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At least 217 records · Page 12Linked to original sources
PubMed2002

ViewGene: a graphical tool for polymorphism visualization and characterization.

The human genome project is producing an enormous amount of sequence data, based on which single base changes between individuals can be identified. Unfortunately, computer tools that were adequate for sequence assembly are less than ideal for the characterization of polymorphism data [single nucleotide (snp) or insertion/deletion (indel)] and other sequence features, and their relationship to each other. We have developed viewGene as a flexible tool that takes input from a number of sequence formats and analysis programs (Genbank, FASTA, RepeatMasker, Cross match, BLAST, user-defined data) to construct a sequence reference scaffold that can be viewed through a simple graphical interface. polymorphisms generated from many sources can be added to this scaffold through the same sequence formats, with a variety of options to control what is displayed. Large amounts of polymorphism data can be organized so that patterns and haplotypes can be readily discerned. In our laboratory, viewGene has been used to view annotated genbank records, find nonrepetitive sequence fragments for polymorphism detection, and visualize similarity search results. Manipulation, cross-referencing, and haplotype viewing of snp data are essential for quality assessment and identification of variants associated with genetic disease, and viewGene provides all three of these important functions.

Amino Acid Sequence↗
PubMed2025

insilicoSV: a flexible grammar-based framework for structural variant simulation and placement.

SUMMARY: Structural variants (SVs) are key drivers of genetic variation and disease in the genome. Their discovery remains challenging, however, in large part due to the scarcity of validated SV callsets and comprehensive benchmarks, which are essential for method development and evaluation. The growing number of data-driven learning-based approaches for SV discovery, in particular, requires large, diverse, and well-balanced training datasets to achieve reliable performance. To address this need, SV simulation has served as a key tool for assessing method performance and training SV models. However, existing SV simulators only support a fixed and limited set of SV classes and do not provide fine-grained control over the placement of SVs within specific contexts of the genome. Here we present insilicoSV, a versatile framework for SV simulation, which models SVs using a simple and flexible grammar, allowing users to easily define standard and custom arbitrary genome rearrangements, as well as encode genome placement constraints. This design allows insilicoSV to naturally support new and bespoke SV types, such as the complex rearrangements of cancer genomes. In addition to grammar-based modeling, insilicoSV provides built-in support for 26 predefined SV types, placement of user-provided SVs, small variant simulation, streamlined workflows for the simulation of genome evolution and genome mixtures, read simulation, alignment, and visualization. These features enable the creation of comprehensive genomic datasets for a variety of downstream applications, such as in-depth benchmarking of alignment and variant calling methods, as well as training of data-driven learning-based approaches for SV detection. AVAILABILITY AND IMPLEMENTATION: insilicoSV is available under the MIT license at https://github.com/PopicLab/insilicoSV and https://doi.org/10.5281/zenodo.17402009.

Software↗
PubMed2003

OGRe: a relational database for comparative analysis of mitochondrial genomes.

Organellar Genome Retrieval (OGRe) is a relational database of complete mitochondrial genome sequences for over 250 Metazoan species. OGRe provides a resource for the comparative analysis of mitochondrial genomes at several levels. At the sequence level, OGRe allows the retrieval of any selected set of mitochondrial genes from any selected set of species. Species are classified using a taxonomic system that allows easy selection of related groups of species. Sequence alignments are also available for some species. At the level of individual nucleotides, the system contains information on base frequencies and codon usage frequencies that can be compared between organisms. At the level of whole genomes, OGRe provides several ways of visualizing information on gene order. Diagrams illustrating the genome arrangement can be generated for any selected set of species automatically from the information in the database. Searches can be done based on gene arrangement to find sets of species that have the same order as one another. Diagrams for pairwise comparison of species can be produced that show the positions of break-points in the gene order and use colour to highlight the sections of the genome that have moved. OGRe is available from http://www.bioinf.man.ac.uk/ogre.

Animals↗
PubMed2005

GObar: a gene ontology based analysis and visualization tool for gene sets.

BACKGROUND: Microarray experiments, as well as other genomic analyses, often result in large gene sets containing up to several hundred genes. The biological significance of such sets of genes is, usually, not readily apparent. Identification of the functions of the genes in the set can help highlight features of interest. The Gene Ontology Consortium 1 has annotated genes in several model organisms using a controlled vocabulary of terms and placed the terms on a Gene Ontology (GO), which comprises three disjoint hierarchies for Molecular functions, Biological processes and Cellular locations. The annotations can be used to identify functions that are enriched in the set, but this analysis can be misleading since the underlying distribution of genes among various functions is not uniform. For example, a large number of genes in a set might be kinases just because the genome contains many kinases. RESULTS: We use the Gene Ontology hierarchy and the annotations to pick significant functions and pathways by comparing the distribution of functions in a given gene list against the distribution of all the genes in the genome, using the hypergeometric distribution to assign probabilities. GObar is a web-based visualizer that implements this algorithm. The public website for GObar 2 can analyse gene lists from the yeast (S. cervisiae), fly (D. Melanogaster), mouse (M. musculus) and human (H. sapiens) genomes. It also allows visualization of the GO tree, as well as placement of a single gene on the GO hierarchy. We analyse a gene list from a genomic study of pre-mRNA splicing to demonstrate the utility of GObar. CONCLUSION: GObar is freely available as a web-based tool at http://katahdin.cshl.org:9331/GO2 and can help analyze and visualize gene lists from genomic analyses.

Algorithms↗
PubMed1997

Visual verification of close disposition between a rice A genome-specific DNA sequence (TrsA) and the telomere sequence.

A rice A genome-specific tandem repeat sequence (TrsA) and telomeric nucleotide sequences, (TTTAGGG)n, were simultaneously detected by multicolor fluorescence in situ hybridization (McFISH) using rice prometaphase chromosomes. Six pairs of TrsA sites visualized by fluorescence signals were all localized on the long arms close to the telomeric regions. Differences in the copy number of TrsA at the different sites were visualized both by the size of the telomeric condensation block stained with Giemsa solution and the signal intensity after FISH with TrsA. McFISH analyses using interphase nuclei could resolve close disposition of TrsA and telomere and also gave rough estimation of the distance between them. The functional significance of the close disposition of TrsA and telomere is discussed.

Chromosome Mapping↗
PubMed2026

NAViFluX: a visualization‑centric platform for interactive analysis, refinement and design of genome‑scale metabolic networks.

MOTIVATION: Genome-scale metabolic network (GSMN) models enable flux-based metabolite fate discovery, metabolic engineering, drug target identification, and multi-omics integration. However, programming requirements, architectural complexity, and limited visualization support impede its adoption by the broader scientific community. Existing tools exclusively specialize in GSMN analyses or visualization while lacking important features such as pathway-specific views, database-integrated refinement, and comprehensive enrichment and perturbation analyses. RESULTS: Here, we present NAViFluX (metabolic Network Analysis and Visualization of Flux), a visualization-centric, web browser-based tool that unifies native pathway/subsystem map generation, interactive model refinement via KEGG/BiGG, pathway merging and modules for flux computations, topology, and functional enrichment all within network views. Using three independent case studies on Escherichia coli, the utility of NAViFluX for characterization of nutrient-specific metabolic adaptations, enhancing gene essentiality predictions and interpretability, and rational design of an optimized carbon-fixing metabolic state is demonstrated. AVAILABILITY AND IMPLEMENTATION: All source code and supplementary files associated with the case studies are publicly available via Zenodo at https://zenodo.org/records/19107831. NAViFluX can be easily installed as a standalone software through https://github.com/bnsb-lab-iith/NAViFluX.

Metabolic Networks and Pathways↗
PubMed2000

Alfresco--a workbench for comparative genomic sequence analysis.

Comparative analysis of genomic sequences provides a powerful tool for identifying regions of potential biologic function; by comparing corresponding regions of genomes from suitable species, protein coding or regulatory regions can be identified by their homology. This requires the use of several specific types of computational analysis tools. Many programs exist for these types of analysis; not many exist for overall view/control of the results, which is necessary for large-scale genomic sequence analysis. Using Java, we have developed a new visualization tool that allows effective comparative genome sequence analysis. The program handles a pair of sequences from putatively homologous regions in different species. Results from various different existing external analysis programs, such as database searching, gene prediction, repeat masking, and alignment programs, are visualized and used to find corresponding functional sequence domains in the two sequences. The user interacts with the program through a graphic display of the genome regions, in which an independently scrollable and zoomable symbolic representation of the sequences is shown. As an example, the analysis of two unannotated orthologous genomic sequences from human and mouse containing parts of the UTY locus is presented.

Algorithms↗
PubMedDate not supplied

Comprehensive genetic analysis of cancer cells.

Human cancer is viewed as a disorder of genes originating from the progeny of a single cell that has accumulated multiple genetic alterations. The genetic alterations include point mutation, chromosomal rearrangements and imbalances. Amplifications primarily involve oncogenes whose overexpression leads to growth deregulation, while deletions commonly target tumor suppressor genes that control cell cycle checkpoints and DNA repair mechanisms. With the advent of molecular cytogenetics procedures for global detection of genomic imbalances and for multicolor visualization of structural chromosome changes, as well as the completion of human genome mapping and the development of microarray technology for serial gene expression analysis of the entire genomes, a significant progress has been made in uncovering the molecular basis of cancer. The major challenge in cancer biology is to decipher the molecular anatomy of various cancers and to identify cancer-related genes that now comprise only a fraction of human genes. The complete genetic anatomy of specific cancers would allow a better understanding of the role of genetic alterations in carcinogenesis, provide diagnostic and prognostic markers and discriminate between cells at different stages of progression toward malignancy. This review highlights current technologies that are available to explore cancer cells and outlines their application to investigations in human hepatocellular carcinoma.

Journal Article↗
PubMed2006

Genomes as geography: using GIS technology to build interactive genome feature maps.

BACKGROUND: Many commonly used genome browsers display sequence annotations and related attributes as horizontal data tracks that can be toggled on and off according to user preferences. Most genome browsers use only simple keyword searches and limit the display of detailed annotations to one chromosomal region of the genome at a time. We have employed concepts, methodologies, and tools that were developed for the display of geographic data to develop a Genome Spatial Information System (GenoSIS) for displaying genomes spatially, and interacting with genome annotations and related attribute data. In contrast to the paradigm of horizontally stacked data tracks used by most genome browsers, GenoSIS uses the concept of registered spatial layers composed of spatial objects for integrated display of diverse data. In addition to basic keyword searches, GenoSIS supports complex queries, including spatial queries, and dynamically generates genome maps. Our adaptation of the geographic information system (GIS) model in a genome context supports spatial representation of genome features at multiple scales with a versatile and expressive query capability beyond that supported by existing genome browsers. RESULTS: We implemented an interactive genome sequence feature map for the mouse genome in GenoSIS, an application that uses ArcGIS, a commercially available GIS software system. The genome features and their attributes are represented as spatial objects and data layers that can be toggled on and off according to user preferences or displayed selectively in response to user queries. GenoSIS supports the generation of custom genome maps in response to complex queries about genome features based on both their attributes and locations. Our example application of GenoSIS to the mouse genome demonstrates the powerful visualization and query capability of mature GIS technology applied in a novel domain. CONCLUSION: Mapping tools developed specifically for geographic data can be exploited to display, explore and interact with genome data. The approach we describe here is organism independent and is equally useful for linear and circular chromosomes. One of the unique capabilities of GenoSIS compared to existing genome browsers is the capacity to generate genome feature maps dynamically in response to complex attribute and spatial queries.

Animals↗
PubMed2004

ECR Browser: a tool for visualizing and accessing data from comparisons of multiple vertebrate genomes.

With an increasing number of vertebrate genomes being sequenced in draft or finished form, unique opportunities for decoding the language of DNA sequence through comparative genome alignments have arisen. However, novel tools and strategies are required to accommodate this large volume of genomic information and to facilitate the transfer of predictions generated by comparative sequence alignment to researchers focused on experimental annotation of genome function. Here, we present the ECR Browser, a tool that provides easy and dynamic access to whole genome alignments of human, mouse, rat and fish sequences. This web-based tool (http://ecrbrowser.dcode.org) provides the starting point for discovery of novel genes, identification of distant gene regulatory elements and prediction of transcription factor binding sites. The genome alignment portal of the ECR Browser also permits fast and automated alignments of any user-submitted sequence to the genome of choice. The interconnection of the ECR Browser with other DNA sequence analysis tools creates a unique portal for studying and exploring vertebrate genomes.

Animals↗
PubMed2006

GC-Profile: a web-based tool for visualizing and analyzing the variation of GC content in genomic sequences.

In order to understand the evolution, structure and function of genomes, it is important to know the general compositional features of DNA sequences. Based on the quadratic divergence, a new segmentation algorithm to partition a given genome or DNA sequence into compositionally distinct domains has been put forward. With the aid of the technique of cumulative GC profile, the distribution of segmentation points can be displayed intuitively. We have therefore developed them into GC-Profile, an interactive web-based software system, which can be used to segment prokaryotic and eukaryotic genomes. GC-Profile provides a quantitative and qualitative view of genome organization. Based on the obtained results, the relationships between the G+C content and other genomic features, such as distributions of genes and CpG islands, can be analyzed in a perceivable manner. It shows that GC-Profile would be an appropriate starting point for analyzing the isochore structure of higher eukaryotic genomes, and an intuitive tool for identifying genomic islands in prokaryotic genomes. GC-Profile is freely available at the website http://tubic.tju.edu.cn/GC-Profile/. In addition, precompiled binaries, together with examples and documentation, can also be freely downloaded for a local execution.

Algorithms↗
PubMed2003

Genome-wide prediction, display and refinement of binding sites with information theory-based models.

BACKGROUND: We present Delila-genome, a software system for identification, visualization and analysis of protein binding sites in complete genome sequences. Binding sites are predicted by scanning genomic sequences with information theory-based (or user-defined) weight matrices. Matrices are refined by adding experimentally-defined binding sites to published binding sites. Delila-Genome was used to examine the accuracy of individual information contents of binding sites detected with refined matrices as a measure of the strengths of the corresponding protein-nucleic acid interactions. The software can then be used to predict novel sites by rescanning the genome with the refined matrices. RESULTS: Parameters for genome scans are entered using a Java-based GUI interface and backend scripts in Perl. Multi-processor CPU load-sharing minimized the average response time for scans of different chromosomes. Scans of human genome assemblies required 4-6 hours for transcription factor binding sites and 10-19 hours for splice sites, respectively, on 24- and 3-node Mosix and Beowulf clusters. Individual binding sites are displayed either as high-resolution sequence walkers or in low-resolution custom tracks in the UCSC genome browser. For large datasets, we applied a data reduction strategy that limited displays of binding sites exceeding a threshold information content to specific chromosomal regions within or adjacent to genes. An HTML document is produced listing binding sites ranked by binding site strength or chromosomal location hyperlinked to the UCSC custom track, other annotation databases and binding site sequences. Post-genome scan tools parse binding site annotations of selected chromosome intervals and compare the results of genome scans using different weight matrices. Comparisons of multiple genome scans can display binding sites that are unique to each scan and identify sites with significantly altered binding strengths. CONCLUSIONS: Delila-Genome was used to scan the human genome sequence with information weight matrices of transcription factor binding sites, including PXR/RXRalpha, AHR and NF-kappaB p50/p65, and matrices for RNA binding sites including splice donor, acceptor, and SC35 recognition sites. Comparisons of genome scans with the original and refined PXR/RXRalpha information weight matrices indicate that the refined model more accurately predicts the strengths of known binding sites and is more sensitive for detection of novel binding sites.

Binding Sites↗
PubMed2004

MIPS Arabidopsis thaliana Database (MAtDB): an integrated biological knowledge resource for plant genomics.

Arabidopsis thaliana is the most widely studied model plant. Functional genomics is intensively underway in many laboratories worldwide. Beyond the basic annotation of the primary sequence data, the annotated genetic elements of Arabidopsis must be linked to diverse biological data and higher order information such as metabolic or regulatory pathways. The MIPS Arabidopsis thaliana database MAtDB aims to provide a comprehensive resource for Arabidopsis as a genome model that serves as a primary reference for research in plants and is suitable for transfer of knowledge to other plants, especially crops. The genome sequence as a common backbone serves as a scaffold for the integration of data, while, in a complementary effort, these data are enhanced through the application of state-of-the-art bioinformatics tools. This information is visualized on a genome-wide and a gene-by-gene basis with access both for web users and applications. This report updates the information given in a previous report and provides an outlook on further developments. The MAtDB web interface can be accessed at http://mips.gsf.de/proj/thal/db.

Arabidopsis↗
PubMed2004

Visualization and analysis of microarray and gene ontology data with treemaps.

BACKGROUND: The increasing complexity of genomic data presents several challenges for biologists. Limited computer monitor views of data complexity and the dynamic nature of data in the midst of discovery increase the challenge of integrating experimental results with information resources. The use of Gene Ontology enables researchers to summarize results of quantitative analyses in this framework, but the limitations of typical browser presentation restrict data access. RESULTS: Here we describe extensions to the treemap design to visualize and query genome data. Treemaps are a space-filling visualization technique for hierarchical structures that show attributes of leaf nodes by size and color-coding. Treemaps enable users to rapidly compare sizes of nodes and sub-trees, and we use Gene Ontology categories, levels of RNA, and other quantitative attributes of DNA microarray experiments as examples. Our implementation of treemaps, Treemap 4.0, allows user-defined filtering to focus on the data of greatest interest, and these queried files can be exported for secondary analyses. Links to model system web pages from Treemap 4.0 enable users access to details about specific genes without leaving the query platform. CONCLUSIONS: Treemaps allow users to view and query the data from an experiment on a single computer monitor screen. Treemap 4.0 can be used to visualize various genome data, and is particularly useful for revealing patterns and details within complex data sets.

Animals↗
PubMed2025

GBRAP: A Comprehensive Database and Tool for Exploring Genomic Diversity Across All Domains of Life.

Evolutionary studies require extensive examination of genomic information across all domains of life. Despite the availability of a large number of genomes through GenBank, the effective visualization or comparison of the information they contain is challenging due to many reasons, including their size. We introduce genome-based retrieval and analysis parser, a comprehensive software tool to analyze genome files, and an online database housing an extensive collection of carefully curated, high-quality genome statistics for all the organisms available in the RefSeq database of National Center for Biotechnology Information. Users can either directly search, or select from precategorized groups, the organisms of their choice and retrieve data, and the output is generated as tables containing more than 200 columns of useful genomic information (base counts, GC content, Shannon entropy, codon usage, etc.) separately calculated for different genomic elements (e.g. coding sequences, introns, transfer RNA, ribosomal RNA, noncoding RNA, etc.). The data are independently displayed (if applicable) for each chromosomal, mitochondrial, plastid, or plasmid sequence. All the data can be visualized on the database or downloaded as comma-separated value or Excel files. The genome-based retrieval and analysis parser database is free to access without any registration and is publicly available at http://tacclab.org/gbrap/.

Software↗
PubMed1996

EcoCyc: an encyclopedia of Escherichia coli genes and metabolism.

The encyclopedia of Escherichia coli genes and metabolism (EcoCyc) is a database that combines information about the genome and the intermediary metabolism of E.coli. It describes 2034 genes, 306 enzymes encoded by these genes, 580 metabolic reactions that occur in E.coli and the organization of these reactions into 100 metabolic pathways. The EcoCyc graphical user interface allows query and exploration of the EcoCyc database using visualization tools such as genomic map browsers and automatic layouts of metabolic pathways. EcoCyc spans the space from sequence to function to allow investigation of an unusually broad range of questions. EcoCyc can be thought of as both an electronic review article, because of its copious references to the primary literature, and as an in silico model of E.coli that can be probed and analyzed through computational means.

Computer Communication Networks↗
PubMed2025

wgatools: an ultrafast toolkit for manipulating whole-genome alignments.

SUMMARY: With the rapid development of long-read sequencing technologies, the era of individual complete genomes is approaching. We have developed wgatools, a cross-platform, ultrafast toolkit that supports a range of whole-genome alignment formats, offering practical tools for conversion, processing, evaluation, and visualization of alignments, thereby facilitating population-level genome analysis and advancing functional and evolutionary genomics. AVAILABILITY AND IMPLEMENTATION: wgatools supports diverse formats and can process, filter, and statistically evaluate alignments, perform alignment-based variant calling, and visualize alignments both locally and genome-wide. Built with Rust for efficiency and safe memory usage, it ensures fast performance and can handle large datasets consisting of hundreds of genomes. wgatools is published as free software under the MIT open-source license, and its source code is freely available at https://github.com/wjwei-handsome/wgatools and https://zenodo.org/records/14882797.

Software↗
PubMed1994

Murine and bovine blue cone pigment genes: cloning and characterization of two new members of the S family of visual pigments.

Two novel visual pigment genes, mouse blue and bovine blue, have been isolated from mouse and bovine genomic libraries, respectively, using a human blue cone pigment cDNA as probe. Corresponding cDNA clones have also been obtained from mouse retinas. The intron-exon boundaries for the mouse gene were determined by comparing the genomic and cDNA sequences. The visual pigments encoded by the mouse and bovine blue pigment genes are highly homologous to each other (89% amino acid identity) and to human blue and chicken violet cone pigments (greater than 80% identity), but are less homologous to chicken or goldfish blue cone pigments (less than 50% identity). These results indicate that phylogenetically both mouse and bovine blue pigments belong to the S branch of visual pigments, rather than to the M branch.

Amino Acid Sequence↗