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Results for “Genome mining”

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At least 757 records · Page 42Linked to original sources
PubMed2006

Mining expressed sequence tags identifies cancer markers of clinical interest.

BACKGROUND: Gene expression data are a rich source of information about the transcriptional dis-regulation of genes in cancer. Genes that display differential regulation in cancer are a subtype of cancer biomarkers. RESULTS: We present an approach to mine expressed sequence tags to discover cancer biomarkers. A false discovery rate analysis suggests that the approach generates less than 22% false discoveries when applied to combined human and mouse whole genome screens. With this approach, we identify the 200 genes most consistently differentially expressed in cancer (called HM200) and proceed to characterize these genes. When used for prediction in a variety of cancer classification tasks (in 24 independent cancer microarray datasets, 59 classifications total), we show that HM200 and the shorter gene list HM100 are very competitive cancer biomarker sets. Indeed, when compared to 13 published cancer marker gene lists, HM200 achieves the best or second best classification performance in 79% of the classifications considered. CONCLUSION: These results indicate the existence of at least one general cancer marker set whose predictive value spans several tumor types and classification types. Our comparison with other marker gene lists shows that HM200 markers are mostly novel cancer markers. We also identify the previously published Pomeroy-400 list as another general cancer marker set. Strikingly, Pomeroy-400 has 27 genes in common with HM200. Our data suggest that a core set of genes are responsive to the deregulation of pathways involved in tumorigenesis in a variety of tumor types and that these genes could serve as transcriptional cancer markers in applications of clinical interest. Finally, our study suggests new strategies to select and evaluate cancer biomarkers in microarray studies.

Animals↗
PubMed2004

Chloroplast division site placement requires dimerization of the ARC11/AtMinD1 protein in Arabidopsis.

Chloroplast division is mediated by the coordinated action of a prokaryote-derived division system(s) and a host eukaryote-derived membrane fission system(s). The evolutionary conserved prokaryote-derived system comprises several nucleus-encoded proteins, two of which are thought to control division site placement at the midpoint of the organelle: a stromal ATPase MinD and a topological specificity factor MinE. Here, we show that arc11, one of 12 recessive accumulation and replication of chloroplasts (arc) mutants in Arabidopsis, contains highly elongated and multiple-arrayed chloroplasts in developing green tissues. Genomic sequence analysis revealed that arc11 contains a missense mutation in alpha-helix 11 of the chloroplast-targeted AtMinD1 changing an Ala at position 296 to Gly (A296G). Introduction of wild-type AtMinD1 restores the chloroplast division defects of arc11 and quantitative RT-PCR analysis showed that the degree of complementation was highly dependent on transgene expression levels. Overexpression of the mutant ARC11/AtMinD1 in transgenic plants results in the inhibition of chloroplast division, showing that the mutant protein has retained its division inhibition activity. However, in contrast to the defined and punctate intraplastidic localization patterns of an AtMinD1-YFP fusion protein, the single A296G point mutation in ARC11/AtMinD1 results in aberrant localization patterns inside chloroplasts. We further show that AtMinD1 is capable of forming homodimers and that this dimerization capacity is abolished by the A296G mutation in ARC11/AtMinD1. Our data show that arc11 is a loss-of-function mutant of AtMinD1 and suggest that the formation of functional AtMinD1 homodimers is paramount for appropriate AtMinD1 localization, ultimately ensuring correct division machinery placement and chloroplast division in plants.

Amino Acid Sequence↗
PubMed2004

Molecular elements of apoptosis-regulating pathways in follicular thyroid cells: mining for novel therapeutic targets in the treatment of thyroid carcinoma.

Apoptosis or programmed cell death occurs in both normal and pathological conditions, including cancer. Dysregulation of apoptosis allows transformed cells to continually and uninhibitedly enter the cell cycle, thus perpetuating the sequence of mutation, genomic instability and, finally, oncogenesis. The cell death machinery includes cell surface receptors, adaptor molecules, proteolytic enzymes, such as caspases, and a variety of mitochondrial proteins, which interact with each other in a complex fashion. In addition, extensive "cross-talk" exists between the apoptotic pathways and several other signaling systems that govern growth and differentiation. Recent advances in molecular techniques have shed light upon elements of the above pathways in assorted malignancies, including non-medullary thyroid carcinoma (ThyrCa). A subgroup of ThyrCa patients is (or becomes over time) refractory to standard treatment modalities and eventually succumbs to their disease. For such patients with clinically aggressive ThyrCa, novel therapeutic agents are urgently needed. Changes in the sensitivity of cells to apoptosis have clear implications for the treatment of any malignancy. In this review, we outline the main molecular targets that play a role in apoptosis in ThyrCa cells, and discuss various options for promoting apoptosis, either by pharmacologic or gene transfer therapeutic interventions.

Adenocarcinoma, Follicular↗
PubMed2006

Genomics of microRNA.

Discovered just over a decade ago, microRNA (miRNA) is now recognized as one of the major regulatory gene families in eukaryotic cells. Hundreds of miRNAs have been found in animals, plants and viruses, and there are certainly more to come. Through specific base-pairing with mRNAs, these tiny approximately 22-nt RNAs induce mRNA degradation or translational repression, or both. Because a miRNA can target numerous mRNAs, often in combination with other miRNAs, miRNAs operate highly complex regulatory networks. In this article, we summarize the current status of miRNA gene mining and miRNA expression profiling. We also review up-to-date knowledge of miRNA gene structure and the biogenesis mechanism. Our focus is on animal miRNAs.

Animals↗
PubMed2025

Systematic mining and quantification reveal the dominant contribution of non-HLA variations to acute graft-versus-host disease.

Human leukocyte antigen (HLA) disparity between donors and recipients is a key determinant triggering intense alloreactivity, leading to a lethal complication, namely, acute graft-versus-host disease (aGVHD), after allogeneic transplantation. Moreover, aGVHD remains a cause of mortality after HLA-matched allogeneic transplantation. Protocols for HLA-haploidentical hematopoietic cell transplantation (haploHCT) have been established successfully and widely applied, further highlighting the urgency of performing panoramic screening of non-HLA variations correlated with aGVHD. On the basis of our time-consecutive large haploHCT cohort (with a homogenous discovery set and an extended confirmatory set), we first delineated the genetic landscape of 1366 samples to quantitatively model aGVHD risk by assessing the contributions of HLA and non-HLA genes together with clinical factors. In addition to identifying multiple loss-of-function (LoF) risk variations in non-HLA coding genes, our data-driven study revealed that non-HLA genetic variations, independent of HLA disparity, contributed the most to the occurrence of aGVHD. This unexpected major effect was verified in an independent cohort that received HLA-identical sibling HCT. Subsequent functional experiments further revealed the roles of a representative non-HLA LoF gene and LoF gene pair in regulating the alloreactivity of primary human T cells. Our findings highlight the importance of non-HLA genetic risk in the new era of transplantation and propose a new direction to explore the immunogenetic mechanism of alloreactivity and to optimize donor selection strategies for allogeneic transplantation.

Humans↗
PubMed2005

MetaRouter: bioinformatics for bioremediation.

Bioremediation, the exploitation of biological catalysts (mostly microorganisms) for removing pollutants from the environment, requires the integration of huge amounts of data from different sources. We have developed MetaRouter, a system for maintaining heterogeneous information related to bioremediation in a framework that allows its query, administration and mining (application of methods for extracting new knowledge). MetaRouter is an application intended for laboratories working in biodegradation and bioremediation, which need to maintain and consult public and private data, linked internally and with external databases, and to extract new information from it. Among the data-mining features is a program included for locating biodegradative pathways for chemical compounds according to a given set of constraints and requirements. The integration of biodegradation information with the corresponding protein and genome data provides a suitable framework for studying the global properties of the bioremediation network. The system can be accessed and administrated through a web interface. The full-featured system (except administration facilities) is freely available at http://pdg.cnb.uam.es/MetaRouter. Additional material: http://www.pdg.cnb.uam.es/biodeg_net/MetaRouter.

Bacteria↗
PubMed2006

CTGA: the database for genetic disorders in Arab populations.

The Arabs comprise a genetically heterogeneous group that resulted from the admixture of different populations throughout history. They share many common characteristics responsible for a considerable proportion of perinatal and neonatal mortalities. To this end, the Centre for Arab Genomic Studies (CAGS) launched a pilot project to construct the 'Catalogue of Transmission Genetics in Arabs' (CTGA) database for genetic disorders in Arabs. Information in CTGA is drawn from published research and mined hospital records. The database offers web-based basic and advanced search approaches. In either case, the final search result is a detailed HTML record that includes text-, URL- and graphic-based fields. At present, CTGA hosts entries for 692 phenotypes and 235 related genes described in Arab individuals. Of these, 213 phenotypic descriptions and 22 related genes were observed in the Arab population of the United Arab Emirates (UAE). These results emphasize the role of CTGA as an essential tool to promote scientific research on genetic disorders in the region. The priority of CTGA is to provide timely information on the occurrence of genetic disorders in Arab individuals. It is anticipated that data from Arab countries other than the UAE will be exhaustively searched and incorporated in CTGA (http://www.cags.org.ae).

Arabs↗
PubMed2006

[Global gene response to GSM 1800 MHz radiofrequency electromagnetic field in MCF-7 cells].

OBJECTIVE: To investigate whether GSM 1800 MHz radiofrequency electromagnetic field (RF EMF) can change the gene expression profile in MCF-7 cells and to screen RF EMF responsive genes. METHODS: Subcultured MCF-7 cells were intermittently (5-minute fields on/10-minute fields off) exposed or sham-exposed to GSM 1800 MHz RF EMF, which was modulated by 217 Hz EMF, for 24 hours at an average specific absorption rate (SAR) of 2.0 W/kg or 3.5 W/kg. Immediately after RF EMF exposure or sham-exposure, total RNA was isolated from MCF-7 cells and then purified. Affymetrix Human Genome U133A Genechip was applied to examine the change of gene expression profile according to the manufacturer's instruction. Data was analyzed by Affymetrix Microarray Suite 5.0 (MAS 5.0) and Affymetrix Data Mining Tool 3.0 (DMT 3.0). Quantitative reverse transcription polymerase chain reaction (RT-PCR) was used to validate the differentially expressed genes identified by Genechip analysis. RESULTS: A small number of differential expression genes were found in each comparison after RF EMF exposure. Through reproducible and consistent analysis, no gene or five up-regulated genes were screened out after exposure to RF EMF at SAR of 2.0 W/kg or 3.5 W/kg, respectively. However, these five genes could not be further confirmed by RT-PCR. CONCLUSION: The present study did not provide clear evidence that RF EMF exposure might distinctly change the gene expression profile in MCF-7 cells under current experimental conditions, implying that the exposure might not affect the MCF-7 cell physiology, or this cell line might be less sensitive to the RF EMF exposure.

Cell Line, Tumor↗
PubMed2001

Survey of transposable elements from rice genomic sequences.

Oryza sativa L. (domesticated rice) is a monocotyledonous plant, and its 430 Mb genome has been targeted for complete sequencing. We performed a high-resolution computer-based survey for transposable elements on 910 Kb of rice genomic DNA sequences. Both class I and II transposable elements were present, contributing 19.9% of the sequences surveyed. Class II elements greatly outnumbered class I elements (166 versus 22), although class I elements made up a greater percentage (12.2% versus 6.6%) of nucleotides surveyed. Several Mutator-like elements (MULEs) were identified, including rice elements that harbor truncated host cellular genes. MITEs (miniature inverted-repeat transposable elements) account for 71.6% of the mined transposable elements and are clearly the predominant type of transposable element in the sequences examined. Moreover, a putative Stowaway transposase has been identified based on shared sequence similarity with the mined MITEs and previously identified plant mariner-like elements (MLEs). Members of a group of novel rice elements resembling the structurally unusual members of the Basho family in Arabidopsis suggest a wide distribution of these transposons among plants. Our survey provides a preview of transposable element diversity and abundance in rice, and allows for comparison with genomes of other plant species.

Base Sequence↗
PubMed2002

Combinatorial informatics in the post-genomics ERA.

The multitude of potential drug targets emerging from genome sequencing demands new approaches to drug discovery. A chemogenomics strategy, which involves the generation of small-molecule compounds that can be used both as tools to probe biological mechanisms and as leads for drug-property optimization, provides a highly parallel, industrialized solution. Key to the success of this strategy is an integrated suite of chemi-informatics applications that can allow the rapid and directed optimization of chemical compounds with drug-like properties using 'just-in-time' combinatorial chemical synthesis. An effective embodiment of this process requires new computational and data-mining tools that cover all aspects of library generation, compound selection and experimental design, and work effectively on a massive scale.

Combinatorial Chemistry Techniques↗
PubMed2001

What is bioinformatics? A proposed definition and overview of the field.

BACKGROUND: The recent flood of data from genome sequences and functional genomics has given rise to new field, bioinformatics, which combines elements of biology and computer science. OBJECTIVES: Here we propose a definition for this new field and review some of the research that is being pursued, particularly in relation to transcriptional regulatory systems. METHODS: Our definition is as follows: Bioinformatics is conceptualizing biology in terms of macromolecules (in the sense of physical-chemistry) and then applying "informatics" techniques (derived from disciplines such as applied maths, computer science, and statistics) to understand and organize the information associated with these molecules, on a large-scale. RESULTS AND CONCLUSIONS: Analyses in bioinformatics predominantly focus on three types of large datasets available in molecular biology: macromolecular structures, genome sequences, and the results of functional genomics experiments (e.g. expression data). Additional information includes the text of scientific papers and "relationship data" from metabolic pathways, taxonomy trees, and protein-protein interaction networks. Bioinformatics employs a wide range of computational techniques including sequence and structural alignment, database design and data mining, macromolecular geometry, phylogenetic tree construction, prediction of protein structure and function, gene finding, and expression data clustering. The emphasis is on approaches integrating a variety of computational methods and heterogeneous data sources. Finally, bioinformatics is a practical discipline. We survey some representative applications, such as finding homologues, designing drugs, and performing large-scale censuses. Additional information pertinent to the review is available over the web at http://bioinfo.mbb.yale.edu/what-is-it.

Computational Biology↗
PubMed2003

Mining the microbial metabolome: a new frontier for natural product lead discovery.

Traditionally, natural products have been important sources of new leads for the pharmaceutical industry, but with discovery rates of novel structural classes in decline, the need to bioprospect alternate sources of chemical diversity is evident. Microbial genome sequencing projects have revealed the presence of 'silent' biosynthetic gene clusters where there is no current detectable product. Likewise, culture-independent techniques have provided access to the collective genomes of environmental microflora. Both sources of molecular diversity could encode potentially valuable metabolites. The ability to measure the entire complement of metabolites within microorganisms that are used as surrogate hosts to express such gene clusters will be crucial to the exploitation of these yet untapped reservoirs of metabolic diversity for future natural product drug discovery.

Biological Factors↗
PubMed2000

Applied genomics: integration of the technology within pharmaceutical research and development.

Multiple novel technologies have recently been developed to improve the analysis of genetic sequences, to rapidly assess RNA or protein levels in relevant tissues, and to validate function of potential new drug targets. The challenge facing pharmaceutical research is one of effective integration of these new technologies in ways that can maximally affect the discovery and development pipeline. Although database mining and transcriptional profiling clearly have increased the number of putative targets, the current focus is to assign function to new gene targets in a high-throughput manner. This requires a restructuring of the classical linear progression from gene identification, functional elucidation, target validation and screen development. New approaches are called for that can make this process non-linear and high-throughput.

Animals↗
PubMed2005

Online analytical processing (OLAP): a fast and effective data mining tool for gene expression databases.

Gene expression databases contain a wealth of information, but current data mining tools are limited in their speed and effectiveness in extracting meaningful biological knowledge from them. Online analytical processing (OLAP) can be used as a supplement to cluster analysis for fast and effective data mining of gene expression databases. We used Analysis Services 2000, a product that ships with SQLServer2000, to construct an OLAP cube that was used to mine a time series experiment designed to identify genes associated with resistance of soybean to the soybean cyst nematode, a devastating pest of soybean. The data for these experiments is stored in the soybean genomics and microarray database (SGMD). A number of candidate resistance genes and pathways were found. Compared to traditional cluster analysis of gene expression data, OLAP was more effective and faster in finding biologically meaningful information. OLAP is available from a number of vendors and can work with any relational database management system through OLE DB.

Journal Article↗
PubMed2006

Development of an open source laboratory information management system for 2-D gel electrophoresis-based proteomics workflow.

BACKGROUND: In the post-genome era, most research scientists working in the field of proteomics are confronted with difficulties in management of large volumes of data, which they are required to keep in formats suitable for subsequent data mining. Therefore, a well-developed open source laboratory information management system (LIMS) should be available for their proteomics research studies. RESULTS: We developed an open source LIMS appropriately customized for 2-D gel electrophoresis-based proteomics workflow. The main features of its design are compactness, flexibility and connectivity to public databases. It supports the handling of data imported from mass spectrometry software and 2-D gel image analysis software. The LIMS is equipped with the same input interface for 2-D gel information as a clickable map on public 2DPAGE databases. The LIMS allows researchers to follow their own experimental procedures by reviewing the illustrations of 2-D gel maps and well layouts on the digestion plates and MS sample plates. CONCLUSION: Our new open source LIMS is now available as a basic model for proteome informatics, and is accessible for further improvement. We hope that many research scientists working in the field of proteomics will evaluate our LIMS and suggest ways in which it can be improved.

Computational Biology↗
PubMed2005

CASCAD: a database of annotated candidate single nucleotide polymorphisms associated with expressed sequences.

BACKGROUND: With the recent progress made in large-scale genome sequencing projects a vast amount of novel data is becoming available. A comparative sequence analysis, exploiting sequence information from various resources, can be used to uncover hidden information, such as genetic variation. Although there are enormous amounts of SNPs for a wide variety of organisms submitted to NCBI dbSNP and annotated in most genome assembly viewers like Ensembl and the UCSC Genome Browser, these platforms do not easily allow for extensive annotation and incorporation of experimental data supporting the polymorphism. However, such information is very important for selecting the most promising and useful candidate polymorphisms for use in experimental setups. DESCRIPTION: The CASCAD database is designed for presentation and query of candidate SNPs that are retrieved by in silico mining of high-throughput sequencing data. Currently, the database provides collections of laboratory rat (Rattus norvegicus) and zebrafish (Danio rerio) candidate SNPs. The database stores detailed information about raw data supporting the candidate, extensive annotation and links to external databases (e.g. GenBank, Ensembl, UniGene, and LocusLink), verification information, and predictions of a potential effect for non-synonymous polymorphisms in coding regions. The CASCAD website allows search based on an arbitrary combination of 27 different parameters related to characteristics like candidate SNP quality, genomic localization, and sequence data source or strain. In addition, the database can be queried with any custom nucleotide sequences of interest. The interface is crosslinked to other public databases and tightly coupled with primer design and local genome assembly interfaces in order to facilitate experimental verification of candidates. CONCLUSIONS: The CASCAD database discloses detailed information on rat and zebrafish candidate SNPs, including the raw data underlying its discovery. An advanced web-based search interface http://cascad.niob.knaw.nl allows universal access to the database content and allows various queries supporting many types of research utilizing single nucleotide polymorphisms.

Animals↗
PubMed2004

Genomic and proteomic profiling for biomarkers and signature profiles of toxicity.

Toxicity profiling measures and compares all gene expression changes among biological samples after toxicant exposure. Toxicity profiling with DNA microarrays to measure all mRNA transcripts (transcriptomics), or by global separation and identification of proteins (proteomics), has led to the discovery of better descriptors of toxicity, toxicant classification and exposure monitoring than current indicators. A shared goal in transcript and proteomic profiling is the development of biomarkers and signatures of chemical toxicity. In this review, biomarkers and signature profiles are described for specific chemical toxicants that affect target organs such as liver, kidney, neural tissues, gastrointestinal tract and skeletal muscle, for specific disease models such as cancer and inflammation, and for unique chemical-protein adducts underlying cell injury. The recent introduction of toxicogenomics databases support researchers in sharing, analyzing, visualizing and mining expression data, assist the integration of transcriptomics, proteomics and toxicology datasets, and eventually will permit in silico biomarker and signature pattern discovery.

Animals↗