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Results for “genomic data”

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PubMed1998

The Merck Gene Index browser: an extensible data integration system for gene finding, gene characterization and EST data mining.

MOTIVATION: To make effective use of the vast amounts of expressed sequence tag (EST) sequence data generated by the Merck-sponsored EST project and other similar efforts, sequences must be organized into gene classes, and scientists must be able to 'mine' the gene class data in the context of related genomic data. RESULTS: This paper presents the Merck Gene Index browser, an easily extensible, World Wide Web-based system for mining the Merck Gene Index (MGI) and related genomic data. The MGI is a non-redundant set of clones and sequences, each representing a distinct gene, constructed from all high-quality 3' EST sequences generated by the Merck-sponsored EST project. The MGI browser integrates data from a variety of sources and storage formats, both local and remote, using an eclectic integration strategy, including a federation of relational databases, a local data warehouse and simple hypertext links. Data currently integrated include: LENS cDNA clone and EST data, dbEST protein and non-EST nucleic acid similarity data, WashU sequence chromatograms. Entrez sequence and Medline entries, and UniGene gene clusters. Flatfile sequence data are accessed using the Bioapps server, an internally developed client-server system that supports generic sequence analysis applications. Browser data are retrieved and formatted by means of the Bioinformatics Data Integration Toolkit (B-DIT), a new suite of Perl routines.

Abstracting and Indexing↗

PubMed2025

Structural variation detection and association analysis of whole-genome-sequence data from 16,543 Alzheimer's disease sequencing project subjects.

INTRODUCTION: The role of structural variations (SVs) in Alzheimer's disease (AD) remains understudied. METHODS: We analyzed whole-genome sequencing data from the Alzheimer's Disease Sequencing Project (N = 16,543) and identified 400,234 (168,223 high-quality) SVs. Laboratory validation yielded a sensitivity of 82% (85% for high-quality). RESULTS: We found a burden of singletons (odds ratio [OR] = 1.07, p = 0.0017) and homozygous deletions (OR = 1.14, p < 0.0001) in cases. On AD genes, we observed the ultra-rare SVs associated with the disease, including protein-altering SVs in ABCA7, APP, PLCG2, and SORL1. Twenty-one SVs are in linkage disequilibrium (LD) with known AD-risk variants, exemplified by a 5k deletion in LD (R2 = 0.99) with rs143080277 in NCK2. We identified a rare deletion near RNA5SP293 associated with AD (OR = 1.99, p = 1.3 × 10-5), which was replicated using an independent dataset. DISCUSSION: This study highlights the pivotal role of SVs in AD genetics. HIGHLIGHTS: Observed a significant burden of singletons and homozygous deletions in Alzheimer's disease (AD) patients. Identified rare protein-altering structural variations (SVs) in ABCA7, APP, PLCG2, and SORL1. Established linkages between SVs and AD risk-associated single nucleotide variants (SNVs). Discovered a novel deletion near RNA5SP293 linked to AD, replicated independently. Uncovered over-representation of SVs in neuronal function pathways.

Humans↗

PubMed2004

Sight: automating genomic data-mining without programming skills.

SUMMARY: We created and tested Sight, a Java-based package that provides a user-friendly interface to generate and connect agents for automatic genomic data-mining for individual requirements without requiring programming skills from the user. AVAILABILITY: http://physiologie.uni-ulm.de//Seiten/Arbeitsgruppe/Jurkat-Rott/Jurkat-Rott.htm. The system does not require additional components and runs on IBM PCs under Windows (NT 4.0, 2000 and XP) or Linux (Phat 4.0 and Mandrake 9.0).

Algorithms↗

PubMed2006

Genome-wide prediction of genetic interactions in a metazoan.

Genetic interactions provide information about genes and processes with overlapping functions in biological systems. For Saccharomyces cerevisiae, computational integration of multiple types of functional genomic data is used to generate genome-wide predictions of genetic interactions. However, this methodology cannot be applied to the vastly more complex genome of metazoans, and only recently has the first metazoan genome-wide prediction of genetic interactions been reported. The prediction for Caenorhabditis elegans was generated by computationally integrating functional genomic data from S. cerevisiae, C. elegans and Drosophila melanogaster. This achievement is an important step toward system-level understanding of biological systems and human diseases.

Animals↗

PubMed2025

Gencube: centralized retrieval and integration of multi-omics resources from leading databases.

MOTIVATION: The volume of multi-omics data for diverse species is growing at an unprecedented rate, with new genome assemblies, related annotations, and high-throughput sequencing resources being submitted daily to various genomic data repositories. In response to this data influx, both existing and new databases are establishing optimized hierarchical structures to manage the vast amount of information. However, the lack of accessible command-line tools, combined with the functional limitations and unintuitive design of existing options, presents significant challenges for researchers. This gap underscores a critical need for a tool that enables streamlined retrieval and integration of omics data across these diverse repositories. RESULTS: We have developed Gencube, a command-line tool that enables centralized retrieval and integration of a comprehensive set of six different data types-genome assemblies, gene sets, annotations, sequences, comparative genomic data, and NGS-based omics resources-from various leading databases. AVAILABILITY AND IMPLEMENTATION: Gencube is a free and open-source tool, with its code available on GitHub: https://github.com/snu-cdrc/gencube and also archived on Zenodo: https://doi.org/10.5281/zenodo.14607649.

Databases, Genetic↗

PubMed2026

Evaluation of amplicon-based nanopore sequencing for foot-and-mouth disease viruses in clinical and environmental samples.

Foot-and-mouth disease (FMD) causes severe global economic loss, necessitating rapid viral characterization. Nanopore sequencing provides a simple, real-time workflow suitable for on-site outbreak response, addressing the limitations of conventional methods. In this study, we optimized a previously published amplicon-based protocol and used this method to characterize a diverse range of samples (vesicular fluid, epithelium, serum, nasal/oral swabs, and environmental samples) collected during FMD outbreaks in 2025 in the Republic of Korea. Of the 129 samples collected, we successfully recovered complete genomes from 37 samples and VP1 sequences from 85 samples. Amplifying the S-fragment in isolation and separately barcoding each pool of PCR amplicons markedly improved sequence recovery. Furthermore, sequencing success depended on viral load and sample type. Based on comparisons with real-time RT-PCR results, whole-genome sequence (WGS) recovery exceeded 77.3% at cycle threshold (Ct) values ≤25 across all clinical samples. In the Ct > 30 category, serum samples yielded the highest WGS recovery rates (44.4%). This rate was markedly higher than the success rates observed for epithelium (20.0%) and nasal swabs (9.1%), whereas oral swabs and environmental samples failed to yield any sequences (0%). However, VP1 recovery from environmental samples reached 80% at Ct ≤ 30 (8/10), providing an approach to enable non-invasive monitoring. These findings demonstrate that amplicon-based nanopore sequencing is a practical method for the rapid generation of genomic data during FMD outbreaks.IMPORTANCEAlthough rapid detection and genomic data analysis are crucial for effective foot-and-mouth disease (FMD) control, the collection of these data can be challenging for certain sample types and impacted by reduced viral loads that result from nationwide FMD vaccination. This study provides a practical solution through large-scale evaluation of an optimized amplicon-based nanopore sequencing protocol to enhance the sequencing success rates for both clinical and environmental samples. Using a modified protocol to enhance genome recovery, we demonstrated that sequence data could be retrieved from diverse sample types (even with high real-time RT-PCR cycle threshold values). We identified serum as the most suitable sample, with environmental sample sequencing allowing for non-invasive monitoring during outbreaks. These results support the use of nanopore sequencing for rapid genomic analysis, particularly in outbreak responses, such as rapid surveillance, emergency vaccine selection, and epidemiological monitoring.

Foot-and-Mouth Disease↗

PubMed2004

Novel analytical methods applied to type 1 diabetes genome-scan data.

Complex traits like type 1 diabetes mellitus (T1DM) are generally taken to be under the influence of multiple genes interacting with each other to confer disease susceptibility and/or protection. Although novel methods are being developed, analyses of whole-genome scans are most often performed with multipoint methods that work under the assumption that multiple trait loci are unrelated to each other; that is, most models specify the effect of only one locus at a time. We have applied a novel approach, which includes decision-tree construction and artificial neural networks, to the analysis of T1DM genome-scan data. We demonstrate that this approach (1) allows identification of all major susceptibility loci identified by nonparametric linkage analysis, (2) identifies a number of novel regions as well as combinations of markers with predictive value for T1DM, and (3) may be useful in characterizing markers in linkage disequilibrium with protective-gene variants. Furthermore, the approach outlined here permits combined analyses of genetic-marker data and information on environmental and clinical covariates.

Decision Trees↗

PubMed2004

Computational prediction of operons in Synechococcus sp. WH8102.

We computationally predict operons in the Synechococcus sp. WH8102 genome based on three types of genomic data: intergenic distances, COG gene functions and phylogenetic profiles. In the proposed method, we first estimate a log-likelihood distribution for each type of genomic data, and then fuse these distribution information by a perceptron to discriminate pairs of genes within operons (WO pairs) from those across transcription unit borders (TUB pairs). Computational experiments demonstrated that WO pairs tend to have shorter intergenic distances, a higher probability being in the same COG functional categories and more similar phylogenetic profiles than TUB pairs, indicating their powerful capabilities for operon prediction. By testing the method on 236 known operons of Escherichia coli K12, an overall accuracy of 83.8% is obtained by joint learning from multiple types of genomic data, whereas individual information source yields accuracies of 80.4%, 74.4%, and 70.6% respectively. We have applied this new approach, in conjunction with our previous comparative genome analysis-based approach, to predict 556 (putative) operons in WH8102. All predicted data are available at (http://www.cs.ucr.edu/~xin/operons.htm) for public use.

Computational Biology↗

PubMed2005

Subset clustering of binary sequences, with an application to genomic abnormality data.

This article develops a model-based approach to clustering multivariate binary data, in which the attributes that distinguish a cluster from the rest of the population may depend on the cluster being considered. The clustering approach is based on a multivariate Dirichlet process mixture model, which allows for the estimation of the number of clusters, the cluster memberships, and the cluster-specific parameters in a unified way. Such a clustering approach has applications in the analysis of genomic abnormality data, in which the development of different types of tumors may depend on the presence of certain abnormalities at subsets of locations along the genome. Additionally, such a mixture model provides a nonparametric estimation scheme for dependent sequences of binary data.

Carcinoma, Renal Cell↗

PubMed2024

Deep-Learning Model for Tumor-Type Prediction Using Targeted Clinical Genomic Sequencing Data.

UNLABELLED: Tumor type guides clinical treatment decisions in cancer, but histology-based diagnosis remains challenging. Genomic alterations are highly diagnostic of tumor type, and tumor-type classifiers trained on genomic features have been explored, but the most accurate methods are not clinically feasible, relying on features derived from whole-genome sequencing (WGS), or predicting across limited cancer types. We use genomic features from a data set of 39,787 solid tumors sequenced using a clinically targeted cancer gene panel to develop Genome-Derived-Diagnosis Ensemble (GDD-ENS): a hyperparameter ensemble for classifying tumor type using deep neural networks. GDD-ENS achieves 93% accuracy for high-confidence predictions across 38 cancer types, rivaling the performance of WGS-based methods. GDD-ENS can also guide diagnoses of rare type and cancers of unknown primary and incorporate patient-specific clinical information for improved predictions. Overall, integrating GDD-ENS into prospective clinical sequencing workflows could provide clinically relevant tumor-type predictions to guide treatment decisions in real time. SIGNIFICANCE: We describe a highly accurate tumor-type prediction model, designed specifically for clinical implementation. Our model relies only on widely used cancer gene panel sequencing data, predicts across 38 distinct cancer types, and supports integration of patient-specific nongenomic information for enhanced decision support in challenging diagnostic situations. See related commentary by Garg, p. 906. This article is featured in Selected Articles from This Issue, p. 897.

Humans↗

PubMed2005

PLATCOM: a Platform for Computational Comparative Genomics.

MOTIVATION: As more whole genome sequences become available, comparing multiple genomes at the sequence level can provide insight into new biological discovery. However, there are significant challenges for genome comparison. The challenge includes requirement for computational resources owing to the large volume of genome data. More importantly, since the choice of genomes to be compared is entirely subjective, there are too many choices for genome comparison. For these reasons, there is pressing need for bioinformatics systems for comparing multiple genomes where users can choose genomes to be compared freely. RESULTS: PLATCOM (Platform for Computational Comparative Genomics) is an integrated system for the comparative analysis of multiple genomes. The system is built on several public databases and a suite of genome analysis applications are provided as exemplary genome data mining tools over these internal databases. Researchers are able to visually investigate genomic sequence similarities, conserved gene neighborhoods, conserved metabolic pathways and putative gene fusion events among a set of selected multiple genomes. AVAILABILITY: http://platcom.informatics.indiana.edu/platcom

Chromosome Mapping↗

PubMed2005

Variation and evolution of biomolecular systems: searching for functional relevance.

The availability of genome sequences and functional genomics data from multiple species enables us to compare the composition of biomolecular systems like biochemical pathways and protein complexes between species. Here, we review small- and large-scale, "genomics-based" approaches to biomolecular systems variation. In general, caution is required when comparing the results of bioinformatics analyses of genomes or of functional genomics data between species. Limitations to the sensitivity of sequence analysis tools and the noisy nature of genomics data tend to lead to systematic overestimates of the amount of variation. Nevertheless, the results from detailed manual analyses, and of large-scale analyses that filter out systematic biases, point to a large amount of variation in the composition of biomolecular systems. Such observations challenge our understanding of the function of the systems and their individual components and can potentially facilitate the identification and functional characterization of sub-systems within a system. Mapping the inter-species variation of complex biomolecular systems on a phylogenetic species tree allows one to reconstruct their evolution.

Evolution, Molecular↗

PubMed2004

Genome-scale data, angiosperm relationships, and "ending incongruence": a cautionary tale in phylogenetics.

As systematists grapple with assembling the Tree of Life, recent studies have encouraged a genomic-scale approach, obtaining DNA sequence data for entire nuclear, plastid or mitochondrial genomes for a few exemplar taxa. Some have proclaimed that this comparative genomic strategy heralds the end of incongruence in phylogeny reconstruction. Although we applaud the use of many genes to resolve phylogenetic patterns, there is a significant caveat. In spite of, or even because of, the abundant data per taxon, whole-genome sequencing for a few exemplars can provide completely resolved and strongly supported, but incorrect, evolutionary reconstructions. We provide a conspicuous example that includes Amborella, the putative sister of all other extant angiosperms, highlighting the limits of phylogenetics when whole genomes are used but taxon sampling is poor.

Codon↗

PubMed2004

Combining transcriptome data with genomic and cDNA sequence alignments to make confident functional assignments for Aspergillus nidulans genes.

Whole genome sequencing of several filamentous ascomycetes is complete or in progress; these species, such as Aspergillus nidulans, are relatives of Saccharomyces cerevisiae. However, their genomes are much larger and their gene structure more complex, with genes often containing multiple introns. Automated annotation programs can quickly identify open reading frames for hypothetical genes, many of which will be conserved across large evolutionary distances, but further information is required to confirm functional assignments. We describe a comparative and functional genomics approach using sequence alignments and gene expression data to predict the function of Aspergillus nidulans genes. By highlighting examples of discrepancies between the automated genome annotation and cDNA or EST sequencing, we demonstrate that the greater complexity of gene structure in filamentous fungi demands independent data on gene expression and the gene sequence be used to make confident functional assignments.

Aspergillus nidulans↗

PubMed2000

The current excitement in bioinformatics-analysis of whole-genome expression data: how does it relate to protein structure and function?

Whole-genome expression profiles provide a rich new data-trove for bioinformatics. Initial analyses of the profiles have included clustering and cross-referencing to 'external' information on protein structure and function. Expression profile clusters do relate to protein function, but the correlation is not perfect, with the discrepancies partially resulting from the difficulty in consistently defining function. Other attributes of proteins can also be related to expression-in particular, structure and localization-and sometimes show a clearer relationship than function.

Computational Biology↗

PubMed2026

The European Health Data Space and the Secondary Use of Sensitive Health Data.

INTRODUCTION: The European Health Data Space (EHDS) is one of the European Union's most ambitious data-governance projects. It aims to create a common framework through which electronic health data can be accessed and reused across Member States for care, research, innovation, policy, and public-interest purposes. Its practical viability depends not only on digital infrastructure, but also on legal, ethical, and organisational harmonisation, particularly for genetic and genomic data. METHODS: This paper examines the EHDS with emphasis on the secondary use of health data. It reviews the EHDS institutional architecture, discusses Finland's Findata as a national model for structured access, and analyses challenges for data holders and data donors, including interoperability, governance burdens, privacy protection, residual re-identification risk, and genomic-data sensitivity. RESULTS: A cross-border cancer-genomics case study shows that the EHDS can streamline data discovery and the routing of access requests, but does not by itself eliminate legal fragmentation, heterogeneous ethics review, and consent-related barriers. DISCUSSION: Effective implementation will require harmonisation beyond infrastructure, including clearer consent standards, more consistent ethics procedures, interoperable metadata, and proportionate safeguards for genomic data.

Electronic Health Records↗

PubMed2003

What is the relevance of bioinformatics to pharmacology?

Although bioinformatics achieved prominence because of its central role in genome data storage, management and analysis, its focus has shifted as the life sciences exploit these data. In pharmacology, genomic, transcriptomic and proteomic data are being used in the quest for drugs that fulfill unmet medical needs, are disease modifying or curative and are more effective and safer than current drugs. Bioinformatics is used in drug target identification and validation and in the development of biomarkers and toxicogenomic and pharmacogenomic tools to maximize the therapeutic benefit of drugs. Now that the 'parts list' of cellular signalling pathways is available, integrated computational and experimental programmes are being developed, with the goal of enabling in silico pharmacology by linking the genome, transcriptome and proteome to cellular pathophysiology.

Computational Biology↗

PubMed2001

Visualizing associations between genome sequences and gene expression data using genome-mean expression profiles.

The combination of genome-wide expression patterns and full genome sequences offers a great opportunity to further our understanding of the mechanisms and logic of transcriptional regulation. Many methods have been described that identify sequence motifs enriched in transcription control regions of genes that share similar gene expression patterns. Here we present an alternative approach that evaluates the transcriptional information contained by specific sequence motifs by computing for each motif the mean expression profile of all genes that contain the motif in their transcription control regions. These genome-mean expression profiles (GMEP's) are valuable for visualizing the relationship between genome sequences and gene expression data, and for characterizing the transcriptional importance of specific sequence motifs. Analysis of GMEP's calculated from a dataset of 519 whole-genome microarray experiments in Saccharomyces cerevisiae show a significant correlation between GMEP's of motifs that are reverse complements, a result that supports the relationship between GMEP's and transcriptional regulation. Hierarchical clustering of GMEP's identifies clusters of motifs that correspond to binding sites of well-characterized transcription factors. The GMEP's of these clustered motifs have patterns of variation across conditions that reflect the known activities of these transcription factors. Software that computed GMEP's from sequence and gene expression data is available under the terms of the Gnu Public License from http://rana.lbl.gov/.

Algorithms↗