Mining the literature and large datasets.
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Abstracting and Indexing↗
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Results for “Genome mining”
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Unequivocal delineation of clinicogenetic subgroups and development of a new model for improved outcome prediction in neuroblastoma.
PURPOSE: Neuroblastoma is a genetically heterogeneous pediatric tumor with a remarkably variable clinical behavior ranging from widely disseminated disease to spontaneous regression. In this study, we aimed for comprehensive genetic subgroup discovery and assessment of independent prognostic markers based on genome-wide aberrations detected by comparative genomic hybridization (CGH). MATERIALS AND METHODS: Published CGH data from 231 primary untreated neuroblastomas were converted to a digitized format suitable for global data mining, subgroup discovery, and multivariate survival analyses. RESULTS: In contrast to previous reports, which included only a few genetic parameters, we present here for the first time a strategy that allows unbiased evaluation of all genetic imbalances detected by CGH. The presented approach firmly established the existence of three different clinicogenetic subgroups and indicated that chromosome 17 status and tumor stage were the only independent significant predictors for patient outcome. Important new findings were: (1) a normal chromosome 17 status as a delineator of a subgroup of presumed favorable-stage tumors with highly increased risk; (2) the recognition of a survivor signature conferring 100% 5-year survival for stage 1, 2, and 4S tumors presenting with whole chromosome 17 gain; and (3) the identification of 3p deletion as a hallmark of older age at diagnosis. CONCLUSION: We propose a new regression model for improved patient outcome prediction, incorporating tumor stage, chromosome 17, and amplification/deletion status. These findings may prove highly valuable with respect to more reliable risk assessment, evaluation of clinical results, and optimization of current treatment protocols.
Adolescent↗
Application of genome-wide gene expression profiling by high-density DNA arrays to the treatment and study of inflammatory bowel disease.
Identification of factors involved in the initiation, amplification, and perpetuation of the chronic immune response and the identification of markers for the characterization of patient subgroups remain critical objectives for ongoing research in inflammatory bowel disease (IBD). The Human Genome Project and the development of the expressed sequence tag (EST) clone collection and database have made possible a new revolution in gene expression analysis. Instead of measuring one or a few genes, parallel DNA microarrays are capable of simultaneously measuring expression of thousands of genes, providing a glimpse into the logic and functional grouping of gene programs encoded by our genome. Applied to clinical specimens from affected and normal individuals, this methodology has the potential to provide a new level of information about disease pathogenesis not previously possible. Two dominant platforms for the construction of high-density microarrays have emerged: cDNA arrays and GeneChips. The first involves robotic spotting of DNA molecules, often derived from EST clone collections, onto a suitable solid phase matrix such as a glass slide. The second involves direct in situ synthesis of sets of gene-specific oligonucleotides on a silicon wafer by an eloquent derivative of the photolithography process. Both cDNA and oligonucleotide arrays are interrogated by hybridization with a fluorescent-labeled cDNA or cRNA representation of the original tissue mRNA. This enables measurement of the expression levels for thousands of mucosal genes in a single experiment. These technologies have recently become less expensive and more widely accessible to all researchers. This review details the principles and methods behind DNA array technology, data analysis and mining, and potential application to research and treatment of IBD.
Gene Expression Profiling↗
Microarray data mining with visual programming.
UNLABELLED: Visual programming offers an intuitive means of combining known analysis and visualization methods into powerful applications. The system presented here enables users who are not programmers to manage microarray and genomic data flow and to customize their analyses by combining common data analysis tools to fit their needs. AVAILABILITY: http://www.ailab.si/supp/bi-visprog SUPPLEMENTARY INFORMATION: http://www.ailab.si/supp/bi-visprog.
Chromosome Mapping↗
Biotechnology match making: screening orphan ligands and receptors.
To date there has been a considerable amount of interest and success in the pharmaceutical industry in the discovery of drug targets and diagnostics via genomic technologies, namely DNA sequencing, mutation/polymorphism detection and expression monitoring of mRNA. As the ultimate targets for the majority of these methods are actually proteins, more and more emphasis has been placed upon protein-based methods in an effort to define the function of proteins discovered by genomic technologies. One of the most challenging areas of drug target discovery facing researchers today is the search for novel receptor-ligand pairs. Database mining techniques in conjunction with other computational methods are able to identify many novel sequences of putative receptors, but the ability to similarly identify the receptor's natural ligand is not possible by these methods. The past few years have seen an increase in methodology and instrumentation focused on the ability to discover and characterize protein-protein interactions, as well as receptor-ligand pairs. Significant advances have been made in the areas of instrumentation (biosensors and fluorescent plate readers) as well as methodologies relating to phage/ribosome display and library construction.
Animals↗
GEPIS--quantitative gene expression profiling in normal and cancer tissues.
MOTIVATION: Expression profiling in diverse tissues is fundamental to understanding gene function as well as therapeutic target identification. The vast collection of expressed sequence tags (ESTs) and the associated tissue source information provides an attractive opportunity for studying gene expression. RESULTS: To facilitate EST-based expression analysis, we developed GEPIS (gene expression profiling in silico), a tool that integrates EST and tissue source information to compute gene expression patterns in a large panel of normal and tumor samples. We found EST-based expression patterns to be consistent with published papers as well as our own experimental results. We also built a GEPIS Regional Atlas that depicts expression characteristics of all genes in a selected genomic region. This program can be adapted for large-scale screening for genes with desirable expression patterns, as illustrated by our large-scale mining for tissue- and tumor-specific genes. AVAILABILITY: The email server version of the GEPIS application is freely available at http://share.gene.com/share/gepis. An interactive version of GEPIS will soon be freely available at http://www.cgl.ucsf.edu/Research/genentech/gepis/. The source code, modules, data and gene lists can be downloaded at http://share.gene.com/share/gepis.
Algorithms↗
Integrating explainable AI with multiomics systems biology and EHR data mining for personalized drug repurposing in Alzheimer's disease.
Alzheimer's disease (AD) is characterized by region- and patient-specific molecular heterogeneity, which hinders therapeutic design. In this study, we introduce PRISM-ML (PRecision-medicine using Interpretable Systems and Multiomics with Machine Learning), an open-source integrated analysis pipeline that combines interpretable machine learning with systems biology and electronic health record (EHR) data mining to elucidate the molecular diversity of AD and predict promising drug repurposing opportunities. First, we integrated and harmonized transcriptomic (bulk RNA-seq) and genomic (genome-wide association study) data from 2105 brain samples, each with matched data from the same individual (1363 AD patients, 742 controls; nine tissues), sourced from three independent studies. Random forest classifiers with SHapley Additive exPlanations (SHAP) identified patient-specific biomarkers; unsupervised clustering resolved 36 molecularly distinct "subtissues" (clusters of samples); and gene-gene co-expression networks prioritized 262 high-centrality bottleneck genes as putative regulators of dysregulated pathways. Next, knowledge graph-based drug repurposing predicted six FDA-approved drugs that simultaneously target multiple bottleneck genes and multiple AD-relevant pathways. Notably, in a large U.S. de-identified insurance-claims database (n = 364733), exposure to promethazine, one of the candidate drugs, was associated with a 57-62 % lower incidence of AD versus an active antihistamine comparator (adjusted hazard ratio 0.38; inverse-probability weighted 0.43; both p < 0.001), providing real-world support for its repurposing potential. In summary, PRISM-ML, as an explainable multi-omics analysis pipeline, is readily transferable to other complex diseases, advancing precision medicine.
Computational Biology↗
Mining yeast transcriptional regulatory modules from factor DNA-binding sites and gene expression data.
UNLABELLED: In eukaryotes, gene expression is controlled by various transcription factors that bind to the promoter regions. Transcription factors may act positively, negatively or not at all. Different combinations of them may also activate or repress gene expression, and form regulatory networks of transcription. Uncovering such regulatory networks is a central challenge in genomic biology. In this study, we first defined a new kind of motifs in regulatory networks, transcriptional regulatory modules (TRMs), with the form factorset --> geneset, which emphasizes the combinatorial gene control of the group of factors factorset on the group of genes geneset. Second, we developed an efficient method based on a closed itemset mining technique for finding the two most informative kinds of TRMs, closed inf-TRMs and closed sup-TRMs, from factor DNA-binding sites and gene expression profiles data. The set of all closed inf-TRMs and closed sup-TRMs is often orders of magnitude smaller than the set of all TRMs but does not loss any information. When being applied to yeast data, our method produced results that are more compact, concise and comprehensive than those from previous studies to identify and interpret the transcriptional role of regulator combinations on sets of genes. AVAILABILITY: Supplementary files: http://www.jaist.ac.jp/~h-pham/regulation/.
Algorithms↗
Analysis of environmental microbial communities by reverse sample genome probing.
Development of fast and accurate methods for monitoring environmental microbial diversity is one of the great challenges in microbiology today. Oligonucleotide probes based on 16S rRNA sequences are widely used to identify bacteria in the environment. However, the successful development of a chip of immobilized 16S rRNA probes for identification of large numbers of species in a single hybridization step has not yet been reported. In reverse sample genome probing (RSGP), labelled total community DNA is hybridized to arrays in which genomes of cultured microorganisms are spotted on a solid support in denatured form. This method has provided useful information on changes in composition of the cultured component of microbial communities in oil fields, the soil rhizhosphere, hydrocarbon-contaminated soils and acid mine drainage sites. Applications and limitations of the method, as well as the prospects of extending RSGP to cover also the as yet uncultured component of microbial communities, are evaluated.
Bacteria↗
New approaches towards integrated proteomic databases and depositories.
Since the publication of the human genome, two key points have emerged. First, it is still not certain which regions of the genome code for proteins. Second, the number of discrete protein-coding genes is far fewer than the number of different proteins. Proteomics has the potential to address some of these postgenomic issues if the obstacles that we face can be overcome in our efforts to combine proteomic and genomic data. There are many challenges associated with high-throughput and high-output proteomic technologies. Consequently, for proteomics to continue at its current growth rate, new approaches must be developed to ease data management and data mining. Initiatives have been launched to develop standard data formats for exchanging mass spectrometry proteomic data, including the Proteomics Standards Initiative formed by the Human Proteome Organization. Databases such as SwissProt and Uniprot are publicly available repositories for protein sequences annotated for function, subcellular location and known potential post-translational modifications. The availability of bioinformatics solutions is crucial for proteomics technologies to fulfil their promise of adding further definition to the functional output of the human genome. The aim of the Oxford Genome Anatomy Project is to provide a framework for integrating molecular, cellular, phenotypic and clinical information with experimental genetic and proteomics data. This perspective also discusses models to make the Oxford Genome Anatomy Project accessible and beneficial for academic and commercial research and development.
Databases, Protein↗
NCBI GEO: mining millions of expression profiles--database and tools.
The Gene Expression Omnibus (GEO) at the National Center for Biotechnology Information (NCBI) is the largest fully public repository for high-throughput molecular abundance data, primarily gene expression data. The database has a flexible and open design that allows the submission, storage and retrieval of many data types. These data include microarray-based experiments measuring the abundance of mRNA, genomic DNA and protein molecules, as well as non-array-based technologies such as serial analysis of gene expression (SAGE) and mass spectrometry proteomic technology. GEO currently holds over 30,000 submissions representing approximately half a billion individual molecular abundance measurements, for over 100 organisms. Here, we describe recent database developments that facilitate effective mining and visualization of these data. Features are provided to examine data from both experiment- and gene-centric perspectives using user-friendly Web-based interfaces accessible to those without computational or microarray-related analytical expertise. The GEO database is publicly accessible through the World Wide Web at http://www.ncbi.nlm.nih.gov/geo.
Animals↗
High-throughput metabolic state analysis: the missing link in integrated functional genomics of yeasts.
The lack of comparable metabolic state assays severely limits understanding the metabolic changes caused by genetic or environmental perturbations. The present study reports the application of a novel derivatization method for metabolome analysis of yeast, coupled to data-mining software that achieve comparable throughput, effort and cost compared with DNA arrays. Our sample workup method enables simultaneous metabolite measurements throughout central carbon metabolism and amino acid biosynthesis, using a standard GC-MS platform that was optimized for this purpose. As an implementation proof-of-concept, we assayed metabolite levels in two yeast strains and two different environmental conditions in the context of metabolic pathway reconstruction. We demonstrate that these differential metabolite level data distinguish among sample types, such as typical metabolic fingerprinting or footprinting. More importantly, we demonstrate that this differential metabolite level data provides insight into specific metabolic pathways and lays the groundwork for integrated transcription-metabolism studies of yeasts.
Aerobiosis↗
Genomic prospecting and biochemical characterization of a novel thermostable 3-quinuclidinone reductase from hot spring metagenomes for efficient biocatalysis.
This study presents the discovery and characterization of a novel thermophilic 3-quinuclidinone reductase (ScQR) identified through metagenomic mining of hot spring environments. ScQR, a member of the short-chain dehydrogenase/reductase (SDR) superfamily, was heterologously expressed in Escherichia coli, and its catalytic properties were systematically characterized. The enzyme demonstrates exceptional thermal stability, retaining 86% of its activity after 48 hours at 70°C. Furthermore, K+ and Mg²+ ions significantly enhanced ScQR's activity at specific concentrations. Structural analysis revealed that ScQR adopts a typical SDR fold with a conserved catalytic triad (S141-Y155-K159), and it is NAD(H) dependent. Enzyme assays indicated that ScQR is highly stereoselective for (R)-3-quinuclidinol, with no activity against its enantiomer, (S)-3-quinuclidinol. The enzyme exhibits optimal activity at pH 9 and 85°C, making it a promising candidate for industrial applications requiring high thermal stability. Molecular dynamics simulations further revealed that ScQR preserves global structural integrity up to 360 K, whereas higher temperatures induce destabilization, predominantly in the C-terminal region and residues 95-100. In addition, structure-guided computational design enabled by LigandMPNN and UniKP yielded three ScQR variants with improved substrate affinity and catalytic efficiency while maintaining the overall fold and function. This work underscores the power of metagenomics with structure-driven protein design in discovering novel enzymes with unique catalytic properties from extreme environments and establishes ScQR as a promising biocatalyst for biotechnological and pharmaceutical applications.IMPORTANCEThis study reports the discovery of ScQR, a novel thermophilic 3-quinuclidinone reductase identified via metagenomic mining. ScQR represents one of the most heat-resistant members of the SDR superfamily discovered to date, maintaining 86% activity after 48 hours at 70°C. These findings establish ScQR as a robust biocatalyst for high-temperature pharmaceutical applications and demonstrate a scalable workflow for optimizing enzymes from extreme environments, offering significant value to the fields of biocatalysis and protein engineering.
computational design↗
PEDANT genome database: 10 years online.
The PEDANT genome database provides exhaustive annotation of 468 genomes by a broad set of bioinformatics algorithms. We describe recent developments of the PEDANT Web server. The all-new Graphical User Interface (GUI) implemented in Javatrade mark allows for more efficient navigation of the genome data, extended search capabilities, user customization and export facilities. The DNA and Protein viewers have been made highly dynamic and customizable. We also provide Web Services to access the entire body of PEDANT data programmatically. Finally, we report on the application of association rule mining for automatic detection of potential annotation errors. PEDANT is freely accessible to academic users at http://pedant.gsf.de.
Computer Graphics↗
Cyclophilin nomenclature problems, or, 'a visit from the sequence police'.
Why is agreement on one particular name for each gene important? As one genome after another becomes sequenced, it is imperative to consider the complexity of genes, genetic architecture, gene expression, gene-gene and gene-product interactions and evolutionary relatedness across species. To agree on a particular gene name not only makes one's own research easier, it aids automated text-mining algorithms and search engines, which are increasingly employed to find relationships in the millions of abstracts in the medical research literature and sequence databases. A common nomenclature system will also be helpful to the present generation, as well as future generations, of graduate students and postdoctoral fellows who are about to enter genomics research. In this paper, the authors present some problems that arose when two separate research communities decided to choose the same root, CYP, for naming their gene families. They then offer a logical solution, by renaming the cyclophilin genes with a common root, such as cyn- in Caenorhabditis and CYN- in mammals (Cyn in mouse), and using evolutionary divergence to cluster genes of the highest level of relatedness.
Amino Acid Sequence↗
Functional bioinformatics for Arabidopsis thaliana.
MOTIVATION: The genome of Arabidopsis thaliana, which has the best understood plant genome, still has approximately one-third of its genes with no functional annotation at all from either MIPS or TAIR. We have applied our Data Mining Prediction (DMP) method to the problem of predicting the functional classes of these protein sequences. This method is based on using a hybrid machine-learning/data-mining method to identify patterns in the bioinformatic data about sequences that are predictive of function. We use data about sequence, predicted secondary structure, predicted structural domain, InterPro patterns, sequence similarity profile and expressions data. RESULTS: We predicted the functional class of a high percentage of the Arabidopsis genes with currently unknown function. These predictions are interpretable and have good test accuracies. We describe in detail seven of the rules produced.
Algorithms↗
Mechanism of Shaofu Zhuyu decoction in improving diabetic mellitus erectile dysfunction inhibition of ferroptosis based on network pharmacology and experimental validation.
OBJECTIVE: To explore the medication patterns and mechanisms of action of Shaofu Zhuyu decoction (, SFZYD) in inhibiting ferroptosis through the nuclear factor erythroid 2-related factor 2 (Nrf2)/heme oxygenase 1 (HO-1)/glutathione peroxidase 4 (GPX4) pathway to improve diabetes mellitus-induced erectile dysfunction (DMED). METHODS: Firstly, data mining was employed to identify the medication patterns of Traditional Chinese Medicine (TCM) in treating DMED. Secondly, network pharmacology combined with a ferroptosis database was used to predict the targets. Subsequently, cell counting kit-8, 4',6-diamidino-2-phenylindole staining, reverse transcription-polymerase chain reaction (RT-PCR), and reagent kits were utilized to assess the repair effects of SFZYD on corpus cavernosum endothelial cells (CCECs) induced by high glucose (HG). Metabolic indicators, hematoxylin-eosin staining, and Masson staining were performed to observe the restorative effects of SFZYD on erectile function and penile tissue in diabetic rats. Finally, using Nrf2 inhibitors, the expression of related proteins and mRNAs was detected through Western blotting and RT-PCR. Reactive oxygen species levels and mitochondrial membrane potential were detected by flow cytometry. RESULTS: Data mining revealed that the prescription rules for blood stasis-type DMED coincide with the treatment principles of SFZYD. Network pharmacology identified 48 ferroptosis-related targets, primarily heme oxygenase 1 (HMOX1) and GPX4. Kyoto Encyclopedia of Genes and Genomes enrichment analysis associated these targets with the ferroptosis pathway. SFZYD repaired HG-induced CCECs damage and restored HMOX1 and GPX4 mRNA levels. in vivo, SFZYD effectively alleviated erectile dysfunction and repaired blood sinuses and fibrosis in diabetic rats. Following Nrf2 inhibition, the expression of Nrf2, HMOX1, and GPX4 decreased, while SFZYD intervention reversed these effects, improving ferroptosis and oxidative stress indicators. CONCLUSION: This study explored the potential mechanisms and efficacy of the TCM prescription SFZYD in treating DMED through data mining, network pharmacology analysis, cellular experiments, and animal experiments. It verified its effectiveness in repairing HG-induced CCECs damage, improving the pathological state of penile tissue in diabetic rats, and restoring erectile function by regulating the Nrf2/HO-1/GPX4 signaling pathway. This provides new insights and scientific evidence for treating DMED with TCM.
Male↗
A novel low-penetrance locus for familial glioma at 15q23-q26.3.
Epidemiological studies and case reports suggest that familial clustering of gliomas may occur in families that do not fit any known tumor syndromes. In the present study, 15 familial glioma pedigrees from a limited geographical area were hypothesized to carry the same low-penetrance susceptibility allele. We used a two-stage strategy for disease gene mapping. A genome scan in four glioma families revealed four interesting loci at chromosome arms 1q, 6q, 8p, and 15q. Additional markers in these regions provided evidence of significant linkage to 15q23-q26.3 with a maximum nonparametric linkage score of 3.35 with marker D15S130. Investigation of all 15 glioma families by association analysis (haplotype pattern mining) and through use of the transmission/disequilibrium test gave further evidence of significant association/transmission distortion at the same 15q locus (P = 0.02 and P = 0.03, respectively). No evidence of involvement of known tumor syndromes was obtained from the data provided by the linkage analysis or hospital records. Thus, the first genome-wide linkage analysis of familial glioma suggests a novel susceptibility locus at 15q23-q26.3.
Brain Neoplasms↗