Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “genomic features”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

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

At least 55 records · Page 3Linked to original sources
PubMed2025

Clinical and molecular landscape of metastatic extramammary Paget's disease.

BACKGROUND: Extramammary Paget's disease (EMPD) is a rare malignancy without established systemic therapy. EMPD shares molecular features with breast cancer, such as human epidermal growth factor receptor 2 (HER2) and hormone receptor (HR) expression, but their clinical relevance remains unclear. MATERIALS AND METHODS: Tumors from 20 metastatic invasive EMPD cases were analyzed for molecular and biological features. Genomic features, transcriptomic profiles, and HER2 and HR expression status were investigated using immunohistochemistry, fluorescence in situ hybridization, and targeted-genome next-generation sequencing and nCounter BC360 panels. Metastatic breast cancer samples were used as a comparison to clarify metastatic EMPD's clinical relevance. RESULTS: Estrogen receptor expression was observed in 45% of EMPD tumors, while only 10% expressed progesterone receptor. HER2 was overexpressed in 30% of cases, and HER2-directed therapies were durably effective. Among 8 patients with NGS data, 63% (5/8) harbored oncogenic ERBB2 alterations independent of HER2 expression. BC360 profiling revealed biological differences between EMPD and breast cancer, particularly poor biological compatibility for HR-positive tumors. Immune profiling showed that a subset of EMPD tumors exhibited CD8+ T-cell signatures and PD-1/PD-L1 gene expression comparable to triple-negative breast cancer. The median overall survival was 22.1 months (95% CI, 12.0-42.2), with 16 patients (80%) treated with systemic therapy, including anti-HER2 therapy, hormonal therapy, or cytotoxic therapies based on their molecular features. CONCLUSIONS: This study highlights the unique molecular and biological features of metastatic EMPD, emphasizing the need for tailored treatment approaches. This information should be used to guide future clinical strategies for metastatic EMPD.

Humans↗
PubMed2026

Using the DNA language model, GROVER, to parse effects of sequence, chromatin and regulatory features on genome stability.

MOTIVATION: Genome stability is shaped by DNA sequence and chromatin context, but their relative contributions to double-strand break (DSB) sensitivity remain unclear. RESULTS: We show that the DNA language model, GROVER, can infer DSB location based on sequence. DSB hotspots tend to contain GC-rich sequences that belong to promoters, genes and short interspersed nuclear elements (SINEs). Additionally, we identified several specific short sequences (tokens) that are associated with modulating DSB sensitivity. Another model using chromatin and genome regulatory features outperforms the sequence-only model, highlighting complementary and cell-type specific information. Integrating sequence and genome biological features yields the best performance, demonstrating their synergy. Analyzing this model revealed that, dependent on the sample, genome stability information encoded in H3K36me3 and DNase-seq can be learned from the sequence, but not H3K27ac or H3K9me3. Embedding chromatin data directly into the GROVER architecture enabled cell-type specific modeling with performance matching the full chromatin feature model. Our results suggest that while chromatin and regulatory context provides important information, such as cell-type specificity, much of the information shaping DSB patterns is already encoded in the DNA sequence itself. Our integrative modeling approach not only reveals DSB patterns but also provides a generalizable strategy for tracing predictions in genomic data. AVAILABILITY: Data, models, and a tutorial are available on Zenodo.

Chromatin↗
PubMed2006

REEF: searching REgionally Enriched Features in genomes.

BACKGROUND: In Eukaryotic genomes, different features including genes are not uniformly distributed. The integration of annotation information and genomic position of functional DNA elements in the Eukaryotic genomes opened the way to test novel hypotheses of higher order genome organization and regulation of expression. RESULTS: REEF is a new tool, aimed at identifying genomic regions enriched in specific features, such as a class or group of genes homogeneous for expression and/or functional characteristics. The method for the calculation of local feature enrichment uses test statistic based on the Hypergeometric Distribution applied genome-wide by using a sliding window approach and adopting the False Discovery Rate for controlling multiplicity. REEF software, source code and documentation are freely available at http://telethon.bio.unipd.it/bioinfo/reef/. CONCLUSION: REEF can aid to shed light on the role of organization of specific genomic regions in the determination of their functional role.

Algorithms↗
PubMed1998

Genomic disorders: structural features of the genome can lead to DNA rearrangements and human disease traits.

Molecular medicine began with Pauling's seminal work, which recognized sickle-cell anemia as a molecular disease, and with Ingram's demonstration of a specific chemical difference between the hemoglobins of normal and sickled human red blood cells. During the four decades that followed, investigations have focused on the gene--how mutations specifically alter DNA and how these changes affect the structure and expression of encoded proteins. Recently, however, the advances of the human genome project and the completion of total genome sequences for yeast and many bacterial species, have enabled investigators to view genetic information in the context of the entire genome. As a result, we recognize that the mechanisms for some genetic diseases are best understood at a genomic level. The evolution of the mammalian genome has resulted in the duplication of genes, gene segments and repeat gene clusters. This genome architecture provides substrates for homologous recombination between nonsyntenic regions of chromosomes. Such events can result in DNA rearrangements that cause disease.

Chromosome Aberrations↗
PubMed2005

Three Prochlorococcus cyanophage genomes: signature features and ecological interpretations.

The oceanic cyanobacteria Prochlorococcus are globally important, ecologically diverse primary producers. It is thought that their viruses (phages) mediate population sizes and affect the evolutionary trajectories of their hosts. Here we present an analysis of genomes from three Prochlorococcus phages: a podovirus and two myoviruses. The morphology, overall genome features, and gene content of these phages suggest that they are quite similar to T7-like (P-SSP7) and T4-like (P-SSM2 and P-SSM4) phages. Using the existing phage taxonomic framework as a guideline, we examined genome sequences to establish "core" genes for each phage group. We found the podovirus contained 15 of 26 core T7-like genes and the two myoviruses contained 43 and 42 of 75 core T4-like genes. In addition to these core genes, each genome contains a significant number of "cyanobacterial" genes, i.e., genes with significant best BLAST hits to genes found in cyanobacteria. Some of these, we speculate, represent "signature" cyanophage genes. For example, all three phage genomes contain photosynthetic genes (psbA, hliP) that are thought to help maintain host photosynthetic activity during infection, as well as an aldolase family gene (talC) that could facilitate alternative routes of carbon metabolism during infection. The podovirus genome also contains an integrase gene (int) and other features that suggest it is capable of integrating into its host. If indeed it is, this would be unprecedented among cultured T7-like phages or marine cyanophages and would have significant evolutionary and ecological implications for phage and host. Further, both myoviruses contain phosphate-inducible genes (phoH and pstS) that are likely to be important for phage and host responses to phosphate stress, a commonly limiting nutrient in marine systems. Thus, these marine cyanophages appear to be variations of two well-known phages-T7 and T4-but contain genes that, if functional, reflect adaptations for infection of photosynthetic hosts in low-nutrient oceanic environments.

Bacteriophage T7↗
PubMed1999

Gene organization and sequence of the region containing the ribosomal protein genes RPL13A and RPS11 in the human genome and conserved features in the mouse genome.

We have determined the organization and sequence of the region containing two ribosomal protein (rp) genes in the human and mouse genomes. The two genes, human RPL13A and RPS11, and mouse Rpl13a and Rps11, are tandemly located in both genomes with an interval of only 4.6kb in the case of the human genes and 1.6kb in the case of the mouse genes. The human RPL13A and RPS11 are 4236bp and 3254bp in length and comprise eight and five exons respectively, whereas the mouse Rps11 is 1951bp long and has five exons. Structural comparison of these genes, including previously reported mouse Rpl13a, revealed a significant conservation of sequences in the promoter regions. Although most rp genes are dispersed throughout the human genome, the conserved features and adjacent localization indicate possible coordinate transcription of the two genes. Furthermore, we have found that four small nucleolar RNA (snoRNA) genes are located in the introns of the two rp genes, both human and mouse. U32, U33, and U34 snoRNAs are encoded in introns 2, 4, and 5 of RPL13A respectively, and U35 in the sixth intron of RPL13A and the third intron of RPS11. The same organization of these snoRNA genes was also observed in the case of the mouse genes.

Animals↗
PubMed2001

Implications of the human genome for understanding human biology and medicine.

Clinical researchers, practicing physicians, patients, and the general public now live in a world in which the 2.9 billion nucleotide codes of the human genome are available as a resource for scientific discovery. Some of the findings from the sequencing of the human genome were expected, confirming knowledge presaged by many decades of research in both human and comparative genetics. Other findings are unexpected in their scientific and philosophical implications. In either case, the availability of the human genome is likely to have significant implications, first for clinical research and then for the practice of medicine. This article provides our reflections on what the new genomic knowledge might mean for the future of medicine and how the new knowledge relates to what we knew in the era before the availability of the genome sequence. In addition, practicing physicians in many communities are traditionally also ambassadors of science, called on to translate arcane data or the complex ramifications of biology into a language understood by the public at large. This article also may be useful for physicians who serve in this capacity in their communities. We address the following issues: the number of protein-coding genes in the human genome and certain classes of noncoding repeat elements in the genome; features of genome evolution, including large-scale duplications; an overview of the predicted protein set to highlight prominent differences between the human genome and other sequenced eukaryotic genomes; and DNA variation in the human genome. In addition, we show how this information lays the foundations for ongoing and future endeavors that will revolutionize biomedical research and our understanding of human health.

Clinical Medicine↗
PubMed1987

Reiterated DNA sequences in Rhizobium and Agrobacterium spp.

Repeated DNA sequences are a general characteristic of eucaryotic genomes. Although several examples of DNA reiteration have been found in procaryotic organisms, only in the case of the archaebacteria Halobacterium halobium and Halobacterium volcanii [C. Sapienza and W. F. Doolittle, Nature (London) 295:384-389, 1982], has DNA reiteration been reported as a common genomic feature. The genomes of two Rhizobium phaseoli strains, one Rhizobium meliloti strain, and one Agrobacterium tumefaciens strain were analyzed for the presence of repetitive DNA. Rhizobium and Agrobacterium spp. are closely related soil bacteria that interact with plants and that belong to the taxonomical family Rhizobiaceae. Rhizobium species establish a nitrogen-fixing symbiosis in the roots of legumes, whereas Agrobacterium species is a pathogen in different plants. The four strains revealed a large number of repeated DNA sequences. The family size was usually small, from 2 to 5 elements, but some presented more than 10 elements. Rhizobium and Agrobacterium spp. contain large plasmids in addition to the chromosomes. Analysis of the two Rhizobium strains indicated that DNA reiteration is not confined to the chromosome or to some plasmids but is a property of the whole genome.

Cloning, Molecular↗
PubMed2001

The 1.4-Mb CMT1A duplication/HNPP deletion genomic region reveals unique genome architectural features and provides insights into the recent evolution of new genes.

Duplication and deletion of the 1.4-Mb region in 17p12 that is delimited by two 24-kb low copy number repeats (CMT1A-REPs) represent frequent genomic rearrangements resulting in two common inherited peripheral neuropathies, Charcot-Marie-Tooth disease type 1A (CMT1A) and hereditary neuropathy with liability to pressure palsy (HNPP). CMT1A and HNPP exemplify a paradigm for genomic disorders wherein unique genome architectural features result in susceptibility to DNA rearrangements that cause disease. A gene within the 1.4-Mb region, PMP22, is responsible for these disorders through a gene-dosage effect in the heterozygous duplication or deletion. However, the genomic structure of the 1.4-Mb region, including other genes contained within the rearranged genomic segment, remains essentially uncharacterized. To delineate genomic structural features, investigate higher-order genomic architecture, and identify genes in this region, we constructed PAC and BAC contigs and determined the complete nucleotide sequence. This CMT1A/HNPP genomic segment contains 1,421,129 bp of DNA. A low copy number repeat (LCR) was identified, with one copy inside and two copies outside of the 1.4-Mb region. Comparison between physical and genetic maps revealed a striking difference in recombination rates between the sexes with a lower recombination frequency in males (0.67 cM/Mb) versus females (5.5 cM/Mb). Hypothetically, this low recombination frequency in males may enable a chromosomal misalignment at proximal and distal CMT1A-REPs and promote unequal crossing over, which occurs 10 times more frequently in male meiosis. In addition to three previously described genes, five new genes (TEKT3, HS3ST3B1, NPD008/CGI-148, CDRT1, and CDRT15) and 13 predicted genes were identified. Most of these predicted genes are expressed only in embryonic stages. Analyses of the genomic region adjacent to proximal CMT1A-REP indicated an evolutionary mechanism for the formation of proximal CMT1A-REP and the creation of novel genes by DNA rearrangement during primate speciation.

Animals↗
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↗
PubMed2006

Mitochondrial genomes of parasitic arthropods: implications for studies of population genetics and evolution.

Over 39000 species of arthropods parasitize humans, domestic animals and wildlife. Despite their medical, veterinary and economic importance, most aspects of the population genetics and evolution of the vast majority of parasitic arthropods are poorly understood. Mitochondrial genomes are a rich source of markers for studies of population genetics and evolution. These markers include (1) nucleotide sequences of each of the 37 mitochondrial genes and non-coding regions; (2) concatenated nucleotide sequences of 2 or more genes; and (3) genomic features, such as gene duplications, gene rearrangements, and changes in gene content and secondary structures of RNAs. To date, the mitochondrial genomes of over 700 species of multi-cellular animals have been sequenced entirely, however, only 24 of these species are parasitic arthropods. Of the mitochondrial genome markers, only the nucleotide sequences of 4 mitochondrial genes, cox1, cob, rrnS and rrnL, have been well explored in population genetic and evolutionary studies of parasitic arthropods whereas the sequences of the other 33 genes, and various genomic features have not. We review current knowledge of the mitochondrial genomes of parasitic arthropods, summarize applications of mitochondrial genes and genomic features in population genetic and evolutionary studies, and highlight prospects for future research.

Animals↗
PubMed2004

MaGIC: a program to generate targeted marker sets for genome-wide association studies.

High-throughput genotyping technologies such as DNA pooling and DNA microarrays mean that whole-genome screens are now practical for complex disease gene discovery using association studies. Because it is currently impractical to use all available markers, a subset is typically selected on the basis of required saturation density. Restricting markers to those within annotated genomic features of interest (e.g., genes or exons) or within feature-rich regions, reduces workload and cost while retaining much information. We have designed a program (MaGIC) that exploits genome assembly data to create lists of markers correlated with other genomic features. Marker lists are generated at a user-defined spacing and can target features with a user-defined density. Maps are in base pairs or linkage disequilibrium units (LDUs) as derived from the International HapMap data, which is useful for association studies and fine-mapping. Markers may be selected on the basis of heterozygosity and source database, and single nucleotide polymorphism (SNP) markers may additionally be selected on the basis of validation status. The import function means the method can be used for any genomic features such as housekeeping genes, long interspersed elements (LINES), or Alu repeats in humans, and is also functional for other species with equivalent data. The program and source code is freely available at http://cogent.iop.kcl.ac.uk/MaGIC.cogx.

Algorithms↗
PubMedDate not supplied

Using chromosome features in genome mapping.

Studies of the structure and function of chromosomal features and sequence elements have much to contribute to and gain from genome mapping. Abnormalities in chromosome banding associated with specific diseases have pinpointed regions of the genome for intensive analysis. An improved understanding of chromosome organization can facilitate gene identification and isolation, as well as enabling sophisticated genetic and vector systems for genome studies to be devised.

Animals↗
PubMed2000

Criteria for gene identification and features of genome organization: analysis of 6.5 Mb of DNA sequence from human chromosome 21.

To establish criteria for and the limitations of novel gene identification, to identify novel genes of potential relevance to Down Syndrome and to investigate features of genome organization, 6. 550kb. In total, 41 novel gene models were predicted, and for a subset of these, RT-PCR experiments helped to verify and refine the models, and were used to assess expression in early development and in adult brain regions of potential relevance to Down syndrome. Results suggest generally low and/or restricted patterns of expression, and also reveal examples of complex alternative processing, especially in brain, that may have important implications for regulation of protein function. Analysis of complete gene structures of the known genes identified a number of very large introns, a number of very short intergenic distances, and at least one potentially bi-directional promoter. At least 3/4 of known genes and 1/2 of predicted genes are associated with CpG islands. For novel genes, three cases of overlapping genes are predicted. Results of these analyses illustrate some of the complexities inherent in mammalian genome organization and some of the limitations of current sequence analysis technologies. They also doubled the number of potential genes within the region.

Adult↗
PubMed2004

Sockeye: a 3D environment for comparative genomics.

Comparative genomics techniques are used in bioinformatics analyses to identify the structural and functional properties of DNA sequences. As the amount of available sequence data steadily increases, the ability to perform large-scale comparative analyses has become increasingly relevant. In addition, the growing complexity of genomic feature annotation means that new approaches to genomic visualization need to be explored. We have developed a Java-based application called Sockeye that uses three-dimensional (3D) graphics technology to facilitate the visualization of annotation and conservation across multiple sequences. This software uses the Ensembl database project to import sequence and annotation information from several eukaryotic species. A user can additionally import their own custom sequence and annotation data. Individual annotation objects are displayed in Sockeye by using custom 3D models. Ensembl-derived and imported sequences can be analyzed by using a suite of multiple and pair-wise alignment algorithms. The results of these comparative analyses are also displayed in the 3D environment of Sockeye. By using the Java3D API to visualize genomic data in a 3D environment, we are able to compactly display cross-sequence comparisons. This provides the user with a novel platform for visualizing and comparing genomic feature organization.

Animals↗
PubMed2026

Isolation, identification, and genomic characterization of Staphylococcus aureus phage vB_SauL_202595 and its bacteriostatic application in dairy products.

Staphylococcus aureus is an important pathogen associated with bovine mastitis and dairy product contamination, posing economic and public health risks through the food chain. In this study, a temperate phage, vB_SauL_202595, was isolated from a dairy farm environmental sample using S. aureus SHZ-0127 as the host, and its biological characteristics, genomic features, and antibacterial activity in dairy matrices were evaluated. vB_SauL_202595 lysed 18 of 66 tested S. aureus strains, with a lysis susceptibility rate of 27.3%, including 5 highly susceptible strains, indicating a relatively limited host range. The optimal multiplicity of infection was 0.01, the latent period was approximately 30 min, and the burst size was approximately 316 PFU/cell. The phage remained stable at 4°C-37°C and pH 6-10. Genome analysis showed that vB_SauL_202595 belongs to the class Caudoviricetes, has a genome of 44,503 bp with 33.59% GC content, and encodes 63 predicted proteins. No typical antibiotic resistance genes or major virulence factors were detected; however, integrase and repressor genes were identified, supporting its temperate nature. vB_SauL_202595 inhibited S. aureus SHZ-0127 growth, reduced mature biofilm biomass, and decreased viable bacterial counts in milk and yogurt, with reductions of 1.23 and 1.42 log10 CFU/mL under representative conditions, respectively. From a One Health perspective, these findings provide foundational evidence for reducing S. aureus contamination and related antimicrobial resistance risks along the dairy chain. Overall, vB_SauL_202595 represents a candidate phage resource for dairy-associated S. aureus biocontrol research, but its limited host range and lysogeny-related genes require further safety assessment before food-related applications.IMPORTANCEStaphylococcus aureus is a major pathogen associated with bovine mastitis and a common contaminant in dairy products, causing economic losses and public health risks through the food chain. Although phage-based biocontrol has emerged as a promising strategy for controlling S. aureus contamination in dairy products, systematic evidence regarding phage activity in actual dairy matrices remains limited. In this study, we isolated and characterized a dairy farm environment-derived temperate phage, vB_SauL_202595, and evaluated its biological characteristics, genomic features, host range, stability, biofilm removal ability, and antibacterial performance in milk and yogurt. These findings provide foundational experimental evidence for phage-based dairy biocontrol against S. aureus. However, due to its limited host range and lysogeny-related genomic features, vB_SauL_202595 should be considered a candidate phage resource for further study. Broader validation, including phage-cocktail testing, long-term storage assays, product quality assessment, and regulatory safety evaluation, is needed before practical application.

Staphylococcus aureus↗
PubMed2026

Enhanced identification of key bacterial motility genes via a cross-species genomic hybrid feature machine learning approach.

Efficient and accurate identification of functional genes is critical to biological research, yet traditional single-species approaches are often limited by low efficiency. Previously, we established a novel method for identifying key genes using cross-species protein domain features and machine learning. However, the high multiplicity of gene members associated with specific domains creates a substantial workload for subsequent experimental validation. To address this, this study proposes an enhanced approach that integrates EggNOG-based protein sequence annotation with domain analysis. Unannotated sequences are subsequently analyzed for protein domains, generating a comprehensive "direct gene annotation plus domain" hybrid feature matrix. While the hybrid matrix model yielded comparable predictive accuracy, it significantly enhanced feature resolution: the top 50 predicted features were all known motility-related genes or domains. Furthermore, among the top 100 ranked features, 58 are confirmed to be directly related to motility based on experimental evidence. Although strict genus-level control still yielded 51 confirmed features, excessive taxonomic restriction drastically reduces the number of training genomes, which may paradoxically impair identification efficiency. These results demonstrate that the new method effectively reduces the subsequent experimental workload and enables high-throughput identification of functional genes in a single analysis. With accuracy and efficiency far exceeding those of existing single-species identification methods, it provides a highly efficient solution for mining key genes underlying other complex bacterial phenotypes.

Machine Learning↗
PubMed2025

Multimodal deep learning for immunotherapy response prediction and biomarker discovery in non-small cell lung cancer.

OBJECTIVE: Immunotherapy has emerged as a promising treatment for advanced non-small cell lung cancer (NSCLC), but accurately predicting which patients will benefit from it remains a major clinical challenge. To address this, we aim to develop a novel multimodal method, DeepAFM, that integrates histopathology, genomic features, and clinical information to predict patient responses to anti-PD-(L)1 immunotherapy. MATERIALS AND METHODS: A total of 93 patients with advanced NSCLC were included in this study. Histopathological whole-slide images were processed using a self-supervised VQVAE2 for representation learning. PCA and K-means clustering were then applied for dimensionality reduction and feature grouping. Key regions of interest were visualized through permutation importance evaluation and color-coding techniques. The extracted histopathological features, along with genomic alterations and clinical variables, were integrated into the DeepAFM multimodal prediction model. RESULTS: The DeepAFM achieved a high predictive performance with an area under the curve (AUC) of 0.77 (95% confidence interval: 0.69-1.00). Attention-based heatmaps revealed that the model could identify critical pathological patterns, genomic mutations, and clinical indicators associated with patient responses to immunotherapy. DISCUSSION: The integration of multimodal data enabled the model to capture complex interactions among pathology, genomics, and clinical characteristics, enhancing the interpretability and predictive power of immunotherapy response prediction. The visualization techniques facilitated the identification of biologically meaningful features and potential biomarkers. CONCLUSION: This study demonstrates the effectiveness of the DeepAFM in predicting responses to immunotherapy in advanced NSCLC. The approach not only improves prediction accuracy but also provides valuable insights for personalized treatment strategies and biomarker discovery.

Humans↗