Finding shortest and nearly shortest path nodes in large substantially incomplete networks

Dynamic processes on networks, be it information transfer in the Internet, contagious spreading in a social network, or neural signaling, take place along shortest or nearly shortest paths. Unfortunately, our maps of most large networks are substantially incomplete due to either the highly dynamic nature of networks, or high cost of network measurements, or both, rendering traditional path finding methods inefficient. We find that shortest paths in large real networks, such as the network of protein-protein interactions (PPI) and the Internet at the autonomous system (AS) level, are not random but are organized according to latent-geometric rules. If nodes of these networks are mapped to points in latent hyperbolic spaces, shortest paths in them align along geodesic curves connecting endpoint nodes. We find that this alignment is sufficiently strong to allow for the identification of shortest path nodes even in the case of substantially incomplete networks. We demonstrate the utility of latent-geometric path-finding in problems of cellular pathway reconstruction and communication security

Limnodrilus simplex sp nov (Oligochaeta: Naididae: Tubificinae) from Changjiang River, China

Limnodrilus simplex sp. nov. (Oligochaeta: Naididae: Tubificinae) is described based on a single specimen from the mainstream of the Changjiang River near Anqing City, Anhui Province, China. The new species is assigned to Limnodrilus by the presence of long vasa deferentia, spindle-shaped atria with long ejaculatory ducts, large prostate glands, and thick cylindrical penial sheaths. It differs from its congeners in having simple-pointed chaetae and cuticularized penial sheaths without hoods. Limnodrilus simplex is closer to L. paramblysetus and L. amblysetus in possessing penial sheaths with relatively low length/maximum width ratio

ATAV: a comprehensive platform for population-scale genomic analyses

Background A common approach for sequencing studies is to do joint-calling and store variants of all samples in a single file. If new samples are continually added or controls are re-used for several studies, the cost and time required to perform joint-calling for each analysis can become prohibitive. Results We present ATAV, an analysis platform for large-scale whole-exome and whole-genome sequencing projects. ATAV stores variant and per site coverage data for all samples in a centralized database, which is efficiently queried by ATAV to support diagnostic analyses for trios and singletons, as well as rare-variant collapsing analyses for finding disease associations in complex diseases. Runtime logs ensure full reproducibility and the modularized ATAV framework makes it extensible to continuous development. Besides helping with the identification of disease-causing variants for a range of diseases, ATAV has also enabled the discovery of disease-genes by rare-variant collapsing on datasets containing more than 20,000 samples. Analyses to date have been performed on data of more than 110,000 individuals demonstrating the scalability of the framework. To allow users to easily access variant-level data directly from the database, we provide a web-based interface, the ATAV data browser ( http://atavdb.org/ ). Through this browser, summary-level data for more than 40,000 samples can be queried by the general public representing a mix of cases and controls of diverse ancestries. Users have access to phenotype categories of variant carriers, as well as predicted ancestry, gender, and quality metrics. In contrast to many other platforms, the data browser is able to show data of newly-added samples in real-time and therefore evolves rapidly as more and more samples are sequenced. Conclusions Through ATAV, users have public access to one of the largest variant databases for patients sequenced at a tertiary care center and can look up any genes or variants of interest. Additionally, since the entire code is freely available on GitHub, ATAV can easily be deployed by other groups that wish to build their own platform, database, and user interface

DeteX: A highly accurate software for detecting SNV and InDel in single and paired NGS data in cancer research

Background: Genetic testing is becoming more and more accepted in the auxiliary diagnosis and treatment of tumors. Due to the different performance of the existing bioinformatics software and the different analysis results, the needs of clinical diagnosis and treatment cannot be met. To this end, we combined Bayesian classification model (BC) and fisher exact test (FET), and develop an efficient software DeteX to detect SNV and InDel mutations. It can detect the somatic mutations in tumor-normal paired samples as well as mutations in a single sample.Methods: Combination of Bayesian classification model (BC) and fisher exact test (FET).Results: We detected SNVs and InDels in 11 TCGA glioma samples, 28 clinically targeted capture samples and 2 NCCL-EQA standard samples with DeteX, VarDict, Mutect, VarScan and GatkSNV. The results show that, among the three groups of samples, DeteX has higher sensitivity and precision whether it detects SNVs or InDels than other callers and the F1 value of DeteX is the highest. Especially in the detection of substitution and complex mutations, only DeteX can accurately detect these two kinds of mutations. In terms of single-sample mutation detection, DeteX is much more sensitive than the HaplotypeCaller program in Gatk. In addition, although DeteX has higher mutation detection capabilities, its running time is only .609 of VarDict, which is .704 and .343 longer than VarScan and MuTect, respectively.Conclusion: In this study, we developed DeteX to detect SNV and InDel mutations in single and paired samples. DeteX has high sensitivity and precision especially in the detection of substitution and complex mutations. In summary, DeteX from NGS data is a good SNV and InDel caller

A desinstitucionalização e as alternativas habitacionais ao dispor de indivíduos com perturbações mentais: Um novo modelo habitacional – A habitação apoiada

Desde o início do processo de desinstitucionalização desinstitucionalização que este se tem vindo a deparar com dificuldades. Passando pelos poucos recursos ao dispor dos serviços de saúde mental, à tendência para trabalhar com os elementos que apresentam maiores probabilidades de sucesso, à não articulação entre os serviços hospitalares e os centros comunitários de saúde mental, até à falta de investimentos em alternativas habitacionais de carácter permanente. Estas têm sido algumas das situações a que os consumidores de serviços de saúde mental se têm sujeitado. Actualmente, assistimos à emergência de um paradigma que assenta na crença de que se deverá prestar apoio a estes consumidores numa casa tipicamente normal, com uma vivência na comunidade, em que o apoio é disponibilizado consoante as necessidades de cada indivíduo sem que exista uma limitação temporal à sua prestação. Torna-se assim necessário criar novos papéis para os técnicos, no sentido de que estes ajudem os consumidores a escolher, a obter, e a manter uma habitação. É pois urgente o desenvolvimento de um conjunto diversificado de alternativas habitacionais que se baseiem nos recursos e capacidades das comunidades locais, no sentido de garantir que o processo de desinstitucionalização se conclua com sucesso. Palavras-chave: desinstitucionalização, habitação apoiada, satisfação dos consumidores, doença mental.ABSTRACT: Since its beginning the deinstitutiondeinstitutionalization process has faced some difficulties, such as the mental health services lack of resources, the trend to work with the individual who presents higher probability of success, the lack of articulation between hospital services and community mental health centers, and the lack of investments on accommodations for long periods of time. These are some of the situations that consumers of mental health services have endured. Nowadays we witness the emerging of a paradigm which lies on the idea that these consumers need to be supported at a ordinary house, living in community, where the support is provided according to each person’s needs, and without a time limit. This paradigm also creates a need for the professionals to find new roles so that they will be able to help the consumers to choose, get, and keep a home. To ensure the success of deinstitutionalization it is vital that the establishment of different alternatives of accommodation be based on communities’ resources and capabilities.info:eu-repo/semantics/publishedVersio

In Silico Edgetic Profiling and Network Analysis of Human Genetic Variants, with an Application to Disease Module Detection

In the past several decades, Next Generation Sequencing (NGS) methods have produced large amounts of genomic data at the exponentially increasing rate. It has also enabled tremendous advancements in the quest to understand the molecular mechanisms underlying human complex traits. Along with the development of the NGS technology, many genetic variation and genotype–phenotype databases and functional annotation tools have been developed to assist scientists to better understand the intricacy of the data. Together, the above findings bring us one step closer towards mechanistic understanding of the complex phenotypes. However, it has rarely been possible to translate such a massive amount of information on mutations and their associations with phenotypes into biological or therapeutic insights, and the mechanisms underlying genotype-phenotype relationships remain partially explained. Meanwhile, increasing evidence shows that biological networks are essential, albeit not sufficient, for the better understanding of these mechanisms. Among them, protein- protein interaction (PPI) network studies have attracted perhaps most attention. Our overarching goal of this dissertation is to (i) perform a systematic study to investigate the role of pathogenic human genetic variant in the interactome; (ii) examine how common population-specific SNVs affect PPI network and how they contribute to population phenotypic variance and disease susceptibility; and (iii) develop a novel framework to incorporate the functional effect of mutations for disease module detection.\n\nIn this dissertation, we first present a systematic multi-level characterization of human mutations associated with genetic disorders by determining their individual and combined interaction-rewiring effects on the human interactome. Our in-silico analysis highlights the intrinsic differences and important similarities between the pathogenic single nucleotide variants (SNVs) and frameshift mutations. Functional profiling of SNVs indicates widespread disruption of the protein-protein interactions and synergistic effects of SNVs. The coverage of our approach is several times greater than the recently published experimental study and has the minimal overlap with it, while the distributions of determined edgotypes between the two sets of profiled mutations are remarkably similar. Case studies reveal the central role of interaction- disrupting mutations in type 2 diabetes mellitus and suggest the importance of studying mutations that abnormally strengthen the protein interactions in cancer.\n\nSecond, aided with our SNP-IN tool, we performed a systematic edgetic profiling of population specific non-synonymous SNVs and interrogate their role in the human interactome. Our results demonstrated that a considerable amount of normal nsSNVs can cause disruptive impact to the interactome. We also showed that genes enriched with disruptive mutations associated with diverse functions and have implications in various diseases. Further analysis indicates that distinct gene edgetic profiles among major populations can help explain the population phenotypic variance. Finally, network analysis reveals phenotype-associated modules are enriched with disruptive mutations and the difference of the accumulated damage in such modules may suggest population-specific disease susceptibility.\n\nLastly, we propose and develop a computational framework, Discovering most IMpacted SUbnetworks in interactoMe (DIMSUM), which enables the integration of genome-wide association studies (GWAS) and functional effects of mutations into the protein–protein interaction (PPI) network to improve disease module detection. Specifically, our approach incorporates and propagates the functional impact of non- synonymous single nucleotide polymorphisms (nsSNPs) on PPIs to implicate the genes that are most likely influenced by the disruptive mutations, and to identify the module with the greatest functional impact. Comparison against state-of-the-art seed-based module detection methods shows that our approach could yield modules that are biologically more relevant and have stronger association with the studied disease.\n\nWith the advancement of next-generation sequencing technology that drives precision medicine, there is an increasing demand in understanding the changes in molecular mechanisms caused by the specific genetic variation. The current and future in-silico edgotyping tools present a cheap and fast solution to deal with the rapidly growing datasets of discovered mutations. Our work shows the feasibility of a large- scale in-silico edgetic study and revealing insights into the orchestrated play of mutations inside a complex PPI network. We also expect for our module detection method to become a part of the common toolbox for the disease module analysis, facilitating the discovery of new disease markers

Enriching Human Interactome with Functional Mutations to Detect High-Impact Network Modules Underlying Complex Diseases

Rapid progress in high-throughput -omics technologies moves us one step closer to the datacalypse in life sciences. In spite of the already generated volumes of data, our knowledge of the molecular mechanisms underlying complex genetic diseases remains limited. Increasing evidence shows that biological networks are essential, albeit not sufficient, for the better understanding of these mechanisms. The identification of disease-specific functional modules in the human interactome can provide a more focused insight into the mechanistic nature of the disease. However, carving a disease network module from the whole interactome is a difficult task. In this paper, we propose a computational framework, Discovering most IMpacted SUbnetworks in interactoMe (DIMSUM), which enables the integration of genome-wide association studies (GWAS) and functional effects of mutations into the protein–protein interaction (PPI) network to improve disease module detection. Specifically, our approach incorporates and propagates the functional impact of non-synonymous single nucleotide polymorphisms (nsSNPs) on PPIs to implicate the genes that are most likely influenced by the disruptive mutations, and to identify the module with the greatest functional impact. Comparison against state-of-the-art seed-based module detection methods shows that our approach could yield modules that are biologically more relevant and have stronger association with the studied disease. We expect for our method to become a part of the common toolbox for the disease module analysis, facilitating the discovery of new disease markers

Boosting Gene Expression Clustering with System-Wide Biological Information and Deep Learning

Gene expression analysis provides genome-wide insights into the transcriptional activity of a cell. One of the first computational steps in exploration and analysis of the gene expression data is clustering. With a number of standard clustering methods routinely used, most of the methods do not take prior biological information into account. Here, we propose a new approach for gene expression clustering analysis. The approach benefits from a new deep learning architecture, Robust Autoencoder, which provides a more accurate high-level representation of the feature sets, and from incorporating prior system-wide biological information into the clustering process. We tested our approach on two gene expression datasets and compared the performance with two widely used clustering methods, hierarchical clustering and k-means, and with a recent deep learning clustering approach. Our approach outperformed all other clustering methods on the labeled yeast gene expression dataset. Furthermore, we showed that it is better in identifying the functionally common clusters than k-means on the unlabeled human gene expression dataset. The results demonstrate that our new deep learning architecture can generalize well the specific properties of gene expression profiles. Furthermore, the results confirm our hypothesis that the prior biological network knowledge is helpful in the gene expression clustering

Three new species of Potamothrix (Oligochaeta, Naididae, Tubificinae) from Fuxian Lake, the deepest lake of Yunnan Province, Southwest China

Three new species of Potamothrix Vejdovský & Mrázek, 1902 (Oligochaeta: Tubificinae), P. praeprostatus sp. n., P. paramoldaviensis sp. n. and P. parabedoti sp. n., are reported from Fuxian Lake of Yunnan Province, Southwest China. P. praeprostatus differs from its allies by its prostate glands joining atria in its proximal to middle portion, and spermathecal chaetae. P. paramoldaviensis is distinguishable from its allies bypenial chaeta but no penes, and differs from P. moldaviensis by its homogenous atrium. P. parabedoti is distinctive in the position of its reproductive organs, and differs from P. bedoti by its homogenous atrium. Hitherto, 34 freshwater oligochaete species have been recorded in Yunnan Province, including nine endemic species from the plateau lakes