Sources Of Biological Information Research Articles

Constructing disease onset signatures using multi-dimensional network-structured biomarkers

Potential disease-modifying therapies for neurodegenerative disorders need to be introduced prior to the symptomatic stage in order to be effective. However, current diagnosis of neurological disorders mostly rely on measurements of clinical symptoms and thus only identify symptomatic subjects in their late disease course. Thus, it is of interest to select and integrate biomarkers that may reflect early disease-related pathological changes for earlier diagnosis and recruiting pre-sypmtomatic subjects in a prevention clinical trial. Two sources of biological information are relevant to the construction of biomarker signatures for time to disease onset that is subject to right censoring. First, biomarkers' effects on disease onset may vary with a subject's baseline disease stage indicated by a particular marker. Second, biomarkers may be connected through networks, and their effects on disease may be informed by this network structure. To leverage these information, we propose a varying-coefficient hazards model to induce double smoothness over the dimension of the disease stage and over the space of network-structured biomarkers. The distinctive feature of the model is a non-parametric effect that captures non-linear change according to the disease stage and similarity among the effects of linked biomarkers. For estimation and feature selection, we use kernel smoothing of a regularized local partial likelihood and derive an efficient algorithm. Numeric simulations demonstrate significant improvements over existing methods in performance and computational efficiency. Finally, the methods are applied to our motivating study, a recently completed study of Huntington's disease (HD), where structural brain imaging measures are used to inform age-at-onset of HD and assist clinical trial design. The analysis offers new insights on the structural network signatures for premanifest HD subjects.

"Gestaltomics": Systems Biology Schemes for the Study of Neuropsychiatric Diseases.

The integration of different sources of biological information about what defines a behavioral phenotype is difficult to unify in an entity that reflects the arithmetic sum of its individual parts. In this sense, the challenge of Systems Biology for understanding the “psychiatric phenotype” is to provide an improved vision of the shape of the phenotype as it is visualized by “Gestalt” psychology, whose fundamental axiom is that the observed phenotype (behavior or mental disorder) will be the result of the integrative composition of every part. Therefore, we propose the term “Gestaltomics” as a term from Systems Biology to integrate data coming from different sources of information (such as the genome, transcriptome, proteome, epigenome, metabolome, phenome, and microbiome). In addition to this biological complexity, the mind is integrated through multiple brain functions that receive and process complex information through channels and perception networks (i.e., sight, ear, smell, memory, and attention) that in turn are programmed by genes and influenced by environmental processes (epigenetic). Today, the approach of medical research in human diseases is to isolate one disease for study; however, the presence of an additional disease (co-morbidity) or more than one disease (multimorbidity) adds complexity to the study of these conditions. This review will present the challenge of integrating psychiatric disorders at different levels of information (Gestaltomics). The implications of increasing the level of complexity, for example, studying the co-morbidity with another disease such as cancer, will also be discussed.

An Improved Archaeology Algorithm Based on Integrated Multi-Source Biological Information for Yeast Protein Interaction Network

With the development of high-throughput interaction detection techniques such as tandem affinity purification (TAP) and yeast two-hybrid (Y2H), the available genome-wide protein–protein interactions (PPIs) data have been increasing in recent years. Using mathematical, physical, and artificial-intelligence methods, some researchers in computational biology focused on uncovering the evolutionary ages of proteins according to present PPI networks (PINs), but improving their accuracy was challenging. A plausible explanation is that they solved biological problems with non-biological techniques and did not provide much attention to biological backgrounds and meanings of proteins or their relationships. In this paper, we propose two ways to improve the accuracy of age predicting and skillfully “embedding” multi-source biological information in each iteration of an archaeology algorithm for yeast PIN. On the one hand, we reduce the probability of reversing errors by decreasing the non-duplication protein pairs, which are obtained from 460 gene trees constructed by means of a multiple sequence alignment and the neighbor joining algorithm. On the other hand, the reliable crossover standard from different biological information sources can decrease local random errors of alternative treatment. The application of the novel algorithm to simulation data and real yeast PINs shows a marked improvement in accuracy. Our research strongly suggests that putting non-biological methods into the “biological context” will bear more favorable results.

Identification of protein complexes from multi-relationship protein interaction networks.

BackgroundProtein complexes play an important role in biological processes. Recent developments in experiments have resulted in the publication of many high-quality, large-scale protein-protein interaction (PPI) datasets, which provide abundant data for computational approaches to the prediction of protein complexes. However, the precision of protein complex prediction still needs to be improved due to the incompletion and noise in PPI networks.ResultsThere exist complex and diverse relationships among proteins after integrating multiple sources of biological information. Considering that the influences of different types of interactions are not the same weight for protein complex prediction, we construct a multi-relationship protein interaction network (MPIN) by integrating PPI network topology with gene ontology annotation information. Then, we design a novel algorithm named MINE (identifying protein complexes based on Multi-relationship protein Interaction NEtwork) to predict protein complexes with high cohesion and low coupling from MPIN.ConclusionsThe experiments on yeast data show that MINE outperforms the current methods in terms of both accuracy and statistical significance.

A graph-based integrative method of detecting consistent protein functional modules from multiple data sources.

Many clustering methods have been developed to identify functional modules in Protein-Protein Interaction (PPI) networks but the results are far from satisfaction. To overcome the noise and incomplete problems of PPI networks and find more accurate and stable functional modules, we propose an integrative method, bipartite graph-based Non-negative Matrix Factorisation method (BiNMF), in which we adopt multiple biological data sources as different views that describe PPIs. Specifically, traditional clustering models are adopted as preliminary analysis of different views of protein functional similarity. Then the intermediate clustering results are represented by a bipartite graph which can comprehensively represent the relationships between proteins and intermediate clusters and finally overlapping clustering results are achieved. Through extensive experiments, we see that our method is superior to baseline methods and detailed analysis has demonstrated the benefits of integrating diverse clustering methods and multiple biological information sources.

Boosting probabilistic graphical model inference by incorporating prior knowledge from multiple sources.

Inferring regulatory networks from experimental data via probabilistic graphical models is a popular framework to gain insights into biological systems. However, the inherent noise in experimental data coupled with a limited sample size reduces the performance of network reverse engineering. Prior knowledge from existing sources of biological information can address this low signal to noise problem by biasing the network inference towards biologically plausible network structures. Although integrating various sources of information is desirable, their heterogeneous nature makes this task challenging. We propose two computational methods to incorporate various information sources into a probabilistic consensus structure prior to be used in graphical model inference. Our first model, called Latent Factor Model (LFM), assumes a high degree of correlation among external information sources and reconstructs a hidden variable as a common source in a Bayesian manner. The second model, a Noisy-OR, picks up the strongest support for an interaction among information sources in a probabilistic fashion. Our extensive computational studies on KEGG signaling pathways as well as on gene expression data from breast cancer and yeast heat shock response reveal that both approaches can significantly enhance the reconstruction accuracy of Bayesian Networks compared to other competing methods as well as to the situation without any prior. Our framework allows for using diverse information sources, like pathway databases, GO terms and protein domain data, etc. and is flexible enough to integrate new sources, if available.

Cognition and biology: perspectives from information theory

The intimate relation between biology and cognition can be formally examined through statistical models constrained by the asymptotic limit theorems of communication theory, augmented by methods from statistical mechanics and nonequilibrium thermodynamics. Cognition, often involving submodules that act as information sources, is ubiquitous across the living state. Less metabolic free energy is consumed by permitting crosstalk between biological information sources than by isolating them, leading to evolutionary exaptations that assemble shifting, tunable cognitive arrays at multiple scales, and levels of organization to meet dynamic patterns of threat and opportunity. Cognition is thus necessary for life, but it is not sufficient: An organism represents a highly patterned outcome of path-dependent, blind, variation, selection, interaction, and chance extinction in the context of an adequate flow of free energy and an environment fit for development. Complex, interacting cognitive processes within an organism both record and instantiate those evolutionary and developmental trajectories.

Structure and Evolution of Human Sirtuins

Sirtuins form a large homology family of enzymes found almost ubiquitously in living organisms and involved in numerous biological processes. The human genome encodes for seven paralog sirtuins, identified as SIRT1-7. In this review the major structural features of the sirtuin catalytic domain are illustrated and the relevant sources of biological information indicated. The multiple sequence alignment deduced from the optimal structural superposition of four human sirtuins having known three-dimensional structure, to which the amino acid sequences of the remaining three have been subsequently aligned, is also analyzed. The structure of the neighbor-joining tree deduced from the multiple sequence alignment, summarizing the evolutionary relationship among the member of the homology family is illustrated also in relation with the distinct catalytic activities detected in members this homology family.

Protein function prediction by massive integration of evolutionary analyses and multiple data sources

BackgroundAccurate protein function annotation is a severe bottleneck when utilizing the deluge of high-throughput, next generation sequencing data. Keeping database annotations up-to-date has become a major scientific challenge that requires the development of reliable automatic predictors of protein function. The CAFA experiment provided a unique opportunity to undertake comprehensive 'blind testing' of many diverse approaches for automated function prediction. We report on the methodology we used for this challenge and on the lessons we learnt.MethodsOur method integrates into a single framework a wide variety of biological information sources, encompassing sequence, gene expression and protein-protein interaction data, as well as annotations in UniProt entries. The methodology transfers functional categories based on the results from complementary homology-based and feature-based analyses. We generated the final molecular function and biological process assignments by combining the initial predictions in a probabilistic manner, which takes into account the Gene Ontology hierarchical structure.ResultsWe propose a novel scoring function called COmbined Graph-Information Content similarity (COGIC) score for the comparison of predicted functional categories and benchmark data. We demonstrate that our integrative approach provides increased scope and accuracy over both the component methods and the naïve predictors. In line with previous studies, we find that molecular function predictions are more accurate than biological process assignments.ConclusionsOverall, the results indicate that there is considerable room for improvement in the field. It still remains for the community to invest a great deal of effort to make automated function prediction a useful and routine component in the toolbox of life scientists. As already witnessed in other areas, community-wide blind testing experiments will be pivotal in establishing standards for the evaluation of prediction accuracy, in fostering advancements and new ideas, and ultimately in recording progress.

Semantics-driven modelling of user preferences for information retrieval in the biomedical domain

A large amount of biomedical and genomic data are currently available on the Internet. However, data are distributed into heterogeneous biological information sources, with little or even no organization. Semantic technologies provide a consistent and reliable basis with which to confront the challenges involved in the organization, manipulation and visualization of data and knowledge. One of the knowledge representation techniques used in semantic processing is the ontology, which is commonly defined as a formal and explicit specification of a shared conceptualization of a domain of interest. The work presented here introduces a set of interoperable algorithms that can use domain and ontological information to improve information-retrieval processes. This work presents an ontology-based information-retrieval system for the biomedical domain. This system, with which some experiments have been carried out that are described in this paper, is based on the use of domain ontologies for the creation and normalization of lightweight ontologies that represent user preferences in a determined domain in order to improve information-retrieval processes.

GeneCodis3: a non-redundant and modular enrichment analysis tool for functional genomics

Since its first release in 2007, GeneCodis has become a valuable tool to functionally interpret results from experimental techniques in genomics. This web-based application integrates different sources of information to finding groups of genes with similar biological meaning. This process, known as enrichment analysis, is essential in the interpretation of high-throughput experiments. The frequent feedbacks and the natural evolution of genomics and bioinformatics have allowed the growth of the tool and the development of this third release. In this version, a special effort has been made to remove noisy and redundant output from the enrichment results with the inclusion of a recently reported algorithm that summarizes significantly enriched terms and generates functionally coherent modules of genes and terms. A new comparative analysis has been added to allow the differential analysis of gene sets. To expand the scope of the application, new sources of biological information have been included, such as genetic diseases, drugs–genes interactions and Pubmed information among others. Finally, the graphic section has been renewed with the inclusion of new interactive graphics and filtering options. The application is freely available at http://genecodis.cnb.csic.es.

Improving ChIP-seq peak-calling for functional co-regulator binding by integrating multiple sources of biological information

BackgroundChromatin immunoprecipitation coupled with massively parallel sequencing (ChIP-seq) is increasingly being applied to study genome-wide binding sites of transcription factors. There is an increasing interest in understanding the mechanism of action of co-regulator proteins, which do not bind DNA directly, but exert their effects by binding to transcription factors such as the estrogen receptor (ER). However, due to the nature of detecting indirect protein-DNA interaction, ChIP-seq signals from co-regulators can be relatively weak and thus biologically meaningful interactions remain difficult to identify.ResultsIn this study, we investigated and compared different statistical and machine learning approaches including unsupervised, supervised, and semi-supervised classification (self-training) approaches to integrate multiple types of genomic and transcriptomic information derived from our experiments and public database to overcome difficulty of identifying functional DNA binding sites of the co-regulator SRC-1 in the context of estrogen response. Our results indicate that supervised learning with naïve Bayes algorithm significantly enhances peak calling of weak ChIP-seq signals and outperforms other machine learning algorithms. Our integrative approach revealed many potential ERα/SRC-1 DNA binding sites that would otherwise be missed by conventional peak calling algorithms with default settings.ConclusionsOur results indicate that a supervised classification approach enables one to utilize limited amounts of prior knowledge together with multiple types of biological data to enhance the sensitivity and specificity of the identification of DNA binding sites from co-regulator proteins.

Large margin classifiers and Random Forests for integrated biological prediction

Incorporating various sources of biological information is important for biological discovery. For example, genes have a multiview representation. They can be represented by features such as sequence length and pairwise similarities. Hence, the types vary from numerical features to categorical features. We propose a large margin Random Forests (RF) classification approach based on RF proximity kernals. Random Forests accommodate mixed data types naturally. The performance on four biological datasets is promising compared with other state of the art methods including Support Vector Machines (SVMs) and RF classifiers. It demonstrates high potential in the discovery of functional roles of biomolecules.

ADGO 2.0: interpreting microarray data and list of genes using composite annotations

ADGO 2.0 is a web-based tool that provides composite interpretations for microarray data comparing two sample groups as well as lists of genes from diverse sources of biological information. Some other tools also incorporate composite annotations solely for interpreting lists of genes but usually provide highly redundant information. This new version has the following additional features: first, it provides multiple gene set analysis methods for microarray inputs as well as enrichment analyses for lists of genes. Second, it screens redundant composite annotations when generating and prioritizing them. Third, it incorporates union and subtracted sets as well as intersection sets. Lastly, users can upload their own gene sets (e.g. predicted miRNA targets) to generate and analyze new composite sets. The first two features are unique to ADGO 2.0. Using our tool, we demonstrate analyses of a microarray dataset and a list of genes for T-cell differentiation. The new ADGO is available at http://www.btool.org/ADGO2.

Integrating Different Biological Evidence Around Some Microevolutionary Processes: Bottlenecks and Asian-American Arctic Gene Flow in the New World Settlement

Excepting some specific efforts, most of the mainstream debate around the Americas’ settlement has been directed by specialists dealing with partial evidence. Thus, discussions have been confined to particular academic and scientific environments with limited interchange among archeologists, physical anthropologists, linguists, geneticists, geologists, paleontologists, and so on. As a consequence, integrative views about a process that is complex by definition have been scarce and driven by confrontation rather than by a search for common results. Still, an increasing number of specialists are attempting to integrate different types of data. In our view, a proper way to do this is to focus the discussion around evolutionary or cultural processes and the putative patterns that such processes could have generated in the different types of data, which in turn, depend on the nature of the data. In this way, the analyses and conclusions can be interpreted as “model-bound” rather than purely inferential. In this paper, we first provide a brief summary of main differences among the two main sources of biological information—genetics and craniofacial size and shape—along with the main conclusions that the patterns of genetic and craniofacial variation provide. Furthermore, we exemplify the above-mentioned notion by discussing two particular processes and their hypothetical impact on genetic and craniofacial data: the influence of bottlenecks during the early dispersal and a putative zone of gene flow among Asian and American Circum-Arctic populations.

Combining Drug and Gene Similarity Measures for Drug-Target Elucidation

Understanding drugs and their modes of action is a fundamental challenge in systems medicine. Key to addressing this challenge is the elucidation of drug targets, an important step in the search for new drugs or novel targets for existing drugs. Incorporating multiple biological information sources is of essence for improving the accuracy of drug target prediction. In this article, we introduce a novel framework--Similarity-based Inference of drug-TARgets (SITAR)--for incorporating multiple drug-drug and gene-gene similarity measures for drug target prediction. The framework consists of a new scoring scheme for drug-gene associations based on a given pair of drug-drug and gene-gene similarity measures, combined with a logistic regression component that integrates the scores of multiple measures to yield the final association score. We apply our framework to predict targets for hundreds of drugs using both commonly used and novel drug-drug and gene-gene similarity measures and compare our results to existing state of the art methods, markedly outperforming them. We then employ our framework to make novel target predictions for hundreds of drugs; we validate these predictions via curated databases that were not used in the learning stage. Our framework provides an extensible platform for incorporating additional emerging similarity measures among drugs and genes. Supplementary Material is available at www.liebertonline.com/cmb.

Protein‐protein interactions: Making sense of networks via graph‐theoretic modeling

The emerging area of network biology is seeking to provide insights into organizational principles of life. However, despite significant collaborative efforts, there is still typically a weak link between biological and computational scientists and a lack of understanding of the research issues across the disciplines. This results in the use of simple computational techniques of limited potential that are incapable of explaining these complex data. Hence, the danger is that the community might begin to view the topological properties of network data as mere statistics, rather than rich sources of biological information. A further danger is that such views might result in the imposition of scientific doctrines, such as scale-free-centric (on the modeling side) and genome-centric (on the biological side) opinions onto this area. Here, we take a graph-theoretic perspective on protein-protein interaction networks and present a high-level overview of the area, commenting on possible challenges ahead.

Identification of viral DNA (Anelloviridae) in a 200-year-old dental pulp sample (Napoleon's Great Army, Kaliningrad, 1812)

Ancient human remains are potential sources of biological information including traces of past infections, since previous studies have demonstrated the effective detection of several bacterial agents or host-integrated viruses in old biological remnants like tissues or teeth. Studies of skeletal dental pulp samples are of particular interest since this location is potentially exposed to bloodborne agents during life through its vascularization, and could be considered as well preserved from environment after death of the host. DNA viruses belonging to the family Anelloviridae are highly present in human populations where they harbor an extreme genetic diversity but a yet undefined implication in hosts’ health. We hypothesized that anelloviruses would be detected in ancient remains and that they may also serve as tracer viruses for the study of other viral agents. We analyzed 200-year-old dental pulp samples from remains of soldiers of Napoleon's Great Army during the Russian Retreat. Successful detection of Anelloviridae DNA by PCR was obtained for 1/21 ancient samples tested. The sequence identified showed 23% nucleotide divergence with the closest group of modern isolates (genus Gammatorquevirus), and was confirmed as phylogenetically distinct from those identified in saliva samples from the two investigators in charge of the study (genera Alphatorquevirus and Betatorquevirus). PCR directed toward the human beta globin gene was also performed. Negative controls were negative. Our results demonstrate that an ubiquitary, non-integrated, DNA virus is detectable from ancient biological material, with potential developments in terms of evolution studies or subsequent molecular investigations involving further viral agents.

Complementarity of network and sequence information in homologous proteins

Summary Traditional approaches for homology detection rely on finding sufficient similarities between protein sequences. Motivated by studies demonstrating that from non-sequence based sources of biological information, such as the secondary or tertiary molecular structure, we can extract certain types of biological knowledge when sequence-based approaches fail, we hypothesize that protein-protein interaction (PPI) network topology and protein sequence might give insights into different slices of biological information. Since proteins aggregate to perform a function instead of acting in isolation, analyzing complex wirings around a protein in a PPI network could give deeper insights into the protein’s role in the inner working of the cell than analyzing sequences of individual genes. Hence, we believe that one could lose much information by focusing on sequence information alone. We examine whether the information about homologous proteins captured by PPI network topology differs and to what extent from the information captured by their sequences. We measure how similar the topology around homologous proteins in a PPI network is and show that such proteins have statistically significantly higher network similarity than nonhomologous proteins. We compare these network similarity trends of homologous proteins with the trends in their sequence identity and find that network similarities uncover almost as much homology as sequence identities. Although none of the two methods, network topology and sequence identity, seems to capture homology information in its entirety, we demonstrate that the two might give insights into somewhat different types of biological information, as the overlap of the homology information that they uncover is relatively low. Therefore, we conclude that similarities of proteins’ topological neighborhoods in a PPI network could be used as a complementary method to sequence-based approaches for identifying homologs, as well as for analyzing evolutionary distance and functional divergence of homologous proteins.

Babelomics: an integrative platform for the analysis of transcriptomics, proteomics and genomic data with advanced functional profiling

Babelomics is a response to the growing necessity of integrating and analyzing different types of genomic data in an environment that allows an easy functional interpretation of the results. Babelomics includes a complete suite of methods for the analysis of gene expression data that include normalization (covering most commercial platforms), pre-processing, differential gene expression (case-controls, multiclass, survival or continuous values), predictors, clustering; large-scale genotyping assays (case controls and TDTs, and allows population stratification analysis and correction). All these genomic data analysis facilities are integrated and connected to multiple options for the functional interpretation of the experiments. Different methods of functional enrichment or gene set enrichment can be used to understand the functional basis of the experiment analyzed. Many sources of biological information, which include functional (GO, KEGG, Biocarta, Reactome, etc.), regulatory (Transfac, Jaspar, ORegAnno, miRNAs, etc.), text-mining or protein–protein interaction modules can be used for this purpose. Finally a tool for the de novo functional annotation of sequences has been included in the system. This provides support for the functional analysis of non-model species. Mirrors of Babelomics or command line execution of their individual components are now possible. Babelomics is available at http://www.babelomics.org.