Functional organization and its implication in evolution of the human protein-protein interaction network

<p>Abstract</p> <p>Background</p> <p>Based on the distinguishing properties of protein-protein interaction networks such as power-law degree distribution and modularity structure, several stochastic models for the evolution of these networks have been purposed, motivated by the idea that a validated model should reproduce similar topological properties of the empirical network. However, being able to capture topological properties does not necessarily mean it correctly reproduces how networks emerge and evolve. More importantly, there is already evidence suggesting functional organization and significance of these networks. The current stochastic models of evolution, however, grow the network without consideration for biological function and natural selection.</p> <p>Results</p> <p>To test whether protein interaction networks are functionally organized and their impacts on the evolution of these networks, we analyzed their evolution at both the topological and functional level. We find that the human network is shown to be functionally organized, and its function evolves with the topological properties of the network. Our analysis suggests that function most likely affects local modularity of the network. Consistently, we further found that the topological unit is also the functional unit of the network.</p> <p>Conclusion</p> <p>We have demonstrated functional organization of a protein interaction network. Given our observations, we suggest that its significance should not be overlooked when studying network evolution.</p

The functional importance of disease-associated mutation

BACKGROUND: For many years, scientists believed that point mutations in genes are the genetic switches for somatic and inherited diseases such as cystic fibrosis, phenylketonuria and cancer. Some of these mutations likely alter a protein's function in a manner that is deleterious, and they should occur in functionally important regions of the protein products of genes. Here we show that disease-associated mutations occur in regions of genes that are conserved, and can identify likely disease-causing mutations. RESULTS: To show this, we have determined conservation patterns for 6185 non-synonymous and heritable disease-associated mutations in 231 genes. We define a parameter, the conservation ratio, as the ratio of average negative entropy of analyzable positions with reported mutations to that of every analyzable position in the gene sequence. We found that 84.0% of the 231 genes have conservation ratios less than one. 139 genes had eleven or more analyzable mutations and 88.0% of those had conservation ratios less than one. CONCLUSIONS: These results indicate that phylogenetic information is a powerful tool for the study of disease-associated mutations. Our alignments and analysis has been made available as part of the database at http://cancer.stanford.edu/mut-paper/. Within this dataset, each position is annotated with the analysis, so the most likely disease-causing mutations can be identified

Bioinformatics

This article is made available for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.After reading this chapter, you should know the answers to these questions: Why is sequence, structure, and biological pathway information relevant to medicine? Where on the Internet should you look for a DNA sequence, a protein sequence, or a protein structure? What are two problems encountered in analyzing biological sequence, structure, and function? How has the age of genomics changed the landscape of bioinformatics? What two changes should we anticipate in the medical record as a result of these new information sources? What are two computational challenges in bioinformatics for the future

Translating Bioinformatics Back To Healthcare: Facilitating the use of Artificial Intelligence at UW Medicine

It is an opportune time to be engaged in the research and application of informatics in biomedicine. The increased use of electronic and personal health records and personal mobile devices is creating many opportunities at research academic medical centers. At the University of Washington, I believe we are laying the groundwork to build the informatics and information technology infrastructure to support research on personalized approaches and the use of data science to enable them. We are beginning to see the early successes of these efforts and I will describe some of them. But there are many challenges, for example, we continue to generate massive amounts of data that is largely uncurated. This includes images, genomes and other -omics datasets, personal monitors, electronic health records, etc. In this presentation, I will discuss our support of data for research use within UW Medicine, our efforts to build new machine learning and data science approaches using clinical datasets, and our efforts to develop new machine learning methods and to implement them so that we can study the impacts of their use.Book of abstract: 4th Belgrade Bioinformatics Conference, June 19-23, 202

The Naas Motorway Bypass - A Cost Benefit Analysis. Quarterly Economic Commentary Special Article, January 1984

The paper examines the Naas Motorway Bypass which cost £16m at 1983 prices. Twelve thousand vehicles a day using the bypass save over 10 minutes between 8 am and 8 pm and 6 minutes at other times. Five thousand vehicles a day using the present route through Naas also benefit by saving 4 minutes due to reduced congestion in the town. In addition to time savings, the bypass reduces accidents and fuel costs. Ninety-one per cent of the benefits accrue in time savings. The internal rate of return on the project is estimated at 20.51 per cent, assuming 2 per cent annual traffic and income growth. The sensitivity tests of the results show that even with zero growth in incomes and traffic for twenty years, a high proportion of leisure time savings with zero value and no increase in the value of fuel savings the project would have an internal rate of return which meets the test discount rate used by the Department of Finance. The environmental aspects of the bypass are positive in terms of noise and smoke and lead pollution reduction. The impact on farm severence and natural amenities on the motorway route has been mitigated by several design features of the bypass

A Multifaceted Benchmarking of Synthetic Electronic Health Record Generation Models

Synthetic health data have the potential to mitigate privacy concerns when sharing data to support biomedical research and the development of innovative healthcare applications. Modern approaches for data generation based on machine learning, generative adversarial networks (GAN) methods in particular, continue to evolve and demonstrate remarkable potential. Yet there is a lack of a systematic assessment framework to benchmark methods as they emerge and determine which methods are most appropriate for which use cases. In this work, we introduce a generalizable benchmarking framework to appraise key characteristics of synthetic health data with respect to utility and privacy metrics. We apply the framework to evaluate synthetic data generation methods for electronic health records (EHRs) data from two large academic medical centers with respect to several use cases. The results illustrate that there is a utility-privacy tradeoff for sharing synthetic EHR data. The results further indicate that no method is unequivocally the best on all criteria in each use case, which makes it evident why synthetic data generation methods need to be assessed in context

The loss and gain of functional amino acid residues is a common mechanism causing human inherited disease

Elucidating the precise molecular events altered by disease-causing genetic variants represents a major challenge in translational bioinformatics. To this end, many studies have investigated the structural and functional impact of amino acid substitutions. Most of these studies were however limited in scope to either individual molecular functions or were concerned with functional effects (e.g. deleterious vs. neutral) without specifically considering possible molecular alterations. The recent growth of structural, molecular and genetic data presents an opportunity for more comprehensive studies to consider the structural environment of a residue of interest, to hypothesize specific molecular effects of sequence variants and to statistically associate these effects with genetic disease. In this study, we analyzed data sets of disease-causing and putatively neutral human variants mapped to protein 3D structures as part of a systematic study of the loss and gain of various types of functional attribute potentially underlying pathogenic molecular alterations. We first propose a formal model to assess probabilistically function-impacting variants. We then develop an array of structure-based functional residue predictors, evaluate their performance, and use them to quantify the impact of disease-causing amino acid substitutions on catalytic activity, metal binding, macromolecular binding, ligand binding, allosteric regulation and post-translational modifications. We show that our methodology generates actionable biological hypotheses for up to 41% of disease-causing genetic variants mapped to protein structures suggesting that it can be reliably used to guide experimental validation. Our results suggest that a significant fraction of disease-causing human variants mapping to protein structures are function-altering both in the presence and absence of stability disruption

Using RNase sequence specificity to refine the identification of RNA-protein binding regions

Massively parallel pyrosequencing is a high-throughput technology that can sequence hundreds of thousands of DNA/RNA fragments in a single experiment. Combining it with immunoprecipitation-based biochemical assays, such as cross-linking immunoprecipitation (CLIP), provides a genome-wide method to detect the sites at which proteins bind DNA or RNA. In a CLIP-pyrosequencing experiment, the resolutions of the detected protein binding regions are partially determined by the length of the detected RNA fragments (CLIP amplicons) after trimming by RNase digestion. The lengths of these fragments usually range from 50-70 nucleotides. Many genomic regions are marked by multiple RNA fragments. In this paper, we report an empirical approach to refine the localization of protein binding regions by using the distribution pattern of the detected RNA fragments and the sequence specificity of RNase digestion. We present two regions to which multiple amplicons map as examples to demonstrate this approach