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PubMed2005

Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research.

SUMMARY: We present here Blast2GO (B2G), a research tool designed with the main purpose of enabling Gene Ontology (GO) based data mining on sequence data for which no GO annotation is yet available. B2G joints in one application GO annotation based on similarity searches with statistical analysis and highlighted visualization on directed acyclic graphs. This tool offers a suitable platform for functional genomics research in non-model species. B2G is an intuitive and interactive desktop application that allows monitoring and comprehension of the whole annotation and analysis process. AVAILABILITY: Blast2GO is freely available via Java Web Start at http://www.blast2go.de. SUPPLEMENTARY MATERIAL: http://www.blast2go.de -> Evaluation.

Algorithms↗

PubMed2005

ECgene: genome annotation for alternative splicing.

ECgene provides annotation for gene structure, function and expression, taking alternative splicing events into consideration. The gene-modeling algorithm combines the genome-based expressed sequence tag (EST) clustering and graph-theoretic transcript assembly procedures. The website provides several viewers and applications that have many unique features useful for the analysis of the transcript structure and gene expression. The summary viewer shows the gene summary and the essence of other annotation programs. The genome browser and the transcript viewer are available for comparing the gene structure of splice variants. Changes in the functional domains by alternative splicing can be seen at a glance in the transcript viewer. We also provide two unique ways of analyzing gene expression. The SAGE tags deduced from the assembled transcripts are used to delineate quantitative expression patterns from SAGE libraries available publically. Furthermore, the cDNA libraries of EST sequences in each cluster are used to infer qualitative expression patterns. It should be noted that the ECgene website provides annotation for the whole transcriptome, not just the alternatively spliced genes. Currently, ECgene supports the human, mouse and rat genomes. The ECgene suite of tools and programs is available at http://genome.ewha.ac.kr/ECgene/.

Algorithms↗

PubMed2003

Exploring the repertoire of RNA secondary motifs using graph theory; implications for RNA design.

Understanding the structural repertoire of RNA is crucial for RNA genomics research. Yet current methods for finding novel RNAs are limited to small or known RNA families. To expand known RNA structural motifs, we develop a two-dimensional graphical representation approach for describing and estimating the size of RNA's secondary structural repertoire, including naturally occurring and other possible RNA motifs. We employ tree graphs to describe RNA tree motifs and more general (dual) graphs to describe both RNA tree and pseudoknot motifs. Our estimates of RNA's structural space are vastly smaller than the nucleotide sequence space, suggesting a new avenue for finding novel RNAs. Specifically our survey shows that known RNA trees and pseudoknots represent only a small subset of all possible motifs, implying that some of the 'missing' motifs may represent novel RNAs. To help pinpoint RNA-like motifs, we show that the motifs of existing functional RNAs are clustered in a narrow range of topological characteristics. We also illustrate the applications of our approach to the design of novel RNAs and automated comparison of RNA structures; we report several occurrences of RNA motifs within larger RNAs. Thus, our graph theory approach to RNA structures has implications for RNA genomics, structure analysis and design.

Algorithms↗

PubMed2006

Ancient genomic architecture for mammalian olfactory receptor clusters.

BACKGROUND: Mammalian olfactory receptor (OR) genes reside in numerous genomic clusters of up to several dozen genes. Whole-genome sequence alignment nets of five mammals allow their comprehensive comparison, aimed at reconstructing the ancestral olfactory subgenome. RESULTS: We developed a new and general tool for genome-wide definition of genomic gene clusters conserved in multiple species. Syntenic orthologs, defined as gene pairs showing conservation of both genomic location and coding sequence, were subjected to a graph theory algorithm for discovering CLICs (clusters in conservation). When applied to ORs in five mammals, including the marsupial opossum, more than 90% of the OR genes were found within a framework of 48 multi-species CLICs, invoking a general conservation of gene order and composition. A detailed analysis of individual CLICs revealed multiple differences among species, interpretable through species-specific genomic rearrangements and reflecting complex mammalian evolutionary dynamics. One significant instance involves CLIC #1, which lacks a human member, implying the human-specific deletion of an OR cluster, whose mouse counterpart has been tentatively associated with isovaleric acid odorant detection. CONCLUSION: The identified multi-species CLICs demonstrate that most of the mammalian OR clusters have a common ancestry, preceding the split between marsupials and placental mammals. However, only two of these CLICs were capable of incorporating chicken OR genes, parsimoniously implying that all other CLICs emerged subsequent to the avian-mammalian divergence.

Animals↗

PubMed2005

Backward simulation of ancestors of sampled individuals.

If the population is large and the sampling mechanism is random, the coalescent is commonly used to model the haplotypes in the sample. Ordered genotypes can then be formed by random matching of the derived haplotypes. However, this approach is not realistic when (1) there is departure from random mating (e.g., dominant individuals in breeding populations or monogamy in humans), or (2) the population is small and/or the individuals in the sample are ascertained by applying some particular non-random sampling scheme, as is usually the case when considering the statistical modeling and analysis of pedigree data. For such situations, we present here a data generation method where an ancestral graph with non-overlapping generations is first generated backwards in time, using ideas from coalescent theory. Alleles are randomly assigned to the founders, and subsequently the gene flow over the entire genome is simulated forwards in time by dropping alleles down the graph according to recombination model without interference. The parameters controlling the mating behavior of generated individuals in the graph (degree of monogamy) can be tuned in order to match a particular demographic situation, without restriction to simple random mating. The performance of the approach is illustrated with a simulation example. The software (written in C-language) is freely available for research purposes at http://www.rni.helsinki.fi/~dag/.

Female↗

PubMed2004

TEAM: a tool for the integration of expression, and linkage and association maps.

The identification of genes primarily responsible for complex genetic disorders is a daunting task. Despite the assignment of many susceptibility loci, there has only been limited success in identifying disease genes based solely on positional information from genome-wide screens. The incorporation of several complementary strategies in a single integrated approach should facilitate and further enhance the efficacy of this search for genes. To permit the integration of linkage, association and expression data, together with functional annotations, we have developed a Java-based software tool: TEAM (tool for the integration of expression, and linkage and association maps). TEAM includes a genome viewer, capable of overlaying karyobands, genes, markers, linkage graphs, association data, gene expression levels and functional annotations in one composite view. Data management, analysis and filtering functionality was implemented and extended with links to the Ensembl, Unigene and Gene Ontology databases to facilitate gene annotation. Filtering functionality can help prevent the exclusion of poorly annotated, but differentially expressed, genes that reside in candidate regions that show linkage or association. Here we demonstrate the program's functionality in our study on coeliac disease (OMIM 212750), a multifactorial gluten-sensitive enteropathy. We performed a combined data analysis of a genome-wide linkage screen in 82 Dutch families with affected siblings and the microarray expression profiles of 18,110 cDNAs in 22 intestinal biopsies.

Celiac Disease↗

PubMed2004

ASmodeler: gene modeling of alternative splicing from genomic alignment of mRNA, EST and protein sequences.

Alternative splicing is in important mechanism of modulating gene function and expression which greatly expands transcriptome diversity. ASmodeler is a novel web-based utility that finds gene models including alternative splicing events from genomic alignment of mRNA, EST and protein sequences. User-supplied sequences are aligned against the genome map using the BLAT and SIM4 programs. Resulting exon connectivity is analyzed by applying graph-theoretic methods to build all possible gene models including splice variants. The algorithm essentially combines the genome-based sequence clustering and transcript assembly procedures in a coherent fashion. In addition to the user-supplied sequences, UniGene clusters and many well-known gene predictions such as Genscan, Ensembl and Acembly may be included in gene modeling. The current implementation supports human, mouse and rat genomes. ASmodeler is available at http://genome.ewha.ac.kr/ECgene/ASmodeler/.

Algorithms↗

PubMed2026

Graph-KIR: graph-based KIR copy number estimation and allele calling using short-read sequencing data.

MOTIVATION: The Killer-cell Immunoglobulin-like Receptor (KIR) is a highly polymorphic region in the human genome, associated with autoimmune diseases and organ transplantation. The sequences of KIR genes are highly similar among star alleles as well as in between individual genes, with the copy number of each KIR gene typically ranging from 0 to 4. In this study, we introduce Graph-KIR, a tool designed to estimate gene copy numbers and predict full-resolution (7-digit, encompassing both coding and non-coding sequence variations) from a whole genome sequencing (WGS) sample. RESULTS: Graph-KIR is capable of independently typing KIR alleles per sample with no reliance on the distribution of any framework gene in a cohort. In a set of 100 simulated samples, Graph-KIR demonstrated 99.2% accuracy in copy number estimation and high F1-score of allele typing: 91.79% at 7-digit resolution, 97.37% at 5-digit resolution, and 97.11% at 3-digit resolution. Graph-KIR outperforms existing tools such as Geny (96.39% F1-score), PING's WGS version (92.77% F1-score), and T1K (90.44% F1-score) at 5-digit resolution. By analyzing the results on 44 HPRC samples, Graph-KIR achieves better F1-score than Geny and PING at 7-digit resolution. The release of Graph-KIR adds another valuable tool to assist users in accurately estimating copy numbers and calling alleles of KIR genes from WGS samples. AVAILABILITY AND IMPLEMENTATION: The Graph-KIR and paper-related pipeline codes are available at https://github.com/linnil1/KIR_graph.

Receptors, KIR↗

PubMed2004

Prediction of splice sites with dependency graphs and their expanded bayesian networks.

MOTIVATION: Owing to the complete sequencing of human and many other genomes, huge amounts of DNA sequence data have been accumulated. In bioinformatics, an important issue is how to predict the complete structure of genes from the genomic DNA sequence, especially the human genome. A crucial part in the gene structure prediction is to determine the precise exon-intron boundaries, i.e. the splice sites, in the coding region. RESULTS: We have developed a dependency graph model to fully capture the intrinsic interdependency between base positions in a splice site. The establishment of dependency between two position is based on a chi2-test from known sample data. To facilitate statistical inference, we have expanded the dependency graph (which is usually a graph with cycles that make probabilistic reasoning very difficult, if not impossible) into a Bayesian network (which is a directed acyclic graph that facilitates statistical reasoning). When compared with the existing models such as weight matrix model, weight array model, maximal dependence decomposition, Cai et al.'s tree model as well as the less-studied second-order and third-order Markov chain models, the expanded Bayesian networks from our dependency graph models perform the best in nearly all the cases studied. AVAILABILITY: Software (a program called DGSplicer) and datasets used are available at http://csrl.ee.nthu.edu.tw/bioinf/ CONTACT: cclu@ee.nthu.edu.tw.

Bayes Theorem↗

PubMed2025

A General Framework for Branch Length Estimation in Ancestral Recombination Graphs.

Inference of Ancestral Recombination Graphs (ARGs) is of central interest in the analysis of genomic variation. ARGs can be specified in terms of topologies and coalescence times. The coalescence times are usually estimated using an informative prior derived from coalescent theory, but this may generate biased estimates and can also complicate downstream inferences based on ARGs. Here we introduce, POLEGON, a novel approach for estimating branch lengths for ARGs which uses an uninformative prior. Using extensive simulations, we show that this method provides improved estimates of coalescence times and lead to more accurate inferences of effective population sizes under a wide range of demographic assumptions (population expansion, bottleneck, split, etc). It also improves other downstream inferences including estimates of mutation rates. We apply the method to data from the 1000 Genomes Project to investigate population size histories and differential mutation signatures across populations. We also estimate coalescence times in the HLA region, and show that they exceed 30 million years in multiple segments.

Ancestral Recombination Graph↗

PubMed2005

SpectralNET--an application for spectral graph analysis and visualization.

BACKGROUND: Graph theory provides a computational framework for modeling a variety of datasets including those emerging from genomics, proteomics, and chemical genetics. Networks of genes, proteins, small molecules, or other objects of study can be represented as graphs of nodes (vertices) and interactions (edges) that can carry different weights. SpectralNET is a flexible application for analyzing and visualizing these biological and chemical networks. RESULTS: Available both as a standalone .NET executable and as an ASP.NET web application, SpectralNET was designed specifically with the analysis of graph-theoretic metrics in mind, a computational task not easily accessible using currently available applications. Users can choose either to upload a network for analysis using a variety of input formats, or to have SpectralNET generate an idealized random network for comparison to a real-world dataset. Whichever graph-generation method is used, SpectralNET displays detailed information about each connected component of the graph, including graphs of degree distribution, clustering coefficient by degree, and average distance by degree. In addition, extensive information about the selected vertex is shown, including degree, clustering coefficient, various distance metrics, and the corresponding components of the adjacency, Laplacian, and normalized Laplacian eigenvectors. SpectralNET also displays several graph visualizations, including a linear dimensionality reduction for uploaded datasets (Principal Components Analysis) and a non-linear dimensionality reduction that provides an elegant view of global graph structure (Laplacian eigenvectors). CONCLUSION: SpectralNET provides an easily accessible means of analyzing graph-theoretic metrics for data modeling and dimensionality reduction. SpectralNET is publicly available as both a .NET application and an ASP.NET web application from http://chembank.broad.harvard.edu/resources/. Source code is available upon request.

Algorithms↗

PubMed2006

A quantitative analysis of secondary RNA structure using domination based parameters on trees.

BACKGROUND: It has become increasingly apparent that a comprehensive database of RNA motifs is essential in order to achieve new goals in genomic and proteomic research. Secondary RNA structures have frequently been represented by various modeling methods as graph-theoretic trees. Using graph theory as a modeling tool allows the vast resources of graphical invariants to be utilized to numerically identify secondary RNA motifs. The domination number of a graph is a graphical invariant that is sensitive to even a slight change in the structure of a tree. The invariants selected in this study are variations of the domination number of a graph. These graphical invariants are partitioned into two classes, and we define two parameters based on each of these classes. These parameters are calculated for all small order trees and a statistical analysis of the resulting data is conducted to determine if the values of these parameters can be utilized to identify which trees of orders seven and eight are RNA-like in structure. RESULTS: The statistical analysis shows that the domination based parameters correctly distinguish between the trees that represent native structures and those that are not likely candidates to represent RNA. Some of the trees previously identified as candidate structures are found to be "very" RNA like, while others are not, thereby refining the space of structures likely to be found as representing secondary RNA structure. CONCLUSION: Search algorithms are available that mine nucleotide sequence databases. However, the number of motifs identified can be quite large, making a further search for similar motif computationally difficult. Much of the work in the bioinformatics arena is toward the development of better algorithms to address the computational problem. This work, on the other hand, uses mathematical descriptors to more clearly characterize the RNA motifs and thereby reduce the corresponding search space. These preliminary findings demonstrate that graph-theoretic quantifiers utilized in fields such as computer network design hold significant promise as an added tool for genomics and proteomics.

Algorithms↗

PubMed2001

SNAPping up functionally related genes based on context information: a colinearity-free approach.

We describe a computational approach for finding genes that are functionally related but do not possess any noticeable sequence similarity. Our method, which we call SNAP (similarity-neighborhood approach), reveals the conservation of gene order on bacterial chromosomes based on both cross-genome comparison and context information. The novel feature of this method is that it does not rely on detection of conserved colinear gene strings. Instead, we introduce the notion of a similarity-neighborhood graph (SN-graph), which is constructed from the chains of similarity and neighborhood relationships between orthologous genes in different genomes and adjacent genes in the same genome, respectively. An SN-cycle is defined as a closed path on the SN-graph and is postulated to preferentially join functionally related gene products that participate in the same biochemical or regulatory process. We demonstrate the substantial non-randomness and functional significance of SN-cycles derived from real genome data and estimate the prediction accuracy of SNAP in assigning broad function to uncharacterized proteins. Examples of practical application of SNAP for improving the quality of genome annotation are described.

Algorithms↗

PubMed2026

engGNN: a dual-graph neural network for omics-based disease classification and feature selection.

Omics data, such as transcriptomics, proteomics, and metabolomics, provide critical insights into disease mechanisms and clinical outcomes. However, their high dimensionality, small sample sizes, and intricate biological networks pose major challenges for reliable prediction and meaningful interpretation. Graph neural networks offer a promising way to integrate prior knowledge by encoding feature relationships as graphs. Yet, existing methods typically rely solely on either an externally curated feature graph or a data-driven generated graph, which limits their ability to capture complementary information. To address this, we propose the external and generated Graph Neural Network (engGNN), a dual-graph framework that jointly leverages both external biological networks and data-driven generated graphs. Specifically, engGNN constructs a biologically informed undirected feature graph from established network databases and complements it with a directed feature graph derived from tree-ensemble models. This dual-graph design produces more comprehensive representations, thereby improving predictive performance and interpretability. Through extensive simulation studies and real-world applications to three independent gene expression datasets, engGNN consistently demonstrates strong classification performance compared with competitive baselines. Beyond classification, engGNN provides feature- and source-level interpretability, enabling biologically meaningful analyses such as pathway enrichment analysis. Taken together, these results highlight engGNN as a robust, flexible, and interpretable framework for disease classification and biomarker discovery in high-dimensional omics contexts.

Graph Neural Networks↗

PubMed2004

Comparative analysis of protein domain organization.

We have developed a set of graph theory-based tools, which we call Comparative Analysis of Protein Domain Organization (CADO), to survey and compare protein domain organizations of different organisms. In the language of CADO, the organization of protein domains in a given organism is shown as a domain graph in which protein domains are represented as vertices, and domain combinations, defined as instances of two domains found in one protein, are represented as edges. CADO provides a new way to analyze and compare whole proteomes, including identifying the consensus and difference of domain organization between organisms. CADO was used to analyze and compare >50 bacterial, archaeal, and eukaryotic genomes. Examples and overviews presented here include the analysis of the modularity of domain graphs and the functional study of domains based on the graph topology. We also report on the results of comparing domain graphs of two organisms, Pyrococcus horikoshii (an extremophile) and Haemophilus influenzae (a parasite with reduced genome) with other organisms. Our comparison provides new insights into the genome organization of these organisms. Finally, we report on the specific domain combinations characterizing the three kingdoms of life, and the kingdom "signature" domain organizations derived from those specific domain combinations.

Animals↗

PubMed2025

The Data Distillery: A Graph Framework for Semantic Integration and Querying of Biomedical Data.

The Data Distillery Knowledge Graph (DDKG) is a framework for semantic integration and querying of biomedical data across domains. Built for the NIH Common Fund Data Ecosystem, it supports translational research by linking clinical and experimental datasets in a unified graph model. Clinical standards such as ICD-10, SNOMED, and DrugBank are integrated through UMLS, while genomics and basic science data are structured using ontologies and standards such as HPO, GENCODE, Ensembl, STRING, and ClinVar. The DDKG uses a property graph architecture based on the UBKG infrastructure and supports ontology-based ingestion, identifier normalization, and graph-native querying. The system is modular and can be extended with new datasets or schema modules. We demonstrate its utility for informatics queries across eight use cases, including regulatory variant analysis, tissue-specific expression, biomarker discovery, and cross-species variant prioritization. The DDKG is accessible via a public interface, a programmatic API, and downloadable builds for local use.

Journal Article↗

PubMed2005

Supervised enzyme network inference from the integration of genomic data and chemical information.

MOTIVATION: The metabolic network is an important biological network which relates enzyme proteins and chemical compounds. A large number of metabolic pathways remain unknown nowadays, and many enzymes are missing even in known metabolic pathways. There is, therefore, an incentive to develop methods to reconstruct the unknown parts of the metabolic network and to identify genes coding for missing enzymes. RESULTS: This paper presents new methods to infer enzyme networks from the integration of multiple genomic data and chemical information, in the framework of supervised graph inference. The originality of the methods is the introduction of chemical compatibility as a constraint for refining the network predicted by the network inference engine. The chemical compatibility between two enzymes is obtained automatically from the information encoded by their Enzyme Commission (EC) numbers. The proposed methods are tested and compared on their ability to infer the enzyme network of the yeast Saccharomyces cerevisiae from four datasets for enzymes with assigned EC numbers: gene expression data, protein localization data, phylogenetic profiles and chemical compatibility information. It is shown that the prediction accuracy of the network reconstruction consistently improves owing to the introduction of chemical constraints, the use of a supervised approach and the weighted integration of multiple datasets. Finally, we conduct a comprehensive prediction of a global enzyme network consisting of all enzyme candidate proteins of the yeast to obtain new biological findings. AVAILABILITY: Softwares are available upon request.

Algorithms↗

PubMed2026

Pouria Salehi Nowbandegani.

Dr. Laura Zahn asked Dr. Pouria Salehi Nowbandegani about their study, "Defining and cataloging variants in pangenome graphs," and how they came to study this aspect of genomics.

Humans↗