferred that it is highly studied. We used rejection sampling to generate sets of protein-protein interactions with the same patterns of bias. Applying this method to test hypotheses we find that rejection sampling performs favourably compared to randomly sampling directly from the unweighted distributions.

Scientific research, like many other social activities, is subject to trends. Certain research topics will become more attractive, exciting or fundable as public interests change and new experimental techniques are developed. Here we use GLASS (Gene LAyout by Semantic Similarity), a new methodology for data visualization in a whole-genome context, to explore research trends in yeast biology. Following the public release of the Saccharomyces cerevisiae genome in 1996, a rapid escalation of the number of publications per year is seen for many genes. However, this expansion is not uniform over the whole genome, revealing differences in the levels of scientific activity associated with different aspects of yeast biology.