SEARCH · PubMed Health

Results for “Graph genome”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 325 records · Page 18Linked to original sources

PubMed2004

Inference on recombination and block structure using unphased data.

In this study compatibility with a tree for unphased genotype data is discussed. If the data are compatible with a tree, the data are consistent with an assumption of no recombination in its evolutionary history. Further, it is said that there is a solution to the perfect phylogeny problem; i.e., for each individual a pair of haplotypes can be defined and the set of all haplotypes can be explained without invoking recombination. A new algorithm to decide whether or not a sample is compatible with a tree is derived. The new algorithm relies on an equivalence relation between sites that mutually determine the phase of each other. (The previous algorithm was based on advanced graph theoretical tools.) The equivalence relation is used to derive the number of solutions to the perfect phylogeny problem. Further, a series of statistics, R ( j ) ( M ), j >or= 2, are defined. These can be used to detect recombination events in the sample's history and to divide the sample into regions that are compatible with a tree. The new statistics are applied to real data from human genes. The results from this application are discussed with reference to recent suggestions that recombination in the human genome is highly heterogeneous.

Biometry↗

PubMed2004

Visualization of microarray results to assist interpretation.

Whole genome microarrays allow assessment of the profile of genes expressed under particular experimental conditions, including external stimuli such as pH or temperature, and internal changes brought about by deleting or over-expressing a gene. Such experiments produce large data sets, for which sophisticated analysis software is available. What is lacking are tools for analysing data sets from different experiments, in order to test and generate hypotheses about the links between regulatory networks. We describe here a method for presenting results from different experiments as a directed graph constructed using an automated graph drawing program xneato, enhanced by a logic program designed to cluster data and aid in the generation of hypotheses about possible gene interactions. A web-based front-end to the system has been constructed to explore and manipulate the graphical displays produced. Results of microarray experiments on Mycobacterium tuberculosis were used to develop and evaluate the visualization tool and initiate the development of an inference system for gene interactions based on such data. The GeneGraph project can be accessed at: zebrafish.doc.ic.ac.uk

Computer Graphics↗

PubMed2006

Network growth models and genetic regulatory networks.

We study a class of growth algorithms for directed graphs that are candidate models for the evolution of genetic regulatory networks. The algorithms involve partial duplication of nodes and their links, together with the innovation of new links, allowing for the possibility that input and output links from a newly created node may have different probabilities of survival. We find some counterintuitive trends as the parameters are varied, including the broadening of the in-degree distribution when the probability for retaining input links is decreased. We also find that both the scaling of transcription factors with genome size and the measured degree distributions for genes in yeast can be reproduced by the growth algorithm if and only if a special seed is used to initiate the process.

Animals↗

PubMed2006

Protein classification using probabilistic chain graphs and the Gene Ontology structure.

MOTIVATION: Probabilistic graphical models have been developed in the past for the task of protein classification. In many cases, classifications obtained from the Gene Ontology have been used to validate these models. In this work we directly incorporate the structure of the Gene Ontology into the graphical representation for protein classification. We present a method in which each protein is represented by a replicate of the Gene Ontology structure, effectively modeling each protein in its own 'annotation space'. Proteins are also connected to one another according to different measures of functional similarity, after which belief propagation is run to make predictions at all ontology terms. RESULTS: The proposed method was evaluated on a set of 4879 proteins from the Saccharomyces Genome Database whose interactions were also recorded in the GRID project. Results indicate that direct utilization of the Gene Ontology improves predictive ability, outperforming traditional models that do not take advantage of dependencies among functional terms. Average increase in accuracy (precision) of positive and negative term predictions of 27.8% (2.0%) over three different similarity measures and three subontologies was observed. AVAILABILITY: C/C++/Perl implementation is available from authors upon request.

Algorithms↗

PubMed2004

[Comparative genomic hybridization analysis of nasopharyngeal carcinoma drug-resistant cell CNE2/DDP and its parent cell CNE2].

BACKGROUND & OBJECTIVE: Developed from fluorescence in situ hybridization (FISH), comparative genomic hybridization (CGH) is a new molecular and cellular technique,used to examine the genomic imbalances,especially the loss and amplification of chromosomes,and to locate these alterations in certain chromosome regions. For a comprehensive understanding of the possible differences in genomic DNA between nasopharyngeal carcinoma drug-resistant and drug-sensitive cells, we analyzed the genomic DNA of a nasopharyngeal carcinoma drug-resistant cell line CNE2/DDP and its parent cell line CNE2 with CGH. METHODS: Genomic DNA was extracted from CNE2/DDP and CNE2 cells, as well as from normal placenta tissue. Fluorescent random primed labeling method was used to label the DNA probes (CNE2 and CNE2/DDP cells with green fluorescein-12-dUTP and normal placenta tissue with red tetramethylrhodamine-5-dUTP). The labeled DNA probes were then co-hybridized with normal lymphocyte metaphase chromosomes. Signals were taken by charge coupled device (CCD) and processed with Quips CGH Program after fluorescent hybridization. The green-to-red fluorescent ratio was calculated automatically and showed with graphs. RESULTS: There were extensive chromosome changes in CNE2, mainly the gain of 1q, 3q, 5p, 6p, 7p, 8q, 9q, 11p, 12q and 19q, and the loss of 4q, 12p, 13p, 14p, 15p, 18, 20q, 21p and 22. However, the CNE2/DDP cells, which come from the CNE2, showed a relatively normal karyotype except loss of 8p and gain of 8q and 19q. Consistent result was achieved after the CNE2/DDP cells were cultured in the medium free of any drugs for over one month. It appears that the CNE2/DDP cell has relative normal and much more stable chromosome constitution than its parent CNE2 cell. CONCLUSION: The CNE2/DDP is a single drug-resistant clone selected from CNE2,which is a blend of sub-clones that have different sorts of chromosome number and structure. It strongly suggests that the appearance of tumor drug-resistance is mainly a select process in which the would-be drug-resistant cell clone be selected from unfavorable survival condition.

Cell Line, Tumor↗

PubMed2004

LGL: creating a map of protein function with an algorithm for visualizing very large biological networks.

Networks are proving to be central to the study of gene function, protein-protein interaction, and biochemical pathway data. Visualization of networks is important for their study, but visualization tools are often inadequate for working with very large biological networks. Here, we present an algorithm, called large graph layout (LGL), which can be used to dynamically visualize large networks on the order of hundreds of thousands of vertices and millions of edges. LGL applies a force-directed iterative layout guided by a minimal spanning tree of the network in order to generate coordinates for the vertices in two or three dimensions, which are subsequently visualized and interactively navigated with companion programs. We demonstrate the use of LGL in visualizing an extensive protein map summarizing the results of approximately 21 billion sequence comparisons between 145579 proteins from 50 genomes. Proteins are positioned in the map according to sequence homology and gene fusions, with the map ultimately serving as a theoretical framework that integrates inferences about gene function derived from sequence homology, remote homology, gene fusions, and higher-order fusions. We confirm that protein neighbors in the resulting map are functionally related, and that distinct map regions correspond to distinct cellular systems, enabling a computational strategy for discovering proteins' functions on the basis of the proteins' map positions. Using the map produced by LGL, we infer general functions for 23 uncharacterized protein families.

Algorithms↗

PubMed2000

Estimating the probability for a protein to have a new fold: A statistical computational model.

Structural genomics aims to solve a large number of protein structures that represent the protein space. Currently an exhaustive solution for all structures seems prohibitively expensive, so the challenge is to define a relatively small set of proteins with new, currently unknown folds. This paper presents a method that assigns each protein with a probability of having an unsolved fold. The method makes extensive use of protomap, a sequence-based classification, and scop, a structure-based classification. According to protomap, the protein space encodes the relationship among proteins as a graph whose vertices correspond to 13,354 clusters of proteins. A representative fold for a cluster with at least one solved protein is determined after superposition of all scop (release 1.37) folds onto protomap clusters. Distances within the protomap graph are computed from each representative fold to the neighboring folds. The distribution of these distances is used to create a statistical model for distances among those folds that are already known and those that have yet to be discovered. The distribution of distances for solved/unsolved proteins is significantly different. This difference makes it possible to use Bayes' rule to derive a statistical estimate that any protein has a yet undetermined fold. Proteins that score the highest probability to represent a new fold constitute the target list for structural determination. Our predicted probabilities for unsolved proteins correlate very well with the proportion of new folds among recently solved structures (new scop 1.39 records) that are disjoint from our original training set.

Amino Acid Sequence↗

PubMed2000

Chromosome abnormalities in ovarian adenocarcinoma: III. Using breakpoint data to infer and test mathematical models for oncogenesis.

Cancer geneticists seek to identify genetic changes in tumor cells and to relate the genetic changes to tumor development. Because single changes can disrupt the cell cycle and promote other genetic changes, it is extremely hard to distinguish cause from effect. In this article we illustrate how 7 techniques from statistics, theoretical computer science, and phylogenetics can be used to infer and test possible models of tumor progression from single genome-wide descriptions of aberrations in a large sample of tumors. Specifically, we propose 4 tree models for tumor progression inferred from the large ovarian cancer data set described in the first 2 articles in this series. The models are derived from 2 different methods to select the non-random genetic aberrations and 2 different methods to infer the trees, given a set of events. Various aspects of the tree models are tested and extended by 5 methods: overall tests of independence, likelihood ratio tests, principal components analysis, directed acyclic graph modeling, and Bayesian survival analysis. All our methods lead to strikingly consistent conclusions about chromosomal breakpoints in ovarian adenocarcinoma, including (1) the non-random breakpoints in ovarian adenocarcinoma do not occur independently; (2) breakpoints in regions 1p3 and 11p1 are important early events and distinguish a class of tumors associated with poor prognosis; and (3) breakpoints in 1p1, 3p1, and 1q2 distinguish a class of ovarian tumors, and the breaks at 1p1 and 3p1 are associated with poor prognosis.

Adenocarcinoma↗

PubMed2005

Microarray annotation and biological information on function.

OBJECTIVES: Many methods for statistical analysis of gene expression studies by DNA microarrays produce lists of genes as output. To understand gene lists in terms of traditional biology, e.g. which pathways may be affected, it is necessary to get appropriate annotations for the probes on an array. METHODS: Problems arise with the different sources that have been used by manufacturers to design microarray probes, and their association to biological entities like genes, transcripts and proteins. Function annotation is of crucial importance, and systems like Gene Ontology can be used for this purpose. It arranges annotation terms in a hierarchical manner and thus makes annotations in a gene list amenable to automated analysis. RESULTS: Several methods for analyses of gene function are described. The hierarchical nature of systems like Gene Ontology particularly suggests using methods from graph theory. CONCLUSIONS: The main problem in annotating microarray probes and inferring affected functional modules is the incompleteness and degree of error in current biological databases. Initial approaches to make use of functional annotation exist, but have to be extended, in particular with respect to estimating the statistical significance of results.

Computational Biology↗

PubMed2000

Combinatorial explosion in model gene networks.

The explosive growth in knowledge of the genome of humans and other organisms leaves open the question of how the functioning of genes in interacting networks is coordinated for orderly activity. One approach to this problem is to study mathematical properties of abstract network models that capture the logical structures of gene networks. The principal issue is to understand how particular patterns of activity can result from particular network structures, and what types of behavior are possible. We study idealized models in which the logical structure of the network is explicitly represented by Boolean functions that can be represented by directed graphs on n-cubes, but which are continuous in time and described by differential equations, rather than being updated synchronously via a discrete clock. The equations are piecewise linear, which allows significant analysis and facilitates rapid integration along trajectories. We first give a combinatorial solution to the question of how many distinct logical structures exist for n-dimensional networks, showing that the number increases very rapidly with n. We then outline analytic methods that can be used to establish the existence, stability and periods of periodic orbits corresponding to particular cycles on the n-cube. We use these methods to confirm the existence of limit cycles discovered in a sample of a million randomly generated structures of networks of 4 genes. Even with only 4 genes, at least several hundred different patterns of stable periodic behavior are possible, many of them surprisingly complex. We discuss ways of further classifying these periodic behaviors, showing that small mutations (reversal of one or a few edges on the n-cube) need not destroy the stability of a limit cycle. Although these networks are very simple as models of gene networks, their mathematical transparency reveals relationships between structure and behavior, they suggest that the possibilities for orderly dynamics in such networks are extremely rich and they offer novel ways to think about how mutations can alter dynamics. (c) 2000 American Institute of Physics.

Journal Article↗

PubMed2025

Seed2LP: seed inference in metabolic networks for reverse ecology applications.

MOTIVATION: A challenging problem in microbiology is to determine nutritional requirements of microorganisms and culture them, especially for the microbial dark matter detected solely with culture-independent methods. The latter foster an increasing amount of genomic sequences that can be explored with reverse ecology approaches to raise hypotheses on the corresponding populations. Building upon genome-scale metabolic networks (GSMNs) obtained from genome annotations, metabolic models predict contextualized phenotypes using nutrient information. RESULTS: We developed the tool Seed2LP, addressing the inverse problem of predicting source nutrients, or seeds, from a GSMN and a metabolic objective. The originality of Seed2LP is its hybrid model, combining a scalable and discrete Boolean approximation of metabolic activity, with the numerically accurate flux balance analysis (FBA). Seed inference is highly customizable, with multiple search and solving modes, exploring the search space of external and internal metabolites combinations. Application to a benchmark of 107 curated GSMNs highlights the usefulness of a logic modelling method over a graph-based approach to predict seeds, and the relevance of hybrid solving to satisfy FBA constraints. Focusing on the dependency between metabolism and environment, Seed2LP is a computational support contributing to address the multifactorial challenge of culturing possibly uncultured microorganisms. AVAILABILITY AND IMPLEMENTATION: Seed2LP is available on https://github.com/bioasp/seed2lp.

Metabolic Networks and Pathways↗

PubMed2026

Scalable computation of ultrabubbles in pangenomes by orienting bidirected graphs.

MOTIVATION: Pangenome graphs are increasingly used in bioinformatics, ranging from environmental surveillance and crop improvement to the construction of population-scale human pangenomes. As these graphs grow in size, methods that scale efficiently become essential. A central task in pangenome analysis is the discovery of variation structures. In directed graphs, the most widely studied such structures, superbubbles, can be identified in linear time. Their canonical generalization to bidirected graphs, ultrabubbles, more accurately models DNA reverse complementarity. However, existing ultrabubble algorithms are quadratic in the worst case. RESULTS: We show that all ultrabubbles in a bidirected graph containing at least one tip or one cutvertex-a common property of pangenome graphs-can be computed in linear time. Our key contribution is a new linear-time orientation algorithm that transforms such a bidirected graph into a directed graph of the same size, in practice. Orientation conflicts are resolved by introducing auxiliary source or sink vertices. We prove that ultrabubbles in the original bidirected graph correspond to weak superbubbles in the resulting directed graph, enabling the use of existing linear-time algorithms. Our approach achieves speedups of up to 25× over the ultrabubble implementation in vg, and of >200× over BubbleGun, enabling scalable pangenome analyses. For example, on the v2.0 pangenome graph constructed by the Human Pangenome Reference Consortium from 232 individuals, after reading the input, our method completes in under 3 min, while vg requires >1 hour, and four times more RAM. AVAILABILITY AND IMPLEMENTATION: Our method is implemented in the BubbleFinder tool github.com/algbio/BubbleFinder, via the new ultrabubbles subcommand.

Algorithms↗

PubMed2026

REvolutionH-tl 2.0: A fast and robust tool for decoding evolutionary gene histories.

REvolutionH-tl is a fast, scalable, and integrated software platform for inferring orthology relationships, gene trees, species trees, and reconciled evolutionary scenarios directly from sequence data. Built upon the formal framework of best match graphs (BMGs), REvolutionH-tl predicts orthogroups and orthologous gene pairs with high accuracy, requiring neither precomputed trees nor multiple external tools. The software reconstructs event-labeled gene and species trees, seamlessly integrating reconciliation to produce fast, accurate, and biologically insightful evolutionary scenarios. Through extensive benchmarking on synthetic datasets with known ground truth, REvolutionH-tl outperforms or matches the accuracy of established tools such as OrthoFinder, Proteinortho, RAxML, GeneRax, and RANGER-DTL, while achieving significantly lower runtimes. A key innovation of REvolutionH-tl is its built-in support for detailed, publication-ready visualizations, which allow users to explore genome evolution dynamics, orthogroup composition, and reconciliation results with clarity and ease. These visual features position REvolutionH-tl as the first platform of its kind to combine analytical precision with intuitive interpretability. The software is open-source, cross-platform, and freely available at https://pypi.org/project/revolutionhtl/, providing a robust solution for large-scale evolutionary analyses in comparative genomics.

Software↗

PubMed2026

Network methods for diagonal integration of unpaired single-cell multiomics data: a review.

MOTIVATION: Advances in single-cell sequencing have enabled multiomics profiling at unprecedented resolution; however, mass spectrometry-based single-cell proteomics (scMS) remains inherently destructive, precluding simultaneous transcriptomic capture. Unlike antibody-based methods such as CITE-seq, which permit paired profiling but are restricted to targeted protein panels, scMS provides unbiased, genome-scale coverage of the intracellular proteome yet necessitates post hoc integration of unpaired datasets. This diagonal integration challenge, where transcriptomes and proteomes are measured in separate cells lacking shared anchors, remains underserved by existing reviews, which focus predominantly on vertical integration strategies enabled by non-destructive assays. RESULTS: We survey the complete computational pipeline for constructing mechanistic proteogenomic networks from unpaired single-cell data, covering: (i) unimodal network inference such as knowledge-based approaches, probabilistic graphical models, temporal directionality inference, and generative and foundation model strategies that establish the transcriptomic scaffold; (ii) cross-modal integration architectures such as network propagation, graph neural networks (scMRDR, scmFormer, scCotag), and consensus frameworks designed explicitly for the unpaired proteomics setting; and (iii) benchmarking paradigms spanning network reconstruction (BEELINE, GRETA, CausalBench) and multi-task integration evaluation (scMultiBench, SCMMIB), with guidance on metric selection under network sparsity and class imbalance. We identify three principal axes of future development: generative proteomic translation from transcriptomic precursors, inductive prior embedding in next-generation architectures, and perturbation-based causal benchmarking. AVAILABILITY AND IMPLEMENTATION: This is a review article; no novel software is distributed. A curated benchmark resource table, methods starter guide, and per-method bottleneck annotations are provided in the Supplementary Material.

Multiomics↗

PubMed2004

Molecular triangulation: bridging linkage and molecular-network information for identifying candidate genes in Alzheimer's disease.

A major challenge in human genetics is identifying the molecular basis of common heritable disorders. In contrast to rare single-gene diseases, multifactorial disorders are thought to arise from the combined effect of multiple gene variants, such that any single variant may have only a modest effect on disease susceptibility. We present a method to identify genes that may harbor a significant proportion of the genetic variation that predisposes individuals to a given multifactorial disorder. First, we perform an automated literature analysis that predicts physical interactions (edges) among candidate disease genes (seed nodes, selected on the basis of prior information) and other molecular entities. We derive models of molecular networks from this analysis and map the seed nodes to them. We then compute the graph-theoretic distance (the minimum number of edges that must be traversed) between the seed nodes and all other nodes in the network. We assume that nodes that are found in close proximity to multiple seed nodes are the best disease-related candidate genes. To evaluate this approach, we selected four seed genes, each with a proven role in Alzheimer's disease (AD). The method performed well in predicting additional network nodes that match AD gene candidates identified manually by an expert. We also show that the method prioritizes among the seed nodes themselves, rejecting false-positive seeds that are derived from (noisy) whole-genome genetic-linkage scans. We propose that this strategy will provide a valuable means to bridge genetic and genomic knowledge in the search for genetic determinants of multifactorial disorders.

Algorithms↗

PubMed2004

A novel graph-based similarity measure for 2D chemical structures.

In this paper, we propose a graph-based method to measure the similarity between chemical compounds described by 2D form. Our main idea is to measure the similarity between two compounds based on edges, nodes, and connectivity of their common subgraphs. We applied the proposed similarity measure in combination with a clustering method to more than eleven thousand compounds in the chemical compound database KEGG/LIGAND and discovered that compound clusters with highly similar structure compounds that share common names, take part in the same pathways, and have the same requirement of enzymes in reactions. Furthermore, we discovered the surprising sameness between pathway modules identified by clusters of similar structure compounds and that identified by genomic contexts, namely, operon structures of enzyme genes.

Algorithms↗

PubMed2005

Integration of metabolic networks and gene expression in virtual reality.

MOTIVATION: Metabolic networks combine metabolism and regulation. These complex networks are difficult to understand and visualize due to the amount and diverse types of information that need to be represented. For example, pathway information gives indications of interactions. Experimental data, such as transcriptomics, proteomics and metabolomics data, give snapshots of the system state. Stereoscopic virtual environments provide a true three-dimensional representation of metabolic networks, which can be intuitively manipulated, and may help to manage the data complexity. RESULTS: MetNet3D, a 3D virtual reality system, allows a user to explore gene expression and metabolic pathway data simultaneously. Normalized gene expression data are processed in R and visualized as a 3D plot. Users can find a particular gene of interest or a cluster of genes that behave similarly and see how these genes function in metabolic networks from MetNetDB, a database of Arabidopsis metabolic networks, using animated network graphs. Interactive virtual reality, with its enhanced ability to display more information, makes such integration more effective by abstracting key relationships. AVAILABILITY: MetNet3D and some sample datasets are available at http://www.vrac.iastate.edu/research/sites/metnet/Download/Download.htm. SUPPLEMENTARY INFORMATION: Color snapshots and movies are available at http://www.vrac.iastate.edu/research/sites/metnet/Bioinformatics/SupplementaryInformation.htm.

Algorithms↗

PubMed2005

Bioinformatics analysis of alternative splicing.

Over the past few years, the analysis of alternative splicing using bioinformatics has emerged as an important new field, and has significantly changed our view of genome function. One exciting front has been the analysis of microarray data to measure alternative splicing genome-wide. Pioneering studies of both human and mouse data have produced algorithms for discerning evidence of alternative splicing and clustering genes and samples by their alternative splicing patterns. Moreover, these data indicate the presence of alternative splice forms in up to 80 per cent of human genes. Comparative genomics studies in both mammals and insects have demonstrated that alternative splicing can in some cases be predicted directly from comparisons of genome sequences, based on heightened sequence conservation and exon length. Such studies have also provided new insights into the connection between alternative splicing and a variety of evolutionary processes such as Alu-based exonisation, exon creation and loss. A number of groups have used a combination of bioinformatics, comparative genomics and experimental validation to identify new motifs for splice regulatory factors, analyse the balance of factors that regulate alternative splicing, and propose a new mechanism for regulation based on the interaction of alternative splicing and nonsense-mediated decay. Bioinformatics studies of the functional impact of alternative splicing have revealed a wide range of regulatory mechanisms, from NAGNAG sites that add a single amino acid; to short peptide segments that can play surprisingly complex roles in switching protein conformation and function (as in the Piccolo C2A domain); to events that entirely remove a specific protein interaction domain or membrane anchoring domain. Common to many bioinformatics studies is a new emphasis on graph representations of alternative splicing structures, which have many advantages for analysis.

Algorithms↗