2007 (English)In: The Ethics and Governance of Human Genetic Databases: European Perspectives / [ed] Matti Häyry, Ruth Chadwick, Vilhjálmur Árnason & Gardar Árnason, Cambridge: Cambridge University Press, 2007, no 4, 108-119 p.Chapter in book (Other academic) ...
770 full-length cDNAs. Analysis of the mouse transcriptome based on functional annotation of 60s profile, publications, research topics, and co-authors
The Gene Expression Database (GXD) is a community resource that stores and integrates expression information for the laboratory mouse, with a particular emphasis on mouse development, and makes these data freely available in formats appropriate for comprehensive analysis. GXD is implemented as a relational database and integrated with the Mouse Genome Database (MGD) to enable global analysis of genotype, expression and phenotype information. Interconnections with sequence databases and with databases from other species further extend GXDs utility for the analysis of gene expression data. GXD is available through the Mouse Genome Informatics Web Site at http://www.informatics.jax.org/
In a continuation of a 2014 conference that explored regulatory considerations and strategies for next-generation sequencing, the Friends of Cancer Research, with support from Alexandria Real Estate Equities, Inc, Pasadena, California, met to discuss the issues and problems of coordinating drug and diagnostic development, specifically the use of curated databases.. Ellen V. Sigal, PhD, Chair and Founder of Friends of Cancer Research, introduced the gathering by noting that high-throughput genomic technologies, including next-generation sequencing, allow for rapid assessment of many analytes and can help predict patients risk of developing certain cancers and how they might respond to therapies. There are many advantages of high-throughput sequencing over that of a single analyte, but demonstrating its adequacy for clinical use is challenging, particularly the tension between the need to ensure validity and the practical limitations of submitting data for every possible variant.. She added ...
TY - JOUR. T1 - Analysis of the mouse transcriptome for genes involved in the function of the nervous system. AU - Gustincich, Stefano. AU - Batalov, Serge. AU - Beisel, Kirk W.. AU - Bono, Hidemasa. AU - Carninci, Piero. AU - Fletcher, Colin F.. AU - Grimmond, Sean. AU - Hirokawa, Nobutaka. AU - Jarvis, Erich D.. AU - Jegla, Tim. AU - Kawasawa, Yuka. AU - LeMieux, Julianna. AU - Miki, Harukata. AU - Raviola, Elio. AU - Teasdale, Rohan D.. AU - Tominaga, Naoko. AU - Yagi, Ken. AU - Zimmer, Andreas. AU - Arakawa, Takahiro. AU - Waki, Kazunori. AU - Kawai, Jun. AU - Hayashizaki, Yoshihide. AU - Okazaki, Yasushi. PY - 2003/6/1. Y1 - 2003/6/1. N2 - We analyzed the mouse Representative Transcript and Protein Set for molecules involved in brain function. We found full-length cDNAs of many known brain genes and discovered new members of known brain gene families, including Family 3 G-protein coupled receptors, voltage-gated channels, and connexins. We also identified previously unknown candidates for ...
The rich knowledge of morphological variation among organisms reported in the systematic literature has remained in free-text format, impractical for use in large-scale synthetic phylogenetic work. This noncomputable format has also precluded linkage to the large knowledgebase of genomic, genetic, developmental, and phenotype data in model organism databases. We have undertaken an effort to prototype a curated, ontology-based evolutionary morphology database that maps to these genetic databases (http://kb.phenoscape.org) to facilitate investigation into the mechanistic basis and evolution of phenotypic diversity. Among the first requirements in establishing this database was the development of a multispecies anatomy ontology with the goal of capturing anatomical data in a systematic and computable manner. An ontology is a formal representation of a set of concepts with defined relationships between those concepts. Multispecies anatomy ontologies in particular are an efficient way to represent ...
The browser lines at the beginning of the custom track indicate which native tracks to turn on along their visibilities, while the hide all line turns all the other native tracks off. In addition to these basic instructions there are many more examples on the UCSC Genome Browser Wiki.. What about when you want to view a genome and annotations not hosted on our site? If you have a FASTA file of your genome available, you can use faToTwoBit to convert your genome into a 2bit file, then make an assembly hub out of your data. Once youve created your hub, you can view the hub with the hubUrl setting. As an example, I have hosted an assembly hub for Arabadopsis thaliana here, and I can view the hub via a single URL like so ...
The GMOD project is a confederation of intercompatible open-source projects developing software tools for storing, managing, curating, and publishing biological data. Although the GMOD project originated from the goal of developing a generic tool set for common needs among model organism databases, GMOD tools are meanwhile used by many large and small, collaborative and single-investigator biological database projects for the dissemination of results of experimental research and curated knowledge. GMODs software tools provide a powerful and feature-rich basis for working with biological, in particular genomic and other molecular data. However, due to GMODs historical emphasis on single-genome projects many GMOD tools still lack features that are critical to effectively support the comparative, phylogenetic, and natural diversity-oriented questions frequently asked in evolutionary research. Recent developments have given rise to a window of opportunity for forging collaborations towards filling ...
Researchers from the University of Maryland School of Medicines (UMSOM) Institute for Genome Sciences (IGS) have created VIRGO (human vaginal non-redundant gene catalog): the first genomic catalog of the vaginal microbiome.
GeneWeaver is a web application for the integrated cross-species analysis of functional genomics data to find convergent evidence from heterogeneous sources. The application consists of a large database of gene sets curated from multiple public data resources and curated submissions, along with a suite of analysis tools designed to allow flexible, customized workflows through web-based interactive analysis or scripted API driven analysis. Gene sets come from multiple widely studied species and include ontology annotations, brain gene expression atlases, systems genetic study results, gene regulatory information, pathway databases, drug interaction databases and many other sources. Users can retrieve, store, analyze and share gene sets through a graded access system. Analysis tools are based on combinatorics and statistical methods for comparing, contrasting and classifying gene sets based on their members.. ...
GeneWeaver is a web application for the integrated cross-species analysis of functional genomics data to find convergent evidence from heterogeneous sources. The application consists of a large database of gene sets curated from multiple public data resources and curated submissions, along with a suite of analysis tools designed to allow flexible, customized workflows through web-based interactive analysis or scripted API driven analysis. Gene sets come from multiple widely studied species and include ontology annotations, brain gene expression atlases, systems genetic study results, gene regulatory information, pathway databases, drug interaction databases and many other sources. Users can retrieve, store, analyze and share gene sets through a graded access system. Analysis tools are based on combinatorics and statistical methods for comparing, contrasting, and classifying gene sets based on their members.. ...
To whom it may concern: The completion of the sequencing of the entire DNA of the S. cerevisae genome, is a major event in the history of biology. All those involved are to be congratulated as we now have the first full genetic blueprint of a free living eukaryotic organism. The analysis of these gene products will provide us with a powerful tool for reading the genomes of other eukaryotes, particularly those of higher eukaryotes, which represent the majority of the data currently in the genetic databases. The analysis of the yeast genome is provided a useful framework for the annotation of many of the complete genome projects currently nearing completion, as well as the upcoming human genome. The yeast sequence information used to create this yeast webpage was provided by the GeneQuiz Consortium and the Mips Genome Commission . We have made an initial attempt to integrate these two data structures as well as supplement their annotation with that obtained ,From a set of functionally diagnostic ...
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TY - JOUR. T1 - Mining cancer gene expression databases for latent information on intronic microRNAs. AU - Monterisi, Simona. AU - DArio, Giovanni. AU - Dama, Elisa. AU - Rotmensz, Nicole. AU - Confalonieri, Stefano. AU - Tordonato, Chiara. AU - Troglio, Flavia. AU - Bertalot, Giovanni. AU - Maisonneuve, Patrick. AU - Viale, Giuseppe. AU - Nicassio, Francesco. AU - Vecchi, Manuela. AU - Di Fiore, Pier Paolo. AU - Bianchi, Fabrizio. PY - 2015/2/1. Y1 - 2015/2/1. N2 - Around 50% of all human microRNAs reside within introns of coding genes and are usually co-transcribed. Gene expression datasets, therefore, should contain a wealth of miRNA-relevant latent information, exploitable for many basic and translational research aims. The present study was undertaken to investigate this possibility. We developed an in silico approach to identify intronic-miRNAs relevant to breast cancer, using public gene expression datasets. This led to the identification of a miRNA signature for aggressive breast ...
de Leeuw N, Dijkhuizen T, Hehir-Kwa JY, Carter NP, Feuk L, Firth HV, Kuhn RM, Ledbetter DH, Martin CL, van Ravenswaaij-Arts CM, Scherer SW, Shams S, Van Vooren S, Sijmons R, Swertz M, Hastings R. Diagnostic interpretation of array data using public databases and internet sources. Hum Mutat. 2012 Jun;33(6):930-40. PMID: 26285306; PMC: PMC5027376 Dreszer TR, Karolchik D, Zweig AS, Hinrichs AS, Raney BJ, Kuhn RM, Meyer LR, Wong M, Sloan CA, Rosenbloom KR, Roe G, Rhead B, Pohl A, Malladi VS, Li CH, Learned K, Kirkup V, Hsu F, Harte RA, Guruvadoo L, Goldman M, Giardine BM, Fujita PA, Diekhans M, Cline MS, Clawson H, Barber GP, Haussler D, Kent WJ The UCSC Genome Browser database: extensions and updates 2011. Nucleic Acids Res. 2012 Jan;40(Database issue):D918-23. PMID: 22086951; PMC: PMC3245018. ENCODE Project Consortium, Dunham I, Kundaje A, Aldred SF, Collins PJ, Davis CA, Doyle F, Epstein CB, Frietze S, Harrow J et al. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012 ...
PubMed comprises more than 30 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.
PubMed comprises more than 30 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.
MicroRNAs (miRNAs) play a vital role in the development of ovarian cancer (OC). The aim of this study to investigate the prognostic value and potential signaling pathways of hsa-miR-9-5p (miR-9) in OC through literature review and bioinformatics methods. The expression of miR-9 in OC was assessed using the public datasets from the Gene Expression Omnibus (GEO) database. And a literature review was also performed to investigate the correlation between miR-9 expression and the OC prognosis. Two mRNA datasets (GSE18520 and GSE36668) of OC tissues and normal ovarian tissues (NOTs) were downloaded from GEO to identify the differentially expressed genes (DEGs). The target genes of hsa-miR-9-5p (TG-miR-9-5p) were predicted using miRWALK3.0 and TargetScan. Then the gene overlaps between DEGs in OC and the predicted TG-miR-9-5p were confirmed using a Venn diagram. After that, overlapping genes were subjected to Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway
The HGNC Comparison of Orthology Predictions (HCOP) search is a tool that integrates and displays the orthology assertions predicted for a specified human gene, or set of human genes, by eggNOG, Ensembl Compara, HGNC, HomoloGene, Inparanoid, NCBI Gene Orthology, OMA, OrthoDB, OrthoMCL, Panther, PhylomeDB, PomBase, TreeFam and ZFIN.
The HGNC Comparison of Orthology Predictions (HCOP) search is a tool that integrates and displays the orthology assertions predicted for a specified human gene, or set of human genes, by eggNOG, Ensembl Compara, HGNC, HomoloGene, Inparanoid, NCBI Gene Orthology, OMA, OrthoDB, OrthoMCL, Panther, PhylomeDB, PomBase, TreeFam and ZFIN.
The remainder of corrections will most likely require the curator to go back to the paper to determine how the entity in the extension is related to the primary GO term annotated (this demonstrates that the meaning of the existing annotation is not clear and should be modified). If a curator comes across an example that is not covered by this guidance, and it is not clear how the annotation should be updated, the example should be brought to an annotation call to be discussed and resolved. Additional recommendations have been made for specific annotations on previous annotation calls; http://wiki.geneontology.org/index.php/Annotation_Conf._Call,_June_23,_2015 http://wiki.geneontology.org/index.php/Annotation_Conf._Call,_July_28,_2015 See the Excel spreadsheet containing details of annotations using deprecated relations, with assigned_by information ...
Once the MODs annotations have been integrated into our database, UniProt-GOA will provide the MOD with a file in the GAF2.0 format containing the entire set of GO annotations that match the taxon identifier(s) the MOD is responsible for as well as any additional annotations the MOD has created to other taxons. When importing the annotations back into their own database, the MOD can either note the updates made in this set from the changes in the date attached to each annotation (dates indicate when the last edit was made to the annotation) or they can carry out a full delete and reload of their GO annotation set. Any annotations that we cannot accept from the MOD, but which the MOD wants to keep can be appended to the supplied GAF by the MOD, e.g. annotations to non-coding RNAs, annotations using internal references that arent mapped to a GO_REF, IEA annotations, etc. UniProt-GOA will not store the annotations that are excluded, so it is up to the MOD to keep a record of these. If required, ...
Accessing and exploring large-scale genomics data sets remains a significant challenge to researchers without specialist bioinformatics training. We present the integrated PlantGenIE.org platform for exploration of Populus, conifer and Arabidopsis genomics data, which includes expression networks and associated visualization tools. Standard features of a model organism database are provided, including genome browsers, gene list annotation, BLAST homology searches and gene information pages. Community annotation updating is supported via integration of WebApollo. ...
The mouse genome database (MGD, http://www.informatics.jax.org/), the international community database for mouse, provides access to extensive integrated data on the genetics, genomics and biology of the laboratory mouse. The mouse is an excellent and unique animal surrogate for studying normal development and disease processes in humans. Thus, MGDs primary goals are to facilitate the use of mouse models for studying human disease and enable the development of translational research hypotheses based on comparative genotype, phenotype and functional analyses. Core MGD data content includes gene characterization and functions, phenotype and disease model descriptions, DNA and protein sequence data, polymorphisms, gene mapping data and genome coordinates, and comparative gene data focused on mammals. Data are integrated from diverse sources, ranging from major resource centers to individual investigator laboratories and the scientific literature, using a combination of automated processes and
Description== Leflunomide is a an isoxazole derivative that inhibits dihydroorotate dehydrogenase, the fourth enzyme in the pyrimidine biosynthetic pathway. Source: [https://www.ncbi.nlm.nih.gov/mesh/2028012 MeSH] [[File:Screen Shot 2019-04-19 at 2.26.27 PM.png,frame,right, structure, image from PubChem]] ==Alternative names== ==Usage Notes== ==References== *[http://www.xenbase.org/literature/article.do?method=display&articleId=52355 Hatch et al 2016] *[https://pubchem.ncbi.nlm.nih.gov/compound/3899 NCBI PubChem CID:3899] > 4 Xenbase articles contain a reference to Leflunomide according to [http://www.xenbase.org/cgi-bin/textpresso/xenopus/search textpresso] *[[Small Molecules for Xenopus Research,Back To Small Molecules Home Page ...
A program written by UCSC student Jim Kent, called GigAssembler, is used to periodically assemble a widely used public draft version of the human genome sequence using updated data from GenBank at the National Center For Biotechnology Information (NCBI). This assembly is steadily improving as the the public sequencing consortium churns out new data. We will look at the coverage statistics on the latest assembly, and then look at web tools to explore it, and what they find. The three most widely used public annotation browsers are the UCSC Genome browser (genome.ucsc.edu), the Ensembl genome browser (www.ensembl.org), and the NCBI map viewer (www.ncbi.nlm.nih.gov/genome/guide), the latter based on NCBIs own sequence assembly. We will focus on the UCSC browser, which shows a rich variety of data mapped to the genome sequence, including predicted genes, expressed sequence tags, full length mRNAs, genetic and radiation hybrid map markers, cytogenetically mapped clones, single nucleotide ...
Web Portal for International Cancer Research: Cancer Epidemiology and Genetic Databases, Research Programmes, Electronic Publications, Scientific Papers, IARC Press Releases, IARC Training Courses, IARC Fellowships for Cancer Research, IARC Meetings, etc
The Los Angeles Times reports that an Arizona crime lab technician found two felons with remarkably similar genetic profiles, so similar that they would ordinarily be accepted in court as a match, but one felon was black and the other white. The FBI estimated the odds of unrelated people sharing those genetic markers to be as remote as 1 in 113 billion. Dozens of similar matches have been found, and these findings raise questions about the accuracy of the FBIs DNA statistics. Scientists and legal experts want to test the accuracy of official statistics using the nearly 6 million profiles in CODIS, the national system that includes most state and local databases. The FBI has tried to block distribution of the Arizona results and is blocking people from performing similar searches using CODIS. A legal fight is brewing over whether the nations genetic databases ought to be opened to wider scrutiny. At stake is the credibility of the odds often cited in DNA cases, which can suggest an all but ...
topgo r topGO package provides tools for testing GO terms while accounting for the topology of the GO graph. org packages topGO. Asif D. Not required Entrez Gene IDs this example takes Ensembl Gene IDs and calcuates GO enrichment. I have a predefined list of the Ensembl gene IDs n 28 and I want to perform Gene Ontology using topGO in R. We used the R package topGO Alexa amp Rahnenf hrer 2009 to investigate the potential gene ontologies GO that were statistically enriched for the sets of genes identified among outliers and the Topgo Co. Alexa A Rahnenfuhrer J 2020 . Compare cash back offers. 886 2 2366 1346 Both PCA and k estimates were carried out in R v. It supports GO annotation from OrgDb object GMT file and user s own data. LEA . script. printTopGOresult . TopGO R package 45 was used for Gene Ontology enrichment analysis as well as DAVID 6. 2 years ago by al ash 20 lt prev 182 results page 1 of 8 next gt TopGo authors recommend 5 10 for more stable results 1 for no prune. 852 respectively . ...
Don Gilbert pointed out that cheap short sequencers are now available. Lots of people have inexpensive sequnces, but there still is no way to do cheap annotation. Current GMOD clients are species or family centered. Want to make it easy to integrate multiple species. ApiDB is at the point of opening new species databases and web sites with relatively little effort. Comparative genomics came up over and over again, both across species and within species. As data grows and is consolidated, issues of who owns the data and whos responsible for the annotation become more problematic. How does GMOD want to deal with integration issues? How close to the sequencer does GMOD want to get? We dont want to pull the data off the sequencer. Should we position GMOD as something that can feed data into places like Ensembl? Ensembl does not have curation expertise of the MODs. Even if NCBI is wonderful at consolidation, they wont have quality curation. GMOD sits right there, supporting curation. So, we doubt ...
RiceGE : Rice Functional Genomics Database. gebd Rice Genome Browser. iSect Tools, iView Tools and Gene Expression Atlas. Collection of Rice T-DNA/Ds, Rice cDNA, Marker, EST, MPSS, SAGE, Arabidopsis, Maize, Wheat, Barley Homology, Rice Tiling Array and Gene Expression Data. Created and developed by Huaming Chen
RiceGE : Rice Functional Genomics Database. gebd Rice Genome Browser. iSect Tools, iView Tools and Gene Expression Atlas. Collection of Rice T-DNA/Ds, Rice cDNA, Marker, EST, MPSS, SAGE, Arabidopsis, Maize, Wheat, Barley Homology, Rice Tiling Array and Gene Expression Data. Created and developed by Huaming Chen
Dear Pegah Tavakolkhah, , I have one more question. I have read in the description of some GOs , that they are involved in apoptosis or angiogenesis (which are the main , reasons of cancer). I wanted to know if apoptosis or angiogenesis are GO , themselves. And if they are, would their descendant GOs show the GOs , which are responsible for these two processes? If you do a search for terms either using GOOSE or using AmiGO (http://amigo.geneontology.org/), you can find the GO terms apoptosis (GO:0006915; http://amigo.geneontology.org/cgi-bin/amigo/term-details.cgi?term=GO:0006915) and angiogenesis (GO:0001525; http://amigo.geneontology.org/cgi-bin/amigo/term-details.cgi?term=GO:0001525). You can find the gene products that have been associated with these processes (and the more specific child processes), either by choosing the gene product associations link in AmiGO, or by using GOOSE to query the GO database. I hope that is helpful; if you have any more questions, please dont hesitate to ...
GO annotations: Mouse from MGI; Human from GO Annotations @ EBI (GOA); Rat from RGD; Chicken from GOA; Fly from FlyBase; Pfalc from PlasmoDB; Worm from WormBase; Dicty from dictyBase; Yeast from SGD; Zfin from ZFIN; Tair from TAIR/TIGR; Rice from Gramene; Pombe from Sanger GeneDB ...
Column 16 refers to a column in the Gene Ontologys (GO) tab-delimited gene association file (gaf) that WormBase submits to the GO consortium on a regular basis. Column 16 has been referred to as the Annotation Extension column in that it provides a placeholder for curation details that cannot be captured by a GO term alone, for example the substrate upon which an enzyme acts. A number of different types of information could conceivably be entered into Column 16. The list below begins to document the potential use of Column 16 by WormBase curators with any additional information or questions that have arisen during the course of curation. In the GAF, there will be an explicit relationship between the entity in Column 16 and the GO term. The annotation extension relations are viewable here: http://www.geneontology.org/scratch/xps/go_annotation_extension_relations.obo Column 16 curation at WormBase is just beginning and will likely be fleshed out more fully over the next few months. In the ...
Representation and depiction of phenotype information at SOC and HLT level. (A) We used the hierarchical information of the MedDRA ontology to map all phenotypi
FatiGO is a web-accessible application that functions in much the same way as DAVIDs GoCharts, including the ability to specify term-specificity level. Unlike DAVID, FatiGO does not allow the setting of a minimum hit threshold for simplified viewing of only the most highly represented functional categories. Likewise, FatiGO limits the graphical output to only one top-level GO category at a time, whereas DAVID allows the combined viewing of biological process, molecular function, and cellular component annotations simultaneously. FatiGOs static barchart output looks very similar to DAVIDs GoChart; an important distinction is that DAVIDs GoCharts are dynamic, allowing users to drill-down and traverse the GO hierarchy for any subset of genes, view the underlying chart data and associated annotations, and link out to external data repositories including LocusLink and QuickGO. As shown in Table 3 the majority of accession types accepted and functional annotations offered by DAVID are not ...
Description: This session displays a region of the LHX6 gene that highlights a selection of the new tracks added in the previous year for the hg38/GRCh38 human assembly. The tracks shown in this display (from top to bottom) include GENCODE Genes V22, transcription levels assayed across 9 ENCODE cell lines, DNase hypersensitive regions based on data from 95 ENCODE cell lines, genome-wide conservation scores calculated using phastCons, a multiple genome alignment created using Lastz and Multiz, and pathogenic CNVs from the ClinGen database. Adapted from Figure 1 in Speir, et al. The UCSC Genome Browser database: 2016 update. Nucleic Acids Res. 2016 Jan 4;44(D1):D717-25: http://nar.oxfordjournals.org/content/44/D1/D717. ...
Description: This session displays a region of the LHX6 gene that highlights a selection of the new tracks added in the previous year for the hg38/GRCh38 human assembly. The tracks shown in this display (from top to bottom) include GENCODE Genes V22, transcription levels assayed across 9 ENCODE cell lines, DNase hypersensitive regions based on data from 95 ENCODE cell lines, genome-wide conservation scores calculated using phastCons, a multiple genome alignment created using Lastz and Multiz, and pathogenic CNVs from the ClinGen database. Adapted from Figure 1 in Speir, et al. The UCSC Genome Browser database: 2016 update. Nucleic Acids Res. 2016 Jan 4;44(D1):D717-25: http://nar.oxfordjournals.org/content/44/D1/D717. ...
Symbol: This is the official symbol assigned to this strain according to the strain nomenclature guidelines. This is a combination of strain and substrain designations for inbred strains (or symbol and ILAR code for other strain types).. Strain: The official strain symbol.. Substrain: The official substrain symbol - this can be a collection of ILAR lab codes defining the history of this particular strain. Can also be found in pulldown section below with links to the strain report pages.. Full Name: If the strain has a text name then it is displayed here; this is not visible if no name is associated to the strain, as in this example. Ontology ID: The identification number of the strain ontology term assigned by RGD, linked to the term in the ontology browser. In the strain ontology, rat strains are organized in a hierarchical fashion based on the type of strain and the way they were developed.. Also known as: Old symbols and synonyms that were used for the strain. If a strain is renamed to comply ...
Developmental Anatomic Gene Expression Atlas (AGEA) The Allen Gene Expression Atlas (AGEA) for the Developing Mouse Brain is used to understand how voxels of the brain are related by gene expression (Correlation), and to find genes expressed at a particul
The comparability of gene expression data generated with different microarray platforms is still a matter of concern. Here we address the performance and the overlap in the detection of differentially expressed genes for five different microarray platforms in a challenging biological context where differences in gene expression are few and subtle. Gene expression profiles in the hippocampus of five wild-type and five transgenic δC-doublecortin-like kinase mice were evaluated with five microarray platforms: Applied Biosystems, Affymetrix, Agilent, Illumina, LGTC home-spotted arrays. Using a fixed false discovery rate of 10% we detected surprising differences between the number of differentially expressed genes per platform. Four genes were selected by ABI, 130 by Affymetrix, 3,051 by Agilent, 54 by Illumina, and 13 by LGTC. Two genes were found significantly differentially expressed by all platforms and the four genes identified by the ABI platform were found by at least three other platforms.
DEVELOPMENTAL ANATOMIC GENE EXPRESSION ATLAS (AGEA) AGEA is an interactive relational atlas based on spatial correlations of gene expression data for \2000 genes in the Allen Developing Mouse Brain Atlas. AGEA for the Developing Mouse Brain is used to und
The Ensembl human gene annotations have been updated using Ensembls automatic annotation pipeline. The updated annotation incorporates new protein and cDNA sequences which have become publicly available since the last GRCh37 genebuild (March 2009).. In release 67 (May 2012), we continue to display a joint gene set based on the merge between the automatic annotation from Ensembl and the manually curated annotation from Havana. This refined gene set corresponds to GENCODE release 12. The Consensus Coding Sequence (CCDS) identifiers have also been mapped to the annotations. More information about the CCDS project. Updated manual annotation from Havana is merged into the Ensembl annotation every release. Transcripts from the two annotation sources are merged if they share the same internal exon-intron boundaries (i.e. have identical splicing pattern) with slight differences in the terminal exons allowed. Importantly, all Havana transcripts are included in the final Ensembl/Havana merged (GENCODE) ...
Here at the Genome Browser were constantly looking for ways to improve the Browser and make it more accessible. A big part of that is making it as easy as possible for people to learn how to use our tools to best serve their research. In the past this has included setup and maintenance of documentation, including our help docs as well as a dedicated wiki site, where browser staffers and external users alike have shared content. We also continue to offer real-time support on our mailing list ([email protected]).. Thanks to funding support from the NHGRI we were recently able to amp up our training efforts in two ways. We now have a program whereby interested groups can economically host a Genome Browser workshop at their institution. For more information, fill out our intake survey: bit.ly/ucscTraining.. The other thing we have been able to do is launch a YouTube channel where you will find video tutorials explaining how to use various parts of the Browser. While static documents and email ...
TY - JOUR. T1 - ParkDB. T2 - A Parkinsons disease gene expression database. AU - Taccioli, Cristian. AU - Maselli, Vincenza. AU - Tegnér, Jesper. AU - Gomez-Cabrero, David. AU - Altobelli, Gioia. AU - Emmett, Warren. AU - Lescai, Francesco. AU - Gustincich, Stefano. AU - Stupka, Elia. N1 - Generated from Scopus record by KAUST IRTS on 2021-02-16. PY - 2011/12/1. Y1 - 2011/12/1. N2 - Parkinsons disease (PD) is a common, adult-onset, neuro-degenerative disorder characterized by the degeneration of cardinal motor signs mainly due to the loss of dopaminergic neurons in the substantia nigra. To date, researchers still have limited understanding of the key molecular events that provoke neurodegeneration in this disease. Here, we present ParkDB, the first queryable database dedicated to gene expression in PD. ParkDB contains a complete set of re-Analyzed, curated and annotated microarray datasets. This resource enables scientists to identify and compare expression signatures involved in PD and ...
We investigate large functional genomics and high-throughput biological datasets. Assistance is provided in experimental design and subsequent analysis of next-generation sequencing, microarray, and mass-spectrometry-based proteomics experiments. The current focus is on the analysis of small RNA-Seq, mRNA-Seq and haploid ES cell screen data. Gene lists derived from publicly available studies or generated from in-house high-throughput experiments (NGS, microarray, proteomics) are analyzed for the overrepresentation of pathways, GO-terms, functional domains, or placed in interaction networks to visualize their relationships. Genome-wide expression patterns are contextualized with known processes and pathways using Gene Set Enrichment Analysis (GSEA). Local instances of integrated model organism databases and genome annotation portals permit visualization and analysis of in-house data with dedicated resources and additional privacy. User-driven data exploration is supported by the Ingenuity Pathway ...
We investigate large functional genomics and high-throughput biological datasets. Assistance is provided in experimental design and subsequent analysis of next-generation sequencing, microarray, and mass-spectrometry-based proteomics experiments. The current focus is on the analysis of small RNA-Seq, mRNA-Seq and haploid ES cell screen data. Gene lists derived from publicly available studies or generated from in-house high-throughput experiments (NGS, microarray, proteomics) are analyzed for the overrepresentation of pathways, GO-terms, functional domains, or placed in interaction networks to visualize their relationships. Genome-wide expression patterns are contextualized with known processes and pathways using Gene Set Enrichment Analysis (GSEA). Local instances of integrated model organism databases and genome annotation portals permit visualization and analysis of in-house data with dedicated resources and additional privacy. User-driven data exploration is supported by the Ingenuity Pathway ...
Allows to align query sequences against those present in a selected target database. BLAST is a suite of programs, provided by NCBI, which can be used to quickly search a sequence database for matches to a query sequence. The software provides an access point for these tools to perform sequence alignment on the web. The set of BLAST command-line applications is organized in a way that groups together similar types of searches in one application.
Strategies for discovering the genetic polymorphism responsible for an identified quantitative trait locus (QTL) generally follow two paths. One path involves generating additional experimental mapping populations to narrow an initial, wide QTL support interval [1]. For example, Yalcin et al. [2], used outbred mice and a QTL-knockout interaction test to identify Rgs2 as the gene underlying an anxiety phenotype. The other path involves making use of bioinformatic tools and archival data to better nominate candidate genes within a QTL support interval [3, 4]. For example, Flint and colleagues review and apply a hypothesis of human and mouse sequence conservation that may aid QTL gene or polymorphism discovery [5-7]. The combination of approaches should facilitate polymorphism identification, and more rapidly.. The BXD, an increasingly popular tool for mouse complex trait genetics, are a panel of recombinant inbred lines derived by inbreeding progeny from a C57BL/6J × DBA/2J F2 intercross [8]. ...
Cyclome: Arabidopsis Cyclome Functional Genomics Database. gebd Arabidopsis Genome Browser. iSect Tools, iView Tools and Gene Expression Atlas. Collection of Arabidopsis T-DNA/Ds, Full-length cDNA, Marker, EST, MPSS, SAGE, miRNA, sRNA, Arabidopsis Tiling Array and Gene Expression Data. Created and developed by Huaming Chen
Circadian rhythms of cell and organismal physiology are controlled by an autoregulatory transcription-translation feedback loop that regulates the expression of rhythmic genes in a tissue-specific manner. Recent studies have suggested that components of the circadian pacemaker, such as the Clock and …
#929394 - Construction of Decision Trees Based on Gene Expression Omnibus Data to Classify Bladder Cancer and Its Subtypes - Full View
This is the website for the Reed Labs Butterfly Genome Database at Cornell University.. This site provides a portal for searching and browsing high quality butterfly genome assemblies that are annotated with specialized data types including gene expression (e.g. RNA-seq), chromatin structure, and SNP variation. Data will be added on a rolling basis, and we encourage contributions from other research groups.. Blast: Search genome assemblies and gene predictions using Blast. Genome browser links are embedded in Blast result for your convenience.. Genome Browser: We use the UCSC genome browser as the most powerful current interface for manipulating and viewing complex data tracks. On this page you can go directly to any relevant coordinate in any genome we host.. Downloads: Download genome assemblies and accessory data tracks, as well as custom scripts from Reed Lab publications.. Citations: Publications to cite for specific data sets.. Please note that there are many additional lepidopteran ...
Web Portal for International Cancer Research: Cancer Epidemiology and Genetic Databases, Research Programmes, Electronic Publications, Scientific Papers, IARC Press Releases, IARC Training Courses, IARC Fellowships for Cancer Research, IARC Meetings, etc
Web Portal for International Cancer Research: Cancer Epidemiology and Genetic Databases, Research Programmes, Electronic Publications, Scientific Papers, IARC Press Releases, IARC Training Courses, IARC Fellowships for Cancer Research, IARC Meetings, etc
MaizeGDB is the maize research communitys central repository for genetic and genomic information about the crop plant and research model Zea mays ssp. mays. The MaizeGDB team endeavors to meet research needs as they evolve based on researcher feedback and guidance. Recent work has focused on better integrating existing data with sequence information as it becomes available for the B73, Mo17 and Palomero Toluqueño genomes. Major endeavors along these lines include the implementation of a genome browser to graphically represent genome sequences; implementation of POPcorn, a portal ancillary to MaizeGDB that offers access to independent maize projects and will allow BLAST similarity searches of participating projects data sets from a single point; and a joint MaizeGDB/PlantGDB project to involve the maize community in genome annotation. In addition to summarizing recent achievements and future plans, this article also discusses specific examples of community involvement in setting priorities and design
Bidirectional promoters are short (,1 kbp) intergenic regions of DNA between the 5 ends of the genes in a bidirectional gene pair.[14] A bidirectional gene pair refers to two adjacent genes coded on opposite strands, with their 5 ends oriented toward one another.[15] The two genes are often functionally related, and modification of their shared promoter region allows them to be co-regulated and thus co-expressed.[16] Bidirectional promoters are a common feature of mammalian genomes.[17] About 11% of human genes are bidirectionally paired.[14]. Bidirectionally paired genes in the Gene Ontology database shared at least one database-assigned functional category with their partners 47% of the time.[18] Microarray analysis has shown bidirectionally paired genes to be co-expressed to a higher degree than random genes or neighboring unidirectional genes.[14] Although co-expression does not necessarily indicate co-regulation, methylation of bidirectional promoter regions has been shown to ...
TY - JOUR. T1 - Editorial. T2 - Plant and cell physiologys 2016 online database issue. AU - Ohyanagi, Hajime. AU - Obayashi, Takeshi. AU - Yano, Kentaro. PY - 2016/1/1. Y1 - 2016/1/1. UR - http://www.scopus.com/inward/record.url?scp=84964900062&partnerID=8YFLogxK. UR - http://www.scopus.com/inward/citedby.url?scp=84964900062&partnerID=8YFLogxK. U2 - 10.1093/pcp/pcv205. DO - 10.1093/pcp/pcv205. M3 - Review article. C2 - 26801748. AN - SCOPUS:84964900062. VL - 57. SP - 1. EP - 3. JO - Plant and Cell Physiology. JF - Plant and Cell Physiology. SN - 0032-0781. IS - 1. ER - ...
Update of /cvsroot/gmod/apollo/src/java/apollo/config In directory sc8-pr-cvs2.sourceforge.net:/tmp/cvs-serv30809 Modified Files: FeatureProperty.java TiersIO.java Added Files: ChadoJdbcNameAdapter.java Log Message: ChadoJdbcNameAdapter is based on ParameciumNameAdapter but more generic. This Name Adapter queries the database itself to generate new unique ids. It uses two parameters in the tiers config file : idPrefix : PTET chromosomeFormat : scaffold_(\d+) # To get the chromosome number The ParameciumNameAdapter should disapear... --- NEW FILE: ChadoJdbcNameAdapter.java --- package apollo.config; import org.apache.log4j.*; import apollo.datamodel.*; import apollo.editor.AddTransaction; import apollo.editor.CompoundTransaction; import apollo.editor.Transaction; import apollo.editor.TransactionManager; import apollo.editor.TransactionSubpart; import apollo.editor.UpdateTransaction; import java.io.BufferedReader; import java.io.InputStreamReader; import java.io.InputStream; import ...
Position Title: Research Associate (Senior Postdoc) / Research Associate (Postdoc) Working Title: Plant Ontology Project Coordinator How to Apply: -------------- To review the position description and apply, go to posting #0004322 at http://oregonstate.edu/jobs. When applying, you will be required to electronically submit your application, a cover letter citing your interest in the position and your experience, and a CV/resume including 3 references. Closing date 7/15/09. Position description: --------------------- The Plant Ontology Consortium (www.plantontology.org) is seeking applicants for a full-time position of scientific curator who will coordinate the Consortium s efforts. The Plant Ontology Consortium is a collaboration among researchers at Oregon State University, Cornell University and New York Botanical Garden. The Consortium also collaborates with the curators of many model organism databases including rice, Arabidopsis, maize, grasses, legumes, Solanaceae, bryophytes and plant ...
Bioinformatics is currently faced with very large-scale data sets that lead to computational jobs, especially sequence similarity searches, that can take absurdly long times to run. For example, the National Center for Biotechnology Information (NCBI) Basic Local Alignment Search Tool (BLAST and BLAST+) suite, which is by far the most widely used tool for rapid similarity searching among nucleic acid or amino acid sequences, is highly central processing unit (CPU) intensive. While the BLAST suite of programs perform searches very rapidly, they have the potential to be accelerated. In recent years, distributed computing environments have become more widely accessible and used due to the increasing availability of high-performance computing (HPC) systems. Therefore, simple solutions for data parallelization are needed to expedite BLAST and other sequence analysis tools. However, existing software for parallel sequence similarity searches often requires extensive computational experience and skill ...
Description: Beta cell genomics database provides searches and tools to explore detailed information about genes, transcripts, gene interactions, genomic regions, and beta cell related functional genomics studies. Institution: University of Pennsylvania Contacts: Beta Cell Biology Consortium Home Page: http://genomics.betacell.org/gbco/ ...
Tracks contained in the RefSeq annotation and RefSeq RNA alignment tracks were created at UCSC using data from the NCBI RefSeq project. Data files were downloaded from RefSeq in GFF file format and converted to the genePred and PSL table formats for display in the Genome Browser. Information about the NCBI annotation pipeline can be found here.. The RefSeq Diffs track is generated by UCSC using NCBIs RefSeq RNA alignments.. The UCSC RefSeq Genes track is constructed using the same methods as previous RefSeq Genes tracks. RefSeq RNAs were aligned against the human genome using BLAT. Those with an alignment of less than 15% were discarded. When a single RNA aligned in multiple places, the alignment having the highest base identity was identified. Only alignments having a base identity level within 0.1% of the best and at least 96% base identity with the genomic sequence were kept.. ...