Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Genome mining”

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 793 records · Page 44Linked to original sources

An online database for brain disease research.

BACKGROUND: The Stanley Medical Research Institute online genomics database (SMRIDB) is a comprehensive web-based system for understanding the genetic effects of human brain disease (i.e. bipolar, schizophrenia, and depression). This database contains fully annotated clinical metadata and gene expression patterns generated within 12 controlled studies across 6 different microarray platforms. DESCRIPTION: A thorough collection of gene expression summaries are provided, inclusive of patient demographics, disease subclasses, regulated biological pathways, and functional classifications. CONCLUSION: The combination of database content, structure, and query speed offers researchers an efficient tool for data mining of brain disease complete with information such as: cross-platform comparisons, biomarkers elucidation for target discovery, and lifestyle/demographic associations to brain diseases.

Bipolar Disorder↗

Alterations in gene expression in the testis of angiotensin-(1-7)-receptor Mas-deficient mice.

The Mas protooncogene encodes a G-protein coupled receptor with seven trans-membrane domains and was recently described as a receptor for angiotensin-(1-7). Mas is highly expressed in testis and brain of rodents, whereas its expression in testis is localized to Leydig cells, being upregulated during puberty. Although the expression of this receptor in testis is strongly ontogenetically controlled and cell type-specific, Mas-deficient mice are fertile. To identify genes of which the expression is affected by the deletion of Mas, we performed differential gene expression profiling. Testis-RNA of Mas-knockout mice on a C57Bl/6 background vs. testis-RNA of C57Bl/6 wild type mice were analyzed on an Affymetrix Murine Genome U74v2 GeneChip probing >12,000 transcripts. The microarray identified 67 transcripts, which were downregulated in Mas-deficient mice and 65 genes, which were upregulated. The analysis of 132 differentially expressed genes by a Gene Ontology Mining Tool revealed genes with activities in mitochondria being overrepresented in the set of significantly affected genes. For some selected genes, the results were verified by a specific real time PCR. Three of these genes, coding for proteins involved in mitochondrial function and steroidogenesis, become henceforth targets for our continuing search for the physiological function of Mas in testis.

3-Hydroxysteroid Dehydrogenases↗

Oxidative stress is involved in the development of experimental abdominal aortic aneurysm: a study of the transcription profile with complementary DNA microarray.

BACKGROUND: The role of oxidative stress in the formation of aneurysms is not fully understood. We used the complementary DNA (cDNA) microarray technique to determine the transcription profile in the development of elastase-induced abdominal aortic aneurysm in rat models, with an emphasis on the oxidative stress-related genes. MATERIALS AND METHODS: In the experimental group, rat abdominal aortas were perfused with elastase to induce AAA. In the control group, a sham operation was performed with perfusion of the aortas with saline solution. Four or five animals were used for each time point for each of the elastase-treated or saline-treated groups. At day 2, day 7, and day 10 after surgery, the external aortic diameter was measured and AAA formation was estimated. Total RNA was isolated from aortas and subjected to cDNA microarray analysis with the use of the rat genome U34A high-density oligonucleotide DNA chip (Affymetrix, Santa Clara, Calif), which contains a total number of 8799 genes of which 2017 are expressed sequence tag (EST) genes. The data were analyzed with the GENECHIP Data Mining Tool software (Affymetrix). For genes of interest, reverse-transcription polymerase chain reaction was performed to confirm their expression level. RESULTS: Comparison ranking analysis revealed that during AAA development, the expression of 212 genes, including 46 of EST genes, increased by more than two-fold and 229 genes, including 95 of EST genes, decreased by more than two-fold in at least one of the three time points. The regulated genes included those encoding heme oxygenase, inducible nitric oxide synthase, some extracellular matrix proteins, members of the matrix metalloproteinase family, and those associated with prooxidant/antioxidant and inflammatory responses. Reverse-transcription polymerase chain reaction analysis confirmed the upregulation of genes involved in oxidative stress, such as heme oxygenase, inducible nitric oxide synthase, 12-lipoxygenase, and heart cytochrome c oxydase subunit VIa, and the downregulation of antioxidant genes, such as superoxide dismutase, reduced nicotinamide adenine dinucleotide-cytochrome b-5 reductase, and glutathion S-transferase. CONCLUSION: The cDNA microarray technique was useful for investigation of the transcription profiles during the development of AAA. Our results indicate that oxidative stress may play a pivotal role in the pathologic progression of AAA.

Animals↗

MedBlast: searching articles related to a biological sequence.

UNLABELLED: In the genomic era, researchers often want to know more information about a biological sequence by retrieving its related articles. However, there is no available tool yet to achieve conveniently this goal. Here we developed a new literature-mining tool MedBlast, which uses natural language processing techniques, to retrieve the related articles of a given sequence. An online server of this program is also provided. AVAILABILITY: Both online server and the program are available freely at http://medblast.sibsnet.org

Algorithms↗

Discovering neuropeptides in Caenorhabditis elegans by two dimensional liquid chromatography and mass spectrometry.

Completion of the Caenorhabditis elegans genome sequencing project in 1998 has provided more insight into the complexity of nematode neuropeptide signaling. Several C. elegans neuropeptide precursor genes, coding for approximately 250 peptides, have been predicted from the genomic database. One can, however, not deduce whether all these peptides are actually expressed, nor is it possible to predict all post-translational modifications. Using two dimensional nanoscale liquid chromatography combined with tandem mass spectrometry and database mining, we analyzed a mixed stage C. elegans extract. This peptidomic setup yielded 21 peptides derived from formerly predicted neuropeptide-like protein (NLP) precursors and 28 predicted FMRFamide-related peptides. In addition, we were able to sequence 11 entirely novel peptides derived from nine peptide precursors that were not predicted or identified in any way previously. Some of the identified peptides display profound sequence similarities with neuropeptides from other invertebrates, indicating that these peptides have a long evolutionary history.

Amides↗

Transposition of MINE, a composite retrotransposon, in the avirulence gene ACE1 of the rice blast fungus Magnaporthe grisea.

The ACE1 avirulence gene allele from the rice blast fungus Magnaporthe grisea was characterized in virulent isolate 2/0/3, revealing the insertion of a 1.9 kb MINE retrotransposon in the last ACE1 exon. MINE is a novel chimeric element composed of a transcribed non-coding sequence of 1.1 kb (WEIRD) fused to a 5'-truncated MGL retrotransposon. MINEs were found in high copy number in M. grisea isolates from rice (68 copies) and as a single copy in isolate CD156 from Eleusine. MINEs vary in size (1.3-6.7 kb) with conserved 5' WEIRD sequences and variable 3' MGL sequences. MGLs fused to WEIRDs correspond to different 5'-truncated MGLs with conserved 3' ends. The organization and diversity of MINEs suggest that these retrotransposons result from independent fusions between WEIRD and 5'-truncated MGLs. Such chimera could be formed during MGL reverse transcription as proposed for human U6-LINE1 chimeric retrotransposons and integrated into M. grisea genome using MGL machinery.

DNA, Fungal↗

ZooDDD: a cross-species database for digital differential display analysis.

UNLABELLED: In this article, we combined EST information from the UniGene database and orthologous relationships from the Ensembl database to construct a ZooDDD database. The primary function of ZooDDD is to mine evolutionary conserved, highly expressed, tissue-specific orthologues in model animals. The candidate genes of interest derived from the ZooDDD database will provide biologists with a good step for comparing the expression, functions and evolution of animal genomes. AVAILABILITY: http://bio301.iis.sinica.edu.tw/~ZooDDDNew/main.php.

Base Sequence↗

TXTGate: profiling gene groups with text-based information.

We implemented a framework called TXTGate that combines literature indices of selected public biological resources in a flexible text-mining system designed towards the analysis of groups of genes. By means of tailored vocabularies, term- as well as gene-centric views are offered on selected textual fields and MEDLINE abstracts used in LocusLink and the Saccharomyces Genome Database. Subclustering and links to external resources allow for in-depth analysis of the resulting term profiles.

Animals↗

Information extraction in the life sciences: perspectives for medicinal chemistry, pharmacology and toxicology.

Information extraction approaches have been successfully applied to mine the scientific literature in biology and medicine. So far, the main focus of research and development in this domain was on the recognition and extraction of gene and protein names in the context of molecular biology and genome research and on disease names and other medical terms in the context of clinical research. Similar to biology and medical sciences, medicinal chemistry, pharmacology and toxicology are descriptive sciences. However, information extraction approaches in these disciplines encounter a number of problems that are specific to the fact that these scientific areas are essentially centred at chemical compounds and their structures. In this review, we will give a short overview on general information extraction strategies in the life sciences and we will introduce new approaches to apply information extraction to the domain of pharmacology, medicinal chemistry and toxicology. Finally, we will emphasize on how information extraction approaches will support public and commercial research in medicinal chemistry, pharmacology and toxicology by linking information on chemical structures to biological information.

Biological Science Disciplines↗

Metabolite profiling for plant functional genomics.

Multiparallel analyses of mRNA and proteins are central to today's functional genomics initiatives. We describe here the use of metabolite profiling as a new tool for a comparative display of gene function. It has the potential not only to provide deeper insight into complex regulatory processes but also to determine phenotype directly. Using gas chromatography/mass spectrometry (GC/MS), we automatically quantified 326 distinct compounds from Arabidopsis thaliana leaf extracts. It was possible to assign a chemical structure to approximately half of these compounds. Comparison of four Arabidopsis genotypes (two homozygous ecotypes and a mutant of each ecotype) showed that each genotype possesses a distinct metabolic profile. Data mining tools such as principal component analysis enabled the assignment of "metabolic phenotypes" using these large data sets. The metabolic phenotypes of the two ecotypes were more divergent than were the metabolic phenotypes of the single-loci mutant and their parental ecotypes. These results demonstrate the use of metabolite profiling as a tool to significantly extend and enhance the power of existing functional genomics approaches.

Arabidopsis↗

HOX Pro DB: the functional genomics of hox ensembles.

The HOX Pro database contains information about the organization, function and evolution of gene ensembles, notably the homeobox-containing genes. It is now clear that a subset of genes containing the homeobox motif play key roles in the orchestration of genes which control embryonic patterning, morphogenesis, cell differentiation and malignant transformation. The HOX Pro contains a broad spectrum of information including images, diagrams and animations. Currently this amounts to approximately 700 HTML pages together with 400 images which contain information on 200 groups of genes and 90 promoters, in turn linked to maps of 13 HOX clusters and nine genetic networks. There are about 700 sequences of individual hox-genes of animals classified in approximately 200 homologous or paralogous groups. Graphical representation of HOX clusters and Hox-based networks is accomplished by means of flow and 3D diagrams, JavaScript animations and Java applets. The HOX Pro now includes sections presenting data mining and data simulation issues. The DB is located at http://www.iephb.nw.ru/hoxpro.

Animals↗

On the way to understand biological complexity in plants: S-nutrition as a case study for systems biology.

The establishment of technologies for high-throughput DNA sequencing (genomics), gene expression (transcriptomics), metabolite and ion analysis (metabolomics/ionomics) and protein analysis (proteomics) carries with it the challenge of processing and interpreting the accumulating data sets. Publicly accessible databases and newly development and adapted bioinformatic tools are employed to mine this data in order to filter relevant correlations and create models describing physiological states. These data allow the reconstruction of networks of interactions of the various cellular components as enzyme activities and complexes, gene expression, metabolite pools or pathway flux modes. Especially when merging information from transcriptomics, metabolomics and proteomics into consistent models, it will be possible to describe and predict the behaviour of biological systems, for example with respect to endogenous or environmental changes. However, to capture the interactions of network elements requires measurements under a variety of conditions to generate or refine existing models. The ultimate goal of systems biology is to understand the molecular principles governing plant responses and consistently explain plant physiology.

Arabidopsis↗

Mining genetic epidemiology data with Bayesian networks application to APOE gene variation and plasma lipid levels.

There is a critical need for data-mining methods that can identify SNPs that predict among individual variation in a phenotype of interest and reverse-engineer the biological network of relationships between SNPs, phenotypes, and other factors. This problem is both challenging and important in light of the large number of SNPs in many genes of interest and across the human genome. A potentially fruitful form of exploratory data analysis is the Bayesian or Belief network. A Bayesian or Belief network provides an analytic approach for identifying robust predictors of among-individual variation in a disease endpoints or risk factor levels. We have applied Belief networks to SNP variation in the human APOE gene and plasma apolipoprotein E levels from two samples: 702 African-Americans from Jackson, MS, and 854 non-Hispanic whites from Rochester, MN. Twenty variable sites in the APOE gene were genotyped in both samples. In Jackson, MS, SNPs 4036 and 4075 were identified to influence plasma apoE levels. In Rochester, MN, SNPs 3937 and 4075 were identified to influence plasma apoE levels. All three SNPs had been previously implicated in affecting measures of lipid and lipoprotein metabolism. Like all data-mining methods, Belief networks are meant to complement traditional hypothesis-driven methods of data analysis. These results document the utility of a Belief network approach for mining large scale genotype-phenotype association data.

Apolipoproteins E↗

[In silico data mining of the human programmed cell death 5 (PDCD5) sequences].

OBJECTIVE: To lay foundation for the functional studies of programmed cell death 5 (PDCD5) and develop new technical pathway for bioinformatics analysis of human functional genes. METHODS: Using PDCD5 as the target molecule, intensive bioinformatics analysis of the nucleic acid and protein sequences were conducted. Data mining and comprehensive analysis by sequence against database similarity searching, ortholog structure comparison, expression profile analysis and gene "neighbor" listing were performed. RESULTS: Two human putative pseudogenes on chromosomes 12 and 5, and one mouse putative pseudogene on chromosome 1 were identified. The methanobacterium thermoautotrophicum ortholog was classified as the same fold as ubiquitin and ribosomal protein S13. The C. elegans ortholog, ubiquitin and IAP (inhibitor of apoptosis proteins) belonged to the same expression profile cluster. This cluster was related to biosynthesis and protein synthesis. PDCD5 orthologs in various genomes were adjacent to various ribosomal proteins on the chromosome. CONCLUSION: The human genome contains at least two processed pseudogenes of PDCD5. Besides the relationship with cell apoptosis, PDCD5 is predicted to have functional relationship with ubiquitin and participate in the translation regulation.

Amino Acid Sequence↗

Herbicide Resistance Genes in Crops: Mechanisms, Progress, and Future Perspectives.

While previous reviews have largely focused on individual crops or single target-site mechanisms, the full-chain comparative landscape across major cereal crops remains unexplored. Here, we fill this critical gap by providing the first systematic, cross-crop comparative review that spans herbicide targets, resistance mechanisms, and breeding applications across four major cereals-rice, maize, wheat, and sorghum. Weed infestation is a serious constraint on crop production. Chemical weed control faces challenges such as herbicide resistance evolution and ecological risks. Developing herbicide-resistant varieties is a fundamental approach to achieve green and sustainable weed management. This review systematically summarizes research progress on herbicide resistance genes from three aspects: herbicide classification, resistance mechanisms, and crop breeding applications. It highlights key differences among four major cereal crops (rice, maize, wheat, and sorghum) in resistance-gene discovery and translational progress. Rice has the richest target-site resistance-gene resources. Maize leads in commercialization of transgenic herbicide resistance. Wheat focuses on endogenous precise editing due to genome complexity and regulatory constraints. Sorghum relies on specific mutations to serve cereal-legume intercropping systems. Based on this comparison, this review identifies the core trends in resistance breeding: from single-gene to multi-gene stacking, and from exogenous gene introduction to endogenous gene editing. It also points out common bottlenecks, including insufficient systematic mining of resistance-gene resources, lagging elucidation of non-target-site resistance regulatory networks, and strong genotype dependence in genetic transformation. Future efforts should focus on exploring broad-spectrum resistance genes, optimizing precise editing technologies, and developing sustainable resistance management strategies. This review provides a theoretical framework and practical references for molecular breeding of herbicide-resistant crops.

crop breeding↗

Modtector: ultra-fast modification signal mining on mapped sequencing reads.

SUMMARY: Existing tools for RNA epitranscriptomic modification and structural signal analysis are often fragmented, inefficiency, and limited to single signal types. We developed Modtector, an unified tool for extracting mutation and reverse-transcription stop signals from aligned sequencing reads. By using a "count-then-correct" strategy, Modtector reduces computational complexity and enables efficient dual-signal analysis. It achieves multi-fold speedups on large-genome and high-coverage datasets, including completing HEK293 22G data analysis in 5 minutes, and show strong scalability on single-cell datasets with speedups exceeding 50-fold. AVAILABILITY: The source code is available at GitHub (https://github.com/TongZhou2017/modtector) and Crates.io (https://crates.io/crates/modtector). The archived source-code snapshot used in this study is available at Zenodo (DOI: 10.5281/zenodo.20967747), corresponding to GitHub commit 7c60e9d. Workflow examples, datasets, and analysis scripts are available at Zenodo (DOI: 10.5281/zenodo.17316476 and 10.5281/zenodo.18523297).

Humans↗

In silico approaches to mechanistic and predictive toxicology: an introduction to bioinformatics for toxicologists.

Bioinformatics, or in silico biology, is a rapidly growing field that encompasses the theory and application of computational approaches to model, predict, and explain biological function at the molecular level. This information rich field requires new skills and new understanding of genome-scale studies in order to take advantage of the rapidly increasing amount of sequence, expression, and structure information in public and private databases. Toxicologists are poised to take advantage of the large public databases in an effort to decipher the molecular basis of toxicity. With the advent of high-throughput sequencing and computational methodologies, expressed sequences can be rapidly detected and quantitated in target tissues by database searching. Novel genes can also be isolated in silico, while their function can be predicted and characterized by virtue of sequence homology to other known proteins. Genomic DNA sequence data can be exploited to predict target genes and their modes of regulation, as well as identify susceptible genotypes based on single nucleotide polymorphism data. In addition, highly parallel gene expression profiling technologies will allow toxicologists to mine large databases of gene expression data to discover molecular biomarkers and other diagnostic and prognostic genes or expression profiles. This review serves to introduce to toxicologists the concepts of in silico biology most relevant to mechanistic and predictive toxicology, while highlighting the applicability of in silico methods using select examples.

Cluster Analysis↗

Diabetes: a candidate disease for efficient DNA methylation profiling.

Methylation has been implied in a number of biological processes and has been shown to vary under environmental influences as well as in age. Most results on the correlation of methylation patterns with phenotypic characteristics of cells have been obtained by analysis of very few or even single genomic fragments for methylation. However, variation of methylation may more often than not be a phenomenon that affects multiple genomic loci. The role of methylation has been most conclusively demonstrated in complex disease, with cancer being the most prominent example. The influence of aging and environmental influences such as diet seems to be on global methylation patterns, in turn exerting local effects on groups of genes. Hence, methylation seems literally to be orchestrating complex genetic systems. It could, therefore, be considered an archetypal "genomics" parameter. In consequence, technologies used to analyze methylation patterns should be as industrialized as possible to capture the local events across the entire genome. Epigenomics' research team is the first to have achieved the industrialized production of genome sequence-specific wide methylation data. Our microarray and mass-spectrometry-based detection platform currently allow the analysis of up to 50,000 methylation positions per day, for the first time making methylation data amenable to sophisticated information mining. The information content of methylation position has never been analyzed using the high-dimensional statistical methods that are recognized to be required for the analysis of, for example, mRNA expression profiles or proteomic data. As methylation patterns are nothing but a quasi-digital form of expression data, their information content must be evaluated using similar but adapted algorithms. This article presents a broad set of studies that demonstrate that methylation yields information that is comparable or even superior to the current state of the art, namely, mRNA profiling. We argue that the resulting robust, digital and-because of the highly stable nature of DNA as the analyte-more reproducible information could become the "gold standard" for clinical diagnostics and disease gene identification in age-related, environmentally influenced and epigenetic disease in general, substituting for mRNA expression.

DNA Methylation↗