文章： Studying long 16S rDNA sequences with ultrafast- metagenomic sequence classification using exact alignments (Kraken) 2016 （ https://www.ncbi.nlm.nih.gov/pubmed/26812576 ） 文章： A novel semi-supervised algorithm for the taxonomic assignment of metagenomic reads 2016 （ http://www.ncbi.nlm.nih.gov/pubmed/26740458 ） 文章：Improved taxonomic assignment of human intestinal 16S rRNA sequences by a dedicated reference database 2015 （ http://www.ncbi.nlm.nih.gov/pubmed/26651617 ） 文章：Evaluation of four methods of assigning species and genus to medically important bacteria using 16S rRNA gene sequence analysis 2015 （ http://www.ncbi.nlm.nih.gov/pubmed/25786669 ） 文章：Evaluation of the Performances of Ribosomal Database Project (RDP) Classifier for Taxonomic Assignment of 16S rRNA Metabarcoding Sequences Generated from Illumina-Solexa NGS 2015 （ http://www.ncbi.nlm.nih.gov/pubmed/25653722 ） 文章：16S classifier: a tool for fast and accurate taxonomic classification of 16S rRNA hypervariable regions in metagenomic datasets 2015 （ http://www.ncbi.nlm.nih.gov/pubmed/25646627 ） 文章：CLUSTOM: a novel method for clustering 16S rRNA next generation sequences by overlap minimization 2013 （ http://www.ncbi.nlm.nih.gov/pubmed/23650520 ）
Databases for psychologists Subject specific 1 DATABASES FROM the APA 1.1 PsycBOOKS o a full-text database of books and chapters o produced by the American Psychological Association. o psychology o (1950–2002) 1.2 PsycINFO The major, most comprehensive database o Content: psychology and psychological aspects ofrelated disciplines. including psychiatry, nursing, business, education, socialscience, neuroscience, law, medicine, social work , medicine,psychiatry, nursing, sociology, pharmacology, physiology and linguistics o Coverage : journalarticles, book chapters, dissertations and technical reports o Date range : (1806-present) o Updates : monthly o producedby the American Psychological Association 1.3 PsycArticles o Content: general psychology andspecialized basic, applied, clinical, and theoretical research in psychology. o Coverage : journalarticles o Date range : ( 1988 -present) o Updates : o publishedby the American Psychological Association, the APA Educational PublishingFoundation, the Canadian Psychological Association, and Hogrefe PublishingGroup 1.4 PsycCritiques . o Content: relevantto psychology o Coverage : Reviews of books, films and software o Date range : ( 1988 -present) o Updates : o producedby the American Psychological Association 1.5 PsycTests (APA) psychological tests and measures as well as arich source of structured information about the tests 1.6 PsycBITE evidence-based empirical reports on the effectiveness ofnon-pharmacological interventions for the psychological consequences ofacquired brain impairment. · 1.7 PsycEXTRA A grey literature database, is a companion to the scholarlyPsycINFO database. Most of the content was written for professionals anddisseminated outside of peer-reviewed journals (newsletters, magazines,newspapers et al.,) 1.8 PsycTHERAPY is a database of streaming psychotherapydemonstrations featuring some of the most renowned therapists in North Americaworking with participants on a host of therapeutic topics. PsycTHERAPY providesover 300 hours of video content. More than 200 different topics and 65therapeutic approaches, demonstrated by 95 therapists, and covered throughouthundreds of filmed therapy demonstrations. 2 GENERAL /Multidisplinces 2.1 Scopus alarge multidisciplinary database , abstracting database o Content: Chemistry, Physics, Mathematics andEngineering ; Life and Health Sciences ; Social Sciences, Psychology andEconomics ; Biological, Agricultural and Environmental Sciences o Coverage : reviewed journals, quality web resources and patents , Articles, books, reports, and patents o Date range : 1995-present . o Updates : o NOTES: Includes citationfeatures and tools to analyze authors and journals; set up citation alterts;track authors and analyse journal performance 2.2 Web of Science (Web ofKnowledge) a large multidisciplinary database o Content: science,social science, engineering, and art humanities o Coverage : Bibliographicinformation and cited references o Date range : 1900 for the Sciences, 1956 forSocial Sciences and 1975 for Arts Humanities. o Updates : weekly o NOTES: ScienceCitation Index and Social Sciences Citation Index (through the Web of Science) o Good for contextualizing research. 2.3 Google Scholar o Content: scholarly literature from avariety of disciplines o Coverage : peer-reviewedjournal articles, theses, books, preprints, abstracts and technical reports. o Date range : o Updates : o Good for finding psychology articles, thoughhistorical coverage weaker than PsycINFO 2.4 ScienceDirect o Content: Informationsource for scientific, technical, and medical research, a broad range ofsubjects iin the social sciences and humanities, health sciences, lifesciences, and physical sciences and engineering o Coverage : peer-reviewedjournal articles, books, and reference works. o Date range : (1950-present) o Updates : o producedby the publisher Elsevier. Subsidiary publishers include Academic Press, Cell Press,Pergamon, Mosby, and Saunders journals. 2.5 Wiley Online Library WileyOnline Library . o Fulltext scientificelectronic journals published by Wiley o Journals articles andthousands of e-books (including some encyclopaedias) in almost all subjects. 3 mEDICINE 3.1 PubMed / Medline PubMed is the free web tool to search Medlinecontents the largest biomedical database o Content: psychiatry, biological psychology andbrain sciences. covers the fields of medicine, pre-clinicalsciences including clinical psychology. o Coverage : indexes journals o Date range : ( o Updates : o fromthe National Center for Biotechnology Information (NCBI) at the US NationalLibrary of Medicine (NLM). Medline is the premier database for journal articlesin the health sciences. For further information see the Database Guide toPubMed. Contains internationalliterature on biomedicine, including the allied health fields and thebiological and physical sciences, humanities and information science as theyrelate to medicine and health care. . 3.2 Embase an internationalpharmacological and biomedical database indexing medical and drug informationfrom 70 countries. the Europeanequivalent of Medline which indexes journals Comprehensivepharmacological and biomedical database drug information 3.3 PILOTS Database Index to the literature on post-traumatic stress disorder. (1871-present) for articles on post-traumatic stress disorderand other mental-health consequences of traumatic events. 3.4 Cochrane Library · provide access tosystematic reviews on a wide range of topics related to health care. evidence-based clinical practice · A source of up-to-date information on the effects ofinterventions in health care 4 SOCIAL PSYCHOLOGYAND SOCIAL WORK · 4.1 SociologicalAbstracts Bibliographic references and abstracts abstracts of journal articles and citations to book reviews drawn from over 1,700 serials publications covers sociologyand related disciplines in the social and behavioral sciences · SocialWork Abstracts Citations and abstracts of articles 5 EDUCATION · 5.1 EducationFull-Text Citations, abstracts, and full text articles in education periodicals and otherpublications. Education Full Text Indexes and abstracts articles ofEnglish-language periodicals and books on education from 1983 on. Abstractingcoverage begins with January 1994. Full-text coverage begins in January 1996and is available for over half the 600 journals indexed. · 5.2 ERIC (Educational ResourcesInformation Center) Sponsored by the U.S. Department of Education, indexes articles, governmentdocuments, dissertations, etc., in education. British Education Index and ERIC , for information in the field of education For topics that overlap with education, ERIC canbe helpful. (1966-present) References journal articles and unpublished documents on educational theory andpractice. Indexes research on education policy and practice in developmentcontexts. · 5.3 EducationResearch Complete Offers the world's largest and most complete collection of full text educationjournals. 6 ASSESSMENT TOOLS 6.1 MentalMeasurements Yearbook Descriptions and critical reviews and of over 2,000 psychological tests andmeasures. A comprehensive guide to over 3,000contemporary testing instruments Mental MeasurementsYearbook, produced by the Buros Institute, contains full text information aboutand reviews of all English-language standardized tests covering educationalskills, personality, vocational aptitude, psychology, and related areas as includedin the printed Mental Measurements Yearbooks. Contains information and reviews of all english language standardized test. Covers educational skills, vocationalaptitude, psychology and. personality, aptitude, neuropsychology, achievement and intelligence,etc. 6.2 HaPI(Health and Psychosocial Instruments) . A databasedesign to help identify measurement tests used in health, psychosocialsciences, organizational behavior, and library and information science;provides sources, abstract, and reviewer(s) when applicable. 7 Psychoanalysis 7.1 PEP-Web(Psychoanalytic Electronic Publishing) Full text from thearchive of psychoanalytic literature (books and journals) 1871 - 2006 a digital archive of many major works of psychoanalysis. Itincludes the complete works of Sigmund Freud, backfiles 8 COGNITION · 8.1 Linguistics and Language Behavior Abstracts (LLBA) Use this database for language-related issues in psychology. 8.2 CogNet Includes full-text ofbooks and other resources. Use the CogNet Library to browse journals and books from MIT Pressin the cognitive sciences. A collection ofreference works, journals, books and conference proceedings. 9 ORGANISATIONAL ABI/INform Complete Access references and full text articles from leading business databases and newspapers published since 1971. It is offered on the proquest site where you can cross search it with other business databases including Asian Business, Proquest entrepreneurship. Business Source Complete Leading business research database which provides access to the text of several thousand journals covering business, management, human resources, finance and market research topics. Find articles on the media industry. Emerald is a database of references to articles taken from around 450 top management and business journals. The coverage is international but with a UK bias. The database covers the years from 1989 onwards and is updated monthly. Business Source Premier via Ebsco (1922 to present) One of the largest databases covering business, industry and management. Thisis an excellent source organisational psychology and coaching psychology. · Applied Social Sciences Index andAbstracts · Humanities and Social Sciences IndexRetrospective · IBSS (International Bibliography ofthe Social Sciences the contents of books and journals Content: broad range of social science topics, particularly strong on Anthropology, Economics, Politics Sociology. Abstracts and references to journal articles, book reviews and chapters from edited books. Date range : 1951 to present Updates : weekly · International Encyclopedia of Social and BehavorialSciences ProQuest Research Library ( Michigan University ) Indexes over2,300 journals and magazines covering all fields and topics, academic andpopular, beginning as early as 1971. ....................
This web portal was developed by the Structural Engineering Section of the Aristotle University of Thessaloniki (AUTh) in order to illustrate the activities in the Earthquake Engineering. It aims to disseminate the science of Earthquake Engineering and provide with current and reliable information about related affairs. http://www.quakeportal.eu/index.html
Genomic sequence variation 1000 Genomes Project http://www.1000genomes.org/ Data collection and a catalog of human variation dbSNP http://www.ncbi.nlm.nih.gov/projects/SNP/ A catalog ofSNPs and short indels dbVar and Database of Genomic Variants http://www.ncbi.nlm.nih.gov/dbvar/ http://projects.tcag.ca/variation/ A catalog of structural variants Online Mendelian Inheritance in Man http://www.omim.org/about OMIM is a comprehensive, authoritative compendium of human genes and genetic phenotypes that is freely available and updated daily. The full-text, referenced overviews in OMIM contain information on all known mendelian disorders and over 12,000 genes. OMIM focuses on the relationship between phenotype and genotype. It is updated daily, and the entries contain copious links to other genetics resources. Molecular function Encyclopedia Of DNA Elements (ENCODE) Project http://www.genome.gov/10005107 http://encodeproject.org/ Data collection, integrative analysis, and a comprehensive catalog of all sequence-based functional elements Epigenomics (NIH Common Fund) http://www.roadmapepigenomics.org/ https://commonfund.nih.gov/epigenomics/ Data collection, integrative analysis and a resource of human epigenomic data International Human Epigenome Consortium (IHEC) http://www.ihec-epigenomes.org/ Data collection and reference maps of human epigenomes for key cellular states relevant to health and diseases BLUEPRINT Epigenome http://www.blueprint-epigenome.eu/ http://www.nature.com/nbt/journal/v30/n3/full/nbt.2153.html Data collection on the epigenome of blood cells Human BodyMap Viewable with Ensemble ( http://www.ensembl.org/index.html ) or the Integrated Genomics Viewer ( http://www.broadinstitute.org/igv/ ) Gene expression database from Illumina, from RNA-seq data Cancer CellLine Encyclopedia (CCLE) http://www.broadinstitute.org/ccle/home Array based expression data, CNV, mutations, perturbations over huge collection of cell lines FANTOM5 Project http://fantom.gsc.riken.jp/ Large collection of CAGE based expression data across multiple species (time-series and perturbations) Array Express http://www.ebi.ac.uk/arrayexpress/ Database of gene expression experiments Gene Expression Atlas http://www.ebi.ac.uk/gxa/ Database supporting queries of condition-specific gene expression on a curated subset of the Array Express Archive. GNF Gene Expression Atlas Viewable at BioGPS ( http://biogps.org/#goto=welcome ) GNF (Genomics Institute of the Novartis Research Foundation) human and mouse gene expression array data. The Human Protein Atlas http://www.proteinatlas.org/ Protein expression profiles based on immunohistochemistry for a large number of human tissues, cancers and cell lines, subcellular localization, transcript expression levels UniProt http://www.uniprot.org/ A comprehensive, freely accessible database of protein sequence and functional information InterPro http://www.ebi.ac.uk/interpro/ An integrated database of protein classification, functional domains, and annotation (including GO terms). Protein Capture Reagents Initiative http://commonfund.nih.gov/proteincapture/ Resource generation: renewable, monoclonal antibodies and other reagents that target the full range of proteins Knockout Mouse Program (KOMP) http://www.nih.gov/science/models/mouse/knockout/index.html Resource generation: create knockout strains for all mouse genes, Trans-NIH project Library of Integrated Network-based Cellular Signatures (LINCS) https://commonfund.nih.gov/LINCS/ Data collection and analysis of molecular signatures that describe how different types of cells respond to a variety of perturbing agents Molecular Libraries Program (MLP) https://commonfund.nih.gov/molecularlibraries/index.aspx Access to the large-scale screening capacity necessary to identify small molecules that can be optimized as chemical probes to study the functions of genes, cells, and biochemical pathways in health and disease Allen Brain Atlas http://www.brain-map.org/ Data collection and an online public resources integrating extensive gene expression and neuroanatomical data for human and mouse, including variation of mosue gene expression by strain. The Human Connectome Project http://www.humanconnectomeproject.org/ Data collection and integration to create a complete map of the structural and functional neural connections, within and across individuals Geuvadis RNA sequencing project of 1000 Genomes samples http://www.geuvadis.org/web/geuvadis mRNA and small RNA sequencing on 465 lymphoblastoid cell line (LCL) samples from 5 populations of the 1000 Genomes Project: the CEPH (CEU), Finns (FIN), British (GBR), Toscani (TSI) and Yoruba (YRI). Phenotypes and disease The Cancer Genome Atlas (TCGA) http://cancergenome.nih.gov/ Data collection and a data repository, including cancer genome sequence data International Cancer Genome Consortium (ICGC) http://www.icgc.org/ Data collection and a data repository for a comprehensive description of genomic, transcriptomic and epigenomic changes of cancer Genotype-Tissue Expression (GTEx) Project https://commonfund.nih.gov/GTEx/ Data collection, data repository, and sample bank for human gene expression and regulation in multiple tissues, compared to genetic variation Knockout Mouse Phenotyping Program (KOMP2) https://commonfund.nih.gov/KOMP2/ Data collection for standardized phenotyping of a genome-wide collection of mouse knockouts Database of Genotypes and Phenotypes (dbGaP) http://www.ncbi.nlm.nih.gov/gap Data repository for results from studies investigating the interaction of genotype and phenotype NHGRI Catalog of Published GWAS http://www.genome.gov/gwastudies/ Public catalog of published Genome-Wide Association Studies Clinical Genomic Database http://research.nhgri.nih.gov/CGD/ A manually curated database of conditions with known genetic causes, focusing on medically significant genetic data with available interventions. NHGRI's Breast Cancer information core http://research.nhgri.nih.gov/bic/ Breast Cancer Mutation database ClinVar http://www.ncbi.nlm.nih.gov/clinvar/ ClinVar is designed to provide a freely accessible, public archive of reports of the relationships among human variations and phenotypes, with supporting evidence. ClinVar collects reports of variants found in patient samples, assertions made regarding their clinical significance, information about the submitter, and other supporting data. The alleles described in submissions are mapped to reference sequences, and reported according to the HGVS standard. ClinVar then presents the data for interactive users as well as those wishing to use ClinVar in daily workflows and other local applications. ClinVar works in collaboration with interested organizations to meet the needs of the medical genetics community as efficiently and effectively as possible. Human Gene Mutation Database (HGMD) http://www.hgmd.cf.ac.uk/ac/ The Human Gene Mutation Database (HGMD®) represents an attempt to collate known (published) gene lesions responsible for human inherited disease NHLBI Exome Sequencing Project (ESP) Exome Variant Server http://evs.gs.washington.edu/EVS/ The goal of the NHLBI GO Exome Sequencing Project (ESP) is to discover novel genes and mechanisms contributing to heart, lung and blood disorders by pioneering the application of next-generation sequencing of the protein coding regions of the human genome across diverse, richly-phenotyped populations and to share these datasets and findings with the scientific community to extend and enrich the diagnosis, management and treatment of heart, lung and blood disorders. Genetics Home Reference http://ghr.nlm.nih.gov/ Genetics Home Reference is the National Library of Medicine's web site for consumer information about genetic conditions and the genes or chromosomes related to those conditions. GeneReviews http://www.ncbi.nlm.nih.gov/books/NBK1116/ GeneReviews are expert-authored, peer-reviewed disease descriptions presented in a standardized format and focused on clinically relevant and medically actionable information on the diagnosis, management, and genetic counseling of patients and families with specific inherited conditions. Data integration UCSC Genome Bioinformatics http://genome.ucsc.edu/ Genome databases displayed through a genome browser for vertebrates, other eukaryotes, and prokaryotes, including sequence conservation, transcript maps and expression, functional annotation, genetic variation, and human disease information Ensembl http://www.ensembl.org/index.html Genome databases displayed through a genome browser for vertebrates and other eukaryotic species, including sequence conservation, transcript maps and expression, functional annotation, genetic variation, and human disease information Reactome http://www.reactome.org/ReactomeGWT/entrypoint.html Pathway database: open-source, open access, manually curated and peer-reviewed Molecular Signatures Database (MSigDB) http://www.broadinstitute.org/gsea/msigdb/index.jsp MSigDB is a collection of annotated gene sets for use with Gene Set Enrichment (GSEA) software KEGG: Kyoto Encyclopedia of Genes and Genomes http://www.genome.jp/kegg/ Database of pathways, diseases, drugs BIOCARTA http://www.biocarta.com/ Pathway analysis resource Genomatix http://www.genomatix.de/ Proprietary genome annotation and pathway analysis software GOLD:Genomes Online Database http://www.genomesonline.org/cgi-bin/GOLD/index.cgi Information regarding genome and metagenome sequencing projects, and their associated metadata, around the world Model organism databases (selected examples) Mouse Genome Informatics http://www.informatics.jax.org/ Includes genotypes with phenotype annotations, human diseases with one or more mouse models, expression assays and images, pathways, and refSNPs, Rat Genome Database (RGD) http://rgd.mcw.edu/ Repository of rat genetic and genomic data, as well as mapping, strain, and physiological information FlyBase http://flybase.org/ A Database of Drosophila Genes Genomes WormBase http://www.wormbase.org/ The genetics, genomics and biology of C. elegans and related nematodes The Zebrafish Model Organism Database (ZFIN) http://zfin.org/ Support integrated zebrafish genetic, genomic and developmental information XenBase http://www.xenbase.org/common/ Xenopus laevis and Xenopus tropicalis biology and genomics resource Saccharomyces Genome Database (SGD) http://www.yeastgenome.org/ Integrated biological information for budding yeast, along with search and analysis tools
General plant genomics resources Phytozome : comparative genomics of plants. PlantGDB : plant genome database NCBI Plant Genomes Central : Links to NCBI resources for plant genomics. M unich I nformation System for P rotein S equences. Arizona Genome Institute Clemson University Genomics Institute ChromatinDB Carbohydrate active enzymes Cell Wall Genomics Primer3 primer design tool Gene Ontology Plant Ontology MicroRNAs GRIN: Germplasm Resources Information Network The Virtual Plant at NYU. Agricola , a Bibliographic database of citations to the agricultural literature Phytome supports phylogenetic and functional analyses of predicted protein sequences across plants. OrthologID provide phylogenetic analysis of Arabidopsis, rice, Populus, and Chlamydomonas genes PlantTribes provides OrthoMCL clustering of plant proteins, developed as part of the Floral Genome Project. Gene expression PLEXdb (Plant Expression Database) is a unified public resource for gene expression for plants and plant pathogens. http://www.plexdb.org ArrayExpress is a public repository for transcriptomics and related data at EBI. http://www.ebi.ac.uk/arrayexpress/ http://signal.salk.edu MPSS (Massively Parallel Signature Sequencing) datasets for Arabidopsis, rice, and grape, at U Delaware: http://mpss.udel.edu Botany Array Resources (BAR). http://www.bar.utoronto.ca GeneVestigator is a reference expression database and meta-analysis system for studying the expression and regulation of genes by summarizing information from hundreds of microarray experiments into easily interpretable results. https://www.genevestigator.ethz.ch/ Gene Indices and EST resources The Gene Index project (formerly, TIGR gene indices) http://compbio.dfci.harvard.edu/tgi/ PlantTA: Plant Transcript Assemblies http://plantta.tigr.org HarvEST: EST analysis resources for barley, Brachypodium, Citrus, Coffea, cowpea, soybean, rice, and wheat. http://harvest.ucr.edu/ Arabidopsis resources TAIR (The Arabidopsis Information Resource) http://www.arabidopsis.org; http:// www.tair.org) Salk Institute Genome Analysis Laboratory. http://signal.salk.edu/ AtGenExpress, a multinational effort to uncover the transcriptome of A. thaliana. http://www.arabidopsis.org/info/expression/ATGenExpress.jsp Botany Array Resources (BAR). http://www.bar.utoronto.ca Arabidopsis small RNA project http://asrp.cgrb.oregonstate.edu/ Arabidopsis MPSS data at U Delaware. http://mpss.udel.edu/at/ Grasses and cereals: rice, maize, sorghum, wheat, barley, Brachypodium, et al. Gramene: A Resource for Comparative Grass Genomics http://www.gramene.org Grain Genes 2.0: A Database for Triticeae and Avena. http://wheat.pw.usda.gov Sorghum genome sequence at Phytozome: www.phytozome.net/sorghum MaizeDB ( www.maizedb.org) Maize genome sequence at CSHL ( www.maizesequence.org) Oryza map; maize FPC map. Arizona Genome Institute. http://www.genome.arizona.edu/ Maize and sorghum assembled gene islands (Methyl-filtration) http://magi.plantgenomics.iastate.edu/ Brachpodium genome database www.brachybase.org www.modelcrop.org Brachypodium genome database at Phytozome: www.phytozome.net/brachypodium Marker Assisted Selection for wheat. http://maswheat.ucdavis.edu PanZea: Molecular and Functional Diversity of the Maize Genome www.panzea.org Maize Oligonucleotide Array Project http://www.maizearray.org/ Beijing Genome Institute Rice Information System (RISE): http://rise.genomics.org.cn/rice Rice Annotation Project Database (RAP-DB) http://rapdb.dna.affrc.go.jp/ TIGR rice genome annotation and resources. www.tigr.org/tdb/rice International Rice Research Institute http://www.irri.org/ Rice MPSS data at U. Delaware http://mpss.udel.edu/rice/ Legumes: Soybean, Medicago, Lotus, et al. Soybean genome sequence at Phytozome: www.phytozome.net/soybean Medicago truncatula: a model for legume research ( www.medicago.org). Links to genome sequencing, functional genomics, comparative genomics Legume Information System http://www.comparative-legumes.org Soybase and the soybean breeders toolbox. http://soybase.agron.iastate.edu/ Lotus japonicus genome project at Kazusa http://www.kazusa.or.jp/lotus/ BeanGenes: a Phaseolus/Vigra database, including links to other useful sites. http://beangenes.cws.ndsu.nodak.edu/ SoyMap: An integrated map of soybean for resolution and dissection of multiple genome duplication events. http://www.soymap.org/ Soybean genome map at Southern Illinois University http://soybeangenome.siu.edu/ Forest tree genomics Populus trichocarpa genome at Phytozome. http://www.phytozome.net/poplar Dendrome is a collection of forest tree genome databases and other forest genetic information resources for the international forest genetics community. Dendrome is part of a larger collaborative effort to construct genome databases for major crop and forest species. http://dendrome.ucdavis.edu Eucalyptus TreeGenes EST database: http://treegenes.ucdavis.edu PopulusDB expression database at Umea Sweden. http://www.populus.db.umu.se/ Genomic tool development for the chestnut tree and other Fagaceae http://www.fagaceae.org/ Other plant species International Grape Genome Project http://www.vitaceae.org Grape genome sequence at Genoscope: http://www.genoscope.cns.fr/externe/English/Projets/Projet_ML/index.html Grape genome at Phytozome: www.phytozome.net/grape Grape MPSS data at U Delaware http://mpss.udel.edu/grape/ Genome Database of Rosaceae ( www.bioinfo.wsu.edu/gdr) Tomato genome project SOL Genomics Network is a clade-oriented database containing genomic, genetic and taxonomic information for species in the families Solanaceae (e.g., tomato, potato, eggplant, pepper, petunia) and Rubiaceae (coffee). Genomic information is presented in a comparative format and tied to the Arabidopsis genome. www.sgn.cornell.edu Compositae genomics project http://compgenomics.ucdavis.edu/ Mimulus, columbine The Physcomitrella patens computational biology resources http://www.cosmoss.org/ Physcomitrella patens genome at Phytozome: www.phytozome.net/physcomitrella Selaginella moellendorfii genome sequence at Phytozome: www.phytozome.net/selaginella Castor Bean Genome Database at TIGR. http://castorbean.tigr.org/ Papaya Genome Project at University of Hawaii http://cgpbr.hawaii.edu/papaya/ Brassica rapa genome Cotton genome database contains genomic, genetic and taxonomic information for cotton at USDA-ARS College Station TX. http://cottondb.org/ Cotton Genome Database at the Plant Genome Mapping Laboratory, University of Georgia http://www.plantgenome.uga.edu/cotton/StartFrame.htm International Cotton Genome Initiative http://icgi.tamu.edu/ Plant pathogen genomics Pseudomonas syringae comparative genomics www.pseudomonas-syringae.org
英文 编辑本段 回目录 EuroPineDB: a high-coverage web database for maritime pine transcriptome Fernandez-Pozo, N. Canales, J. Guerrero-Fernandez, D. Villalobos, D. P. ... Claros, M. G. BACKGROUND: Pinus pinaster is an economically and ecologically important species that is becoming a woody gymnosperm model. Its enormous genome size makes whole-genome sequencing approaches are hard to apply. Therefore, the expressed portion of the genome has to be characterised and the results and annotations have to be stored in dedicated databases. DESCRIPTION: EuroPineDB is the largest sequence collection available for a single pine species, Pinus pinaster (maritime pine), since it comprises 951 641 raw sequence reads obtained from non-normalised cDNA libraries and high-throughput sequencing from adult (xylem, phloem, roots, stem, needles, cones, strobili) and embryonic (germinated embryos, buds, callus) maritime pine tissues. Using open-source tools, sequences were optimally pre-processed, assembled, and extensively annotated (GO, EC and KEGG terms, descriptions, SNPs, SSRs, ORFs and InterPro codes). As a result, a 10.5x P. pinaster genome was covered and assembled in 55 322 UniGenes. A total of 32 919 (59.5%) of P. pinaster UniGenes were annotated with at least one description, revealing at least 18 466 different genes. The complete database, which is designed to be scalable, maintainable, and expandable, is freely available at: http://www.scbi.uma.es/pindb/. It can be retrieved by gene libraries, pine species, annotations, UniGenes and microarrays (i.e., the sequences are distributed in two-colour microarrays; this is the only conifer database that provides this information) and will be periodically updated. Small assemblies can be viewed using a dedicated visualisation tool that connects them with SNPs. Any sequence or annotation set shown on-screen can be downloaded. Retrieval mechanisms for sequences and gene annotations are provided. CONCLUSIONS: The EuroPineDB with its integrated information can be used to reveal new knowledge, offers an easy-to-use collection of information to directly support experimental work (including microarray hybridisation), and provides deeper knowledge on the maritime pine transcriptome. 中文 编辑本段 回目录 EuroPineDB：南欧海松转录组的一个高覆盖网络数据库 背景： 南欧海松 是一种经济上和生态上重要的物种，它正在成为一种木质裸子植物模型。它的巨大的基因组大小使得全基因组测序方法难以应用。因此，基因组的表达部分不得不被描述，并且结果和注释不得不存储在专门的数据库中。描述：EuroPineDB是可用于单个松树物种，南欧海松（maritime pine）的最大的序列收集，因为它由从非均一化cDNA文库和来自成熟（木质部、筛部、根部、茎、针叶、松果、球果）及胚胎（胚芽、萌芽、愈合组织）南欧海松组织的951,641条原始序列片段组成。使用开源工具，序列被最佳地预处理，装配并广泛注释（GO、EC和KEGG术语，描述，SNPs、SSRs、ORFs和InterPro代码）。结果，一个10.5倍的南欧海松基因组被覆盖，并装配了55,322个UniGenes。总共32,919（59.5%）个南欧海松UniGenes用至少一个描述被注释，揭示了至少18,466个不同的基因。被设计为可扩展、可维护且可扩充的完整数据库可以在http://www.scbi.uma.es/pindb/免费获得。它能够通过基因文库、松树物种、注释、UniGenes和微阵列（即，序列被分布在双色微阵列上；这是惟一的提供了这种信息的松柏植物数据库）进行检索，并将被定期更新。小的装配能够使用连接了它们和SNPs的专门可视化工具进行查看。显示在屏幕上的任何序列或注释集合能够被下载。提供了序列和基因注释的检索机制。结论：EuroPineDB连同它的整合信息能够被用于揭示新知识，提供了一个易用的信息收集，以直接支持实验工作（包括微阵列杂交），并提供了有关南欧海松转录组的更深知识。claros@uma.es。 doi 编辑本段 回目录 10.1186/1471-2164-12-366 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3152544/pdf/1471-2164-12-366.pdf
What, where and how many? Combining object detectors and CRFs Ladick, L'Ubor (Oxford Brookes University, United Kingdom); Sturgess, Paul ; Alahari, Karteek ; Russell, Chris ; Torr, Philip H. S. Source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , v 6314 LNCS, n PART 4, p 424-437, 2010, Computer Vision, ECCV 2010 - 11th European Conference on Computer Vision, Proceedings Database: Compendex Abstract - Detailed Generalization : This paper define a globe energey function for the model, which combines results from sliding window detectors, and low-level pixel-based unary and pairwis relations. motivation: try to recognition objects, find their location and spatial extent, andalso provide the number ofinstances of objects. This work can be viewed as an integration of object class segmentation methods ,which fail to distinguish between adjacent instances of objects of the same class, and object detection approaches, which do not provide information about background classes. 创新点 ：定义了一个全局能量函数 = object detector + pairwise + unary, 并能够高效求解。 pairwise: 利用mid-level cues之间的关系，如superpixels之间的关系 unary:利用low-level cues, 即pixel-based object detector: 能量函数： ,其中后一项即为检测的能量项，前一项为基于像素或超像素的单位置或双位置势函数。 其中f为假设函数，g为惩罚函数 最好检测项归结为： 该函数形式具有Robust ，因此可以用 和 算法高效地实现。 实验结果：第二列为不加检测的，第三类为加了检测的。表现在加了检测的对象轮廓更加完整
Prerequisites To work with this tutorial, we must have Python language, SQLite database, pysqlite language binding and the sqlite3 command line tool installed on the system. If we have Python 2.5+ then we only need to install the sqlite3 command line tool. Both the SQLite library and the pysqlite language binding are built into the Python languge. $ python Python 2.6.5 (r265:79063, Apr 16 2010, 13:09:56) on linux2 Type "help", "copyright", "credits" or "license" for more information. import sqlite3 sqlite3.version '2.4.1' sqlite3.sqlite_version '3.6.22' In the shell, we launch the Python interactive interpreter. We can see the Python version. In our case it is Python 2.6.5. The sqlite.version is the version of the pysqlite (2.4.1), which is the binding of the Python language to the SQLite database. The sqlite3.sqlite_version gives us the version of the SQLite database library. In our case it is 3.6.22. Now we are going to use the sqlite3 command line tool to create a new database. $ sqlite3 test.db SQLite version 3.6.22 Enter ".help" for instructions Enter SQL statements terminated with a ";" We provide a parameter to the sqlite3 tool. A "test.db" is a database name. It is a file on our disk. If it is present, it is opened. If not, it is created. sqlite .tables sqlite .exit $ ls test.db The .tables command gives a list of tables in the test.db database. There are currently no tables. The .exit command terminates the interactive session of the sqlite3 command line tool. The ls Unix command shows the contents of the current working directory. We can see the test.db file. All data will be stored in this single file. Version In the first code example, we will get the version of the SQLite database. #!/usr/bin/python # -*- coding: utf-8 -*- import sqlite3 as lite import sys con = None try: con = lite.connect('test.db') cur = con.cursor() cur.execute('SELECT SQLITE_VERSION()') data = cur.fetchone() print "SQLite version: %s" % data except lite.Error, e: print "Error %s:" % e.args sys.exit(1) finally: if con: con.close() In the above Python script we connect to the previously created test.db database. We execute an SQL statement which returns the version of the SQLite database. import sqlite3 as lite The sqlite3 module is used to work with the SQLite database. con = None We initialize the con variable to None. In case we could not create a connection to the database (for example the disk is full), we would not have a connection variable defined. This would lead to an error in the finally clause. con = lite.connect('test.db') Here we connect to the test.db database. The connect() method returns a connection object. cur = con.cursor() cur.execute('SELECT SQLITE_VERSION()') From the connection, we get the cursor object. The cursor is used to traverse the records from the result set. We call the execute() method of the cursor and execute the SQL statement. data = cur.fetchone() We fetch the data. Since we retrieve only one record, we call the fetchone() method. print "SQLite version: %s" % data We print the data that we have retrieved to the console. except lite.Error, e: print "Error %s:" % e.args sys.exit(1) In case of an exception, we print an error message and exit the script with an error code 1. finally: if con: con.close() In the final step, we release the resources. In the second example, we again get the version of the SQLite database. This time we will use the with keyword. #!/usr/bin/python # -*- coding: utf-8 -*- import sqlite3 as lite import sys con = lite.connect('test.db') with con: cur = con.cursor() cur.execute('SELECT SQLITE_VERSION()') data = cur.fetchone() print "SQLite version: %s" % data The script returns the current version of the SQLite database. With the use of the with keyword. The code is more compact. with con: With the with keyword, the Python interpreter automatically releases the resources. It also provides error handling. Inserting data We will create a Cars table and insert several rows to it. #!/usr/bin/python # -*- coding: utf-8 -*- import sqlite3 as lite import sys con = lite.connect('test.db') with con: cur = con.cursor() cur.execute("CREATE TABLE Cars(Id INT, Name TEXT, Price INT)") cur.execute("INSERT INTO Cars VALUES(1,'Audi',52642)") cur.execute("INSERT INTO Cars VALUES(2,'Mercedes',57127)") cur.execute("INSERT INTO Cars VALUES(3,'Skoda',9000)") cur.execute("INSERT INTO Cars VALUES(4,'Volvo',29000)") cur.execute("INSERT INTO Cars VALUES(5,'Bentley',350000)") cur.execute("INSERT INTO Cars VALUES(6,'Citroen',21000)") cur.execute("INSERT INTO Cars VALUES(7,'Hummer',41400)") cur.execute("INSERT INTO Cars VALUES(8,'Volkswagen',21600)") The above script creates a Cars table and inserts 8 rows into the table. cur.execute("CREATE TABLE Cars(Id INT, Name TEXT, Price INT)") This SQL statement creates a new Cars table. The table has three columns. cur.execute("INSERT INTO Cars VALUES(1,'Audi',52642)") cur.execute("INSERT INTO Cars VALUES(2,'Mercedes',57127)") These two lines insert two cars into the table. Using the with keyword, the changes are automatically committed. Otherwise, we would have to commit them manually. sqlite .mode column sqlite .headers on We verify the written data with the sqlite3 tool. First we modify the way the data is displayed in the console. We use the column mode and turn on the headers. sqlite SELECT * FROM Cars; Id Name Price ---------- ---------- ---------- 1 Audi 52642 2 Mercedes 57127 3 Skoda 9000 4 Volvo 29000 5 Bentley 350000 6 Citroen 21000 7 Hummer 41400 8 Volkswagen 21600 This is the data that we have written to the Cars table. We are going to create the same table. This time using the convenience executemany() method. #!/usr/bin/python # -*- coding: utf-8 -*- import sqlite3 as lite import sys cars = ( (1, 'Audi', 52642), (2, 'Mercedes', 57127), (3, 'Skoda', 9000), (4, 'Volvo', 29000), (5, 'Bentley', 350000), (6, 'Hummer', 41400), (7, 'Volkswagen', 21600) ) con = lite.connect('test.db') with con: cur = con.cursor() cur.execute("DROP TABLE IF EXISTS Cars") cur.execute("CREATE TABLE Cars(Id INT, Name TEXT, Price INT)") cur.executemany("INSERT INTO Cars VALUES(?, ?, ?)", cars) This script drops a Cars table if it exists and (re)creates it. cur.execute("DROP TABLE IF EXISTS Cars") cur.execute("CREATE TABLE Cars(Id INT, Name TEXT, Price INT)") The first SQL statement drops the Cars table, if it exists. The second SQL statement creates the Cars table. cur.executemany("INSERT INTO Cars VALUES(?, ?, ?)", cars) We insert 8 rows into the table using the convenience executemany() method. The first parameter of this method is a parameterized SQL statement. The second parameter is the data, in the form of tuple of tuples. We provide another way to create our Cars table. We commit the changes manually and provide our own error handling. #!/usr/bin/python # -*- coding: utf-8 -*- import sqlite3 as lite import sys try: con = lite.connect('test.db') cur = con.cursor() cur.executescript(""" DROP TABLE IF EXISTS Cars; CREATE TABLE Cars(Id INT, Name TEXT, Price INT); INSERT INTO Cars VALUES(1,'Audi',52642); INSERT INTO Cars VALUES(2,'Mercedes',57127); INSERT INTO Cars VALUES(3,'Skoda',9000); INSERT INTO Cars VALUES(4,'Volvo',29000); INSERT INTO Cars VALUES(5,'Bentley',350000); INSERT INTO Cars VALUES(6,'Citroen',21000); INSERT INTO Cars VALUES(7,'Hummer',41400); INSERT INTO Cars VALUES(8,'Volkswagen',21600); """) con.commit() except lite.Error, e: if con: con.rollback() print "Error %s:" % e.args sys.exit(1) finally: if con: con.close() In the above script we (re)create the Cars table using the executescript() method. cur.executescript(""" DROP TABLE IF EXISTS Cars; CREATE TABLE Cars(Id INT, Name TEXT, Price INT); INSERT INTO Cars VALUES(1,'Audi',52642); INSERT INTO Cars VALUES(2,'Mercedes',57127); ... The executescript() method allows us to execute the whole SQL code in one step. con.commit() Without the with keyword, the changes must be committed using the commit() method. except lite.Error, e: if con: con.rollback() print "Error %s:" % e.args sys.exit(1) In case of an error, the changes are rolled back and an error message is printed to the terminal. The last inserted row id Sometimes, we need to determine the id of the last inserted row. In Python SQLite, we use the lastrowid attribute of the cursor object. #!/usr/bin/python # -*- coding: utf-8 -*- import sqlite3 as lite import sys con = lite.connect(':memory:') with con: cur = con.cursor() cur.execute("CREATE TABLE Friends(Id INTEGER PRIMARY KEY, Name TEXT);") cur.execute("INSERT INTO Friends(Name) VALUES ('Tom');") cur.execute("INSERT INTO Friends(Name) VALUES ('Rebecca');") cur.execute("INSERT INTO Friends(Name) VALUES ('Jim');") cur.execute("INSERT INTO Friends(Name) VALUES ('Robert');") lid = cur.lastrowid print "The last Id of the inserted row is %d" % lid We create a Friends table in memory. The Id is automatically incremented. cur.execute("CREATE TABLE Friends(Id INTEGER PRIMARY KEY, Name TEXT);") In SQLite, INTEGER PRIMARY KEY column is auto incremented. There is also an AUTOINCREMENT keyword. When used in INTEGER PRIMARY KEY AUTOINCREMENT a slightly different algorithm for Id creation is used. cur.execute("INSERT INTO Friends(Name) VALUES ('Tom');") cur.execute("INSERT INTO Friends(Name) VALUES ('Rebecca');") cur.execute("INSERT INTO Friends(Name) VALUES ('Jim');") cur.execute("INSERT INTO Friends(Name) VALUES ('Robert');") These four SQL statements insert four rows into the Friends table. lid = cur.lastrowid Using the lastrowid we get the last inserted row id. $ ./autoinc.py The last Id of the inserted row is 4 We see the output of the script. Retrieving data Now, that we have inserted some data into the database, we want to get it back. #!/usr/bin/python # -*- coding: utf-8 -*- import sqlite3 as lite import sys con = lite.connect('test.db') with con: cur = con.cursor() cur.execute("SELECT * FROM Cars") rows = cur.fetchall() for row in rows: print row In this example, we retrieve all data from the Cars table. cur.execute("SELECT * FROM Cars") This SQL statement selects all data from the Cars table. rows = cur.fetchall() The fetchall() method gets all records. It returns a result set. Technically, it is a tuple of tuples. Each of the inner tuples represent a row in the table. for row in rows: print row We print the data to the console, row by row. $ ./retrieveall.py (1, u'Audi', 52642) (2, u'Mercedes', 57127) (3, u'Skoda', 9000) (4, u'Volvo', 29000) (5, u'Bentley', 350000) (6, u'Citroen', 21000) (7, u'Hummer', 41400) (8, u'Volkswagen', 21600) This is the output of the example. Returning all data at a time may not be feasible. We can fetch rows one by one. #!/usr/bin/python # -*- coding: utf-8 -*- import sqlite3 as lite import sys con = lite.connect('test.db') with con: cur = con.cursor() cur.execute("SELECT * FROM Cars") while True: row = cur.fetchone() if row == None: break print row , row , row In this script we connect to the database and fetch the rows of the Cars table one by one. while True: We access the data from the while loop. When we read the last row, the loop is terminated. row = cur.fetchone() if row == None: break The fetchone() method returns the next row from the table. If there is no more data left, it returns None. In this case we break the loop. print row , row , row The data is returned in the form of a tuple. Here we select records from the tuple. The first is the Id, the second is the car name and the third is the price of the car. $ ./retrieveonebyone.py 1 Audi 52642 2 Mercedes 57127 3 Skoda 9000 4 Volvo 29000 5 Bentley 350000 6 Citroen 21000 7 Hummer 41400 8 Volkswagen 21600 This is the output of the example. The dictionary cursor The default cursor returns the data in a tuple of tuples. When we use a dictionary cursor, the data is sent in a form of Python dictionaries. This way we can refer to the data by their column names. #!/usr/bin/python # -*- coding: utf-8 -*- import sqlite3 as lite import sys con = lite.connect('test.db') with con: con.row_factory = lite.Row cur = con.cursor() cur.execute("SELECT * FROM Cars") rows = cur.fetchall() for row in rows: print "%s %s %s" % (row , row , row ) In this example, we print the contents of the Cars table using the dictionary cursor. con.row_factory = lite.Row We select a dictionary cursor. Now we can access records by the names of columns. for row in rows: print "%s %s %s" % (row , row , row ) The data is accessed by the column names. Parameterized queries Now we will concern ourselves with parameterized queries. When we use parameterized queries, we use placeholders instead of directly writing the values into the statements. Parameterized queries increase security and performance. The Python sqlite3 module supports two types of placeholders. Question marks and named placeholders. #!/usr/bin/python # -*- coding: utf-8 -*- import sqlite3 as lite import sys uId = 1 uPrice = 62300 con = lite.connect('test.db') with con: cur = con.cursor() cur.execute("UPDATE Cars SET Price=? WHERE Id=?", (uPrice, uId)) con.commit() print "Number of rows updated: %d" % cur.rowcount We update a price of one car. In this code example, we use the question mark placeholders. cur.execute("UPDATE Cars SET Price=? WHERE Id=?", (uPrice, uId)) The question marks (?) are placeholders for values. The values are added to the placeholders. print "Number of rows updated: %d" % cur.rowcount The rowcount property returns the number of updated rows. In our case one row was updated. $ ./prepared.py Number of rows updated: 1 Id Name Price ---------- ---------- ---------- 1 Audi 62300 The price of the car was updated. The second example uses parameterized statements with named placeholders. #!/usr/bin/python # -*- coding: utf-8 -*- import sqlite3 as lite import sys uId = 4 con = lite.connect('test.db') with con: cur = con.cursor() cur.execute("SELECT Name, Price FROM Cars WHERE Id=:Id", {"Id": uId}) con.commit() row = cur.fetchone() print row , row We select a name and a price of a car using named placeholders. cur.execute("SELECT Name, Price FROM Cars WHERE Id=:Id", {"Id": uId}) The named placeholders start with a colon character. Inserting images In this section, we are going to insert an image to the SQLite database. Note that some people argue against putting images into databases. Here we only show how to do it. We do not dwell into technical issues of weather to save images in databases or not. sqlite CREATE TABLE Images(Id INTEGER PRIMARY KEY, Data BLOB); For this example, we create a new table called Images. For the images, we use the BLOB data type, which stands for Binary Large Objects. #!/usr/bin/python # -*- coding: utf-8 -*- import sqlite3 as lite import sys def readImage(): try: fin = open("woman.jpg", "rb") img = fin.read() return img except IOError, e: print "Error %d: %s" % (e.args ,e.args ) sys.exit(1) finally: if fin: fin.close() try: con = lite.connect('test.db') cur = con.cursor() data = readImage() binary = lite.Binary(data) cur.execute("INSERT INTO Images(Data) VALUES (?)", (binary,) ) con.commit() except lite.Error, e: if con: con.rollback() print "Error %s:" % e.args sys.exit(1) finally: if con: con.close() In this script, we read an image from the current working directory and write it into the Images table of the SQLite test.db database. try: fin = open("woman.jpg", "rb") img = fin.read() return img We read binary data from the filesystem. We have a jpg image called woman.jpg. binary = lite.Binary(data) The data is encoded using the SQLite Binary object. cur.execute("INSERT INTO Images(Data) VALUES (?)", (binary,) ) This SQL statement is used to insert the image into the database. Reading images In this section, we are going to perform the reverse operation. We will read an image from the database table. #!/usr/bin/python # -*- coding: utf-8 -*- import sqlite3 as lite import sys def writeImage(data): try: fout = open('woman2.jpg','wb') fout.write(data) except IOError, e: print "Error %d: %s" % (e.args , e.args ) sys.exit(1) finally: if fout: fout.close() try: con = lite.connect('test.db') cur = con.cursor() cur.execute("SELECT Data FROM Images LIMIT 1") data = cur.fetchone() writeImage(data) except lite.Error, e: print "Error %s:" % e.args sys.exit(1) finally: if con: con.close() We read image data from the Images table and write it to another file, which we call woman2.jpg. try: fout = open('woman2.jpg','wb') fout.write(data) We open a binary file in a writing mode. The data from the database is written to the file. cur.execute("SELECT Data FROM Images LIMIT 1") data = cur.fetchone() These two lines select and fetch data from the Images table. We obtain the binary data from the first row. Metadata Metadata is information about the data in the database. Metadata in a SQLite contains information about the tables and columns, in which we store data. Number of rows affected by an SQL statement is a metadata. Number of rows and columns returned in a result set belong to metadata as well. Metadata in SQLite can be obtained using the PRAGMA command. SQLite objects may have attributes, which are metadata. Finally, we can also obtain specific metatada from querying the SQLite system sqlite_master table. #!/usr/bin/python # -*- coding: utf-8 -*- import sqlite3 as lite import sys con = lite.connect('test.db') with con: cur = con.cursor() cur.execute('PRAGMA table_info(Cars)') data = cur.fetchall() for d in data: print d , d , d In this example, we issue the PRAGMA table_info(tableName) command, to get some metadata info about our Cars table. cur.execute('PRAGMA table_info(Cars)') The PRAGMA table_info(tableName) command returns one row for each column in the Cars table. Columns in the result set include the column order number, column name, data type, whether or not the column can be NULL, and the default value for the column. for d in data: print d , d , d From the provided information, we print the column order number, column name and column data type. $ ./colnames1.py 0 Id INT 1 Name TEXT 2 Price INT Output of the example. Next we will print all rows from the Cars table with their column names. #!/usr/bin/python # -*- coding: utf-8 -*- import sqlite3 as lite import sys con = lite.connect('test.db') with con: cur = con.cursor() cur.execute('SELECT * FROM Cars') col_names = for cn in cur.description] rows = cur.fetchall() print "%s %-10s %s" % (col_names , col_names , col_names ) for row in rows: print "%2s %-10s %s" % row We print the contents of the Cars table to the console. Now, we include the names of the columns too. The records are aligned with the column names. col_names = for cn in cur.description] We get the column names from the description property of the cursor object. print "%s %-10s %s" % (col_names , col_names , col_names ) This line prints three column names of the Cars table. for row in rows: print "%2s %-10s %s" % row We print the rows using the for loop. The data is aligned with the column names. $ ./colnames2.py Id Name Price 1 Audi 52642 2 Mercedes 57127 3 Skoda 9000 4 Volvo 29000 5 Bentley 350000 6 Citroen 21000 7 Hummer 41400 8 Volkswagen 21600 Output. In our last example related to the metadata, we will list all tables in the test.db database. #!/usr/bin/python # -*- coding: utf-8 -*- import sqlite3 as lite import sys con = lite.connect('test.db') with con: cur = con.cursor() cur.execute("SELECT name FROM sqlite_master WHERE type='table'") rows = cur.fetchall() for row in rows: print row The code example prints all available tables in the current database to the terminal. cur.execute("SELECT name FROM sqlite_master WHERE type='table'") The table names are stored inside the system sqlite_master table. $ ./listtables.py Images sqlite_sequence Salaries Cars These were the tables on my system. Export and import of data We can dump data in an SQL format to create a simple backup of our database tables. #!/usr/bin/python # -*- coding: utf-8 -*- import sqlite3 as lite import sys cars = ( (1, 'Audi', 52643), (2, 'Mercedes', 57642), (3, 'Skoda', 9000), (4, 'Volvo', 29000), (5, 'Bentley', 350000), (6, 'Hummer', 41400), (7, 'Volkswagen', 21600) ) def writeData(data): f = open('cars.sql', 'w') with f: f.write(data) con = lite.connect(':memory:') with con: cur = con.cursor() cur.execute("DROP TABLE IF EXISTS Cars") cur.execute("CREATE TABLE Cars(Id INT, Name TEXT, Price INT)") cur.executemany("INSERT INTO Cars VALUES(?, ?, ?)", cars) cur.execute("DELETE FROM Cars WHERE Price 30000") data = '\n'.join(con.iterdump()) writeData(data) In the above example, we recreate the Cars table in the memory. We delete some rows from the table and dump the current state of the table into a cars.sql file. This file can serve as a current backup of the table. def writeData(data): f = open('cars.sql', 'w') with f: f.write(data) The data from the table is being written to the file. con = lite.connect(':memory:') We create a temporary table in the memory. cur.execute("DROP TABLE IF EXISTS Cars") cur.execute("CREATE TABLE Cars(Id INT, Name TEXT, Price INT)") cur.executemany("INSERT INTO Cars VALUES(?, ?, ?)", cars) cur.execute("DELETE FROM Cars WHERE Price 30000") These lines create a Cars table, insert values and delete rows, where the Price is less than 30000 units. data = '\n'.join(con.iterdump()) The con.iterdump() returns an iterator to dump the database in an SQL text format. The built-in join() function takes the iterator and joins all the strings in the iterator separated by a new line. This data is written to the cars.sql file in the writeData() function. $ cat cars.sql BEGIN TRANSACTION; CREATE TABLE Cars(Id INT, Name TEXT, Price INT); INSERT INTO "Cars" VALUES(1,'Audi',52643); INSERT INTO "Cars" VALUES(2,'Mercedes',57642); INSERT INTO "Cars" VALUES(5,'Bentley',350000); INSERT INTO "Cars" VALUES(6,'Hummer',41400); COMMIT; The contents of the dumped in-memory Cars table. Now we are going to perform a reverse operation. We will import the dumped table back into memory. #!/usr/bin/python # -*- coding: utf-8 -*- import sqlite3 as lite import sys def readData(): f = open('cars.sql', 'r') with f: data = f.read() return data con = lite.connect(':memory:') with con: cur = con.cursor() sql = readData() cur.executescript(sql) cur.execute("SELECT * FROM Cars") rows = cur.fetchall() for row in rows: print row In this script, we read the contents of the cars.sql file and execute it. This will recreate the dumped table. def readData(): f = open('cars.sql', 'r') with f: data = f.read() return data Inside the readData() method we read the contents of the cars.sql table. cur.executescript(sql) We call the executescript() method to launch the SQL script. cur.execute("SELECT * FROM Cars") rows = cur.fetchall() for row in rows: print row We verify the data. $ ./import.py (1, u'Audi', 52643) (2, u'Mercedes', 57642) (5, u'Bentley', 350000) (6, u'Hummer', 41400) The output shows, that we have successfully recreated the saved Cars table. Transactions A transaction is an atomic unit of database operations against the data in one or more databases. The effects of all the SQL statements in a transaction can be either all committed to the database or all rolled back. In SQLite, any command other than the SELECT will start an implicit transaction. Also, within a transaction a command like CREATE TABLE ..., VACUUM, PRAGMA, will commit previous changes before executing. Manual transactions are started with the BEGIN TRANSACTION statement and finished with the COMMIT OR ROLLBACK statements. SQLite support three non-standard transaction levels. DEFERRED, IMMEDIATE and EXCLUSIVE. SQLite Python module also supports an autocommit mode, where all changes to the tables are immediately effective. #!/usr/bin/python # -*- coding: utf-8 -*- import sqlite3 as lite import sys try: con = lite.connect('test.db') cur = con.cursor() cur.execute("DROP TABLE IF EXISTS Friends") cur.execute("CREATE TABLE Friends(Id INTEGER PRIMARY KEY, Name TEXT)") cur.execute("INSERT INTO Friends(Name) VALUES ('Tom')") cur.execute("INSERT INTO Friends(Name) VALUES ('Rebecca')") cur.execute("INSERT INTO Friends(Name) VALUES ('Jim')") cur.execute("INSERT INTO Friends(Name) VALUES ('Robert')") #con.commit() except lite.Error, e: if con: con.rollback() print "Error %s:" % e.args sys.exit(1) finally: if con: con.close() We create a Friends table and try to fill it with data. However, as we will see, the data is not committed. #con.commit() The commit() method is commented. If we uncomment the line, the data will be written to the table. sqlite .tables Cars Friends Images Salaries Temporary sqlite SELECT Count(Id) FROM Friends; Count(Id) ---------- 0 The table is created but the data is not written to the table. In the second example we demonstrate, that some commands implicitly commit previous changes to the database. #!/usr/bin/python # -*- coding: utf-8 -*- import sqlite3 as lite import sys try: con = lite.connect('test.db') cur = con.cursor() cur.execute("DROP TABLE IF EXISTS Friends") cur.execute("CREATE TABLE Friends(Id INTEGER PRIMARY KEY, Name TEXT)") cur.execute("INSERT INTO Friends(Name) VALUES ('Tom')") cur.execute("INSERT INTO Friends(Name) VALUES ('Rebecca')") cur.execute("INSERT INTO Friends(Name) VALUES ('Jim')") cur.execute("INSERT INTO Friends(Name) VALUES ('Robert')") cur.execute("CREATE TABLE IF NOT EXISTS Temporary(Id INT)") except lite.Error, e: if con: con.rollback() print "Error %s:" % e.args sys.exit(1) finally: if con: con.close() Again, we do not call the commit() command explicitly. But this time, the data is written to the Friends table. cur.execute("CREATE TABLE IF NOT EXISTS Temporary(Id INT)") This SQL statement will create a new table. It also commits the previous changes. $ ./implcommit.py sqlite SELECT * FROM Friends; Id Name ---------- ---------- 1 Tom 2 Rebecca 3 Jim 4 Robert The data has been written to the Friends table. In the autocommit mode, an SQL statement is executed immediately. #!/usr/bin/python # -*- coding: utf-8 -*- import sqlite3 as lite import sys try: con = lite.connect('test.db', isolation_level=None) cur = con.cursor() cur.execute("DROP TABLE IF EXISTS Friends") cur.execute("CREATE TABLE Friends(Id INTEGER PRIMARY KEY, Name TEXT)") cur.execute("INSERT INTO Friends(Name) VALUES ('Tom')") cur.execute("INSERT INTO Friends(Name) VALUES ('Rebecca')") cur.execute("INSERT INTO Friends(Name) VALUES ('Jim')") cur.execute("INSERT INTO Friends(Name) VALUES ('Robert')") except lite.Error, e: print "Error %s:" % e.args sys.exit(1) finally: if con: con.close() In this example, we connect to the database in the autocommit mode. con = lite.connect('test.db', isolation_level=None) We have an autocommit mode, when we set the isolation_level to None. $ ./autocommit.py sqlite SELECT * FROM Friends; Id Name ---------- ---------- 1 Tom 2 Rebecca 3 Jim 4 Robert The data was successfully committed to the Friends table. This was the SQLite Python tutorial.
There were a lot of developments in patent information last year. Below are some of the highlights from my favorite public patent databases and related websites. Canadian Patents Database (CIPO) The Canadian Patents Database , which is maintained by the Canadian Intellectual Property Office, contains more than two million Canadian patents and published applications from 1869 to the present. Full-text images are available from 1920 forward. Recent improvements include a few aesthetic changes to the search interface and the inclusion of a representative drawing (if available) displayed in the bibliographic record. In addition, as of January 29, 2010, abstracts in both English and French are available for applications filed under the PCT. (Approximately 75 percent of patent applications received by the CIPO are filed via the PCT system.) PatentScope (WIPO) PatentScope is the public database of record for PCT international patent applications published by the World Intellectual Property Organization. It contains approximately 1.7 million international applications published from 1978 forward. In 2009, WIPO extended PatentScope to include national patent collections from the African Regional Intellectual Property Organization, Cuba, Israel, Korea, Mexico, Singapore, South Africa and Vietnam. The largest of these are Korea (1.3 million documents from 1973-2007), Mexico (180,000 documents from 1991-2009) and Israel (144,000 documents from 1900-1999). WIPO also introduced a new search interface with simple, structured and browse functions, and the option to display search results as tables or graphs. The “classic” PatentScope search interface is still available for searching PCT international applications. Esp@cenet (EPO) Esp@cenet is a collection of free international and national patent databases hosted by the European Patent Office (EPO). The worldwide database contains approximately 60 million patent documents from more than 80 countries and over one million non-patent literature references. Early in the year, the EPO extended esp@cenet’s coverage of Latin American countries, adding several thousand patent documents from Chile (2005-2008), Ecuador (2005-2006), Nicaragua (2006-2008) and Panama (1999-2006). During the summer, more than six million U.S. patent assignment records dating back to 1981 were reloaded. Legal status data was added or reloaded for Russian patent and utility models, Polish patents, and Chinese patents and utility models dating back to October 1985. At the end of the year, the EPO announced a number of enhancements that were implemented in early 2010. These include highlighted search terms in titles, abstracts and full-text; full-text searching for EP and WO documents in all three official languages; and the ability to sort search results by date, inventor, applicant and ECLA code. Users can now enter more search terms in any one field (the previous limit was four). In related news, the French National Institute of Industrial Property (INPI) replaced its old fee-based patent and trademark search systems with free web-based services. These include legal status information for granted French patents (B documents) and European patents designating France. (French patents are not available in the esp@cenet worldwide database but may be accessed via the French esp@cenet gateway.) Other services include databases of French designs and models from 1910 forward and international designs and models from 1979 forward. USPTO The USPTO hosts several public databases, including the two main patent databases, PatFT, containing issued patents from 1976 forward and AppFT, covering published applications from 2001 forward. Other databases include the Patent Application Information Retrieval (PAIR) system and the Patent Assignment Database, which contains recorded patent assignment information from 1980 to present. The USPTO website underwent a major reorganization in mid-2009 but there were no significant changes to the patent databases. U.S. patent documents reached several notable milestones in 2009. In February, the USPTO published the two millionth application. The USPTO published the first application (A document) on March 15, 2001. Prior to that date, applications remained confidential until a patent issued. On March 3rd, patent no. 7,500,000 was issued and on May 19th plant patent no. 20,000 was granted. President Obama’s new initiative to expand public access to government information and data could have a big impact on the USPTO. In September, the USPTO posted an RFI called the USPTO’s Data Dissemination Solution . The proposal seeks input from public or private sector parties interested in helping the USPTO make virtually all its public information freely accessible on the internet. The USPTO estimates that all of its data sets total about two petabytes. In exchange, the parties will be able to retain and use the data for their own purposes. It will be interesting to see if the USPTO can find any partners willing to accept these terms. Last fall, David Kappos, the new Director of the USPTO, launched a blog called the Director’s Forum. In a recent post he expressed a desire to update the Manual of Patent Examining Procedure (MPEP), the USPTO’s handbook of patent rules and regulations. He described the current system used to produce the MPEP as an embarrassment. It takes too long to update and is cumbersome to produce. There are good document management systems that could solve these problems. Kappos also suggested that a reengineered MPEP could include wiki-style content contributed by patent professionals. Patent Lens Patent Lens is a free full-text patent database maintained by Cambia, an independent, non-profit research institute based in Australia. It contains approximately 10 million full-text patent documents published by the Australian patent office (1998+), USPTO (1976+), EPO (1978+) and WIPO (1978+). One of its unique features is the ability to search for gene sequences in patent documents using NCBI’s Blast software. Recent improvements to Patent Lens include a new patent search interface in Chinese and French and the ability to search PCT applications in the language of filing or publication, e.g. Chinese, English, French, German, Japanese, Korean, Russian and Spanish. Patent family trees now include a key explaining the family member colour-coding system. FreePatentsOnline FreePatentsOnline (FPO) is a free full-text patent database launched in 2005 by James Ryley. It covers full-text patent documents published by the EPO, USPTO and WIPO, and Japanese patent abstracts. Early in 2009, FPO added titles for patents cited on the front page of U.S. patent documents. (The actual printed patent lists only the number, date and inventor name.) Around mid-year, FPO launched a spin-off site called LocalPatents that maps U.S. patent data to geographic location. Patent clusters display on a map of the U.S. Users can zoom from the state level down to individual towns and cities and retrieve patents granted to residents of that location. Boliven Patents In January 2009, Boliven , a small start-up based in NYC, launched a website designed for professional entrepreneurs, inventors, researchers and patent attorneys. One of the resources offered was a patent database that covered U.S. patents from 1976 forward, EP documents from 1978 forward, PCT applications from 1989 forward and Japanese patent abstracts. Korean patents and INPADOC data were added later. The search interface included several innovative features, including a “Quick Flip” display option for rapidly viewing patent documents, faceted filtering and analytical tools for displaying search results as charts and graphs. Registered users could save searches, create search alerts and download data. Although the search engine performed well for keyword searches, it struggled with USPC and IPC classifications, producing results that were unreliable and inaccurate. From January to April 2009, access to the patent database was free. On May 5, Boliven’s management announced that it would begin charging users $60 per month. Within a few weeks, however, it changed course and announced that the patent database, among other services, would remain free and that it would attempt to generate revenue from other services. Additional collections of public documents and records were added over the course of the year. However, the website’s future is uncertain. On January 15, 2010, Boliven’s management suddenly announced that it would cease operations as of January 22, citing its failure to meet operational and financial goals. As of February 23, the website and patent database are still online, although no new patent data appears to have been loaded since December. PatSnap PatSnap is a new fee-based patent search and analytics service launched in 2009 by a Singapore-based company. It covers full-text patent documents from the U.S. (1971+), Europe (1978+) and PCT (1978+) and Chinese patent abstracts from 1985 forward. A free trial was offered to members of ELD. Users can register for a free basic account that includes searching and viewing documents from the U.S., European and PCT collections. A day pass costs $39 and includes Chinese patent abstracts, basic analysis, 100 PDF downloads and 500 bibliographic data exports per day. The full plan costs $199 per month. PatSnap has a well-designed user interface, good search options and powerful analytic tools. Keyword searches performed well compared with other public patent databases, but classification searches did not produce the expected results. Related News Websites Intellogist is a new website and online community for professional patent searchers. It is sponsored by Landon IP, a firm located in Alexandria, Virginia specializing in patent and trademark searches and patent analytics, and supported in part by advertisements. Both novice and experienced patent searchers will find it very useful. Resources include profiles of commercial and public patent databases, comparisons of patent search system capabilities, national patent coverage, best practices in prior art searching and a glossary. Registered users can contribute and revise content. International Patent Classification (IPC) There are major changes afoot for the IPC in 2010. The division between core and advanced levels, which was introduced in 2006, will be removed. Patent offices currently classifying documents using the core level will now use the main groups instead. New versions of the IPC will be published once a year in electronic format only. The integration of local classification systems (USPC, ECLA and JPO FI/F Term) will be accelerated. IP5 Initiatives The world’s five major patent offices (USPTO, EPO, JPO, SIPO and KIPO) known as the IP5 moved forward on series of joint projects announced at the end of 2008. The EPO is the lead office tasked with developing a common documentation database and common approach to patent classification. The USPTO is leading the development of a common approach to sharing and document search strategies and common search and examination support tools. The JPO will develop a common application format and access to search and examination results. KIPO is responsible for training policy and mutual machine translation. And the SIPO is working on a common set of rules for examination practice and quality control.
Web Resources for Plant Scientists by Christoph on October 16, 2009 miRU: Plant miRNA Potential Target Finder http://bioinfo3.noble.org/miRNA/miRU.htm website allows users to predict plant miRNA target genes. By entering a small RNA sequence (19-28nt) the program will report all potential complimentary sequences. This PlantGDB http://www.plantgdb.org/ website is dedicated entirely to plant miRNA and provides many useful tools such as sequence browser assemblies, genome browser, and other tools and datasets. This PMRD: plant miRNA Database http://bioinformatics.cau.edu.cn/PMRD/ site offers a complete list of all publicly known plant miRNA sequences. The database is composed of a total of 8,433 sequences including all known sequences in the miRBase database. This UPC: A Resource for Predicting and Comparing Plant microRNAs http://www3a.biotec.or.th/micropc/index.html users can predict and compare plant miRNA sequences. This site includes 128 miRNA families, 125 plant species, and 2,995 protein targets. Here The UEA plant sRNA toolkit http://srna-tools.cmp.uea.ac.uk/targets/ can input sequences up to 50 nucleotides in length in FASTA format to run target predictions for their sequence dataset. Users Incoming search terms for this article: plant gene miRNA sRNA toolkid cancer Total Plant RNA extraction from plant for Deep RNA sequencing service Related posts: Target-align: a tool for plant microRNA target identification Plant microRNA Database Goes Online microRNA Target Prediction Tools TAPIR, a web server for the prediction of plant microRNA targets, including target mimics miRBase Version 15.0 is Released Tagged as: plant microrna database, plant miRNA, plant science http://mirnablog.com/web-resources-for-plant-scientists/
Refer to : http://stackoverflow.com Databases that do not fall into a neat grid. You don't have to specify which things are integers and strings and booleans, etc. These types of databases are more flexible, but they don't use SQL, because they are not structured that way. The advantage of using a NoSQL database is that you don't have to know exactly what your data will look like ahead of time. Perhaps you have a Contacts table, but you don't know what kind of information you'll want to store about each contact. In a relational database, you need to make columns like Name and Address. If you find out later on that you need a phone number, you have to add a column for that. There's no need for this kind of planning/structuring in a NoSQL database. There are also potential scaling advantages, but that is a bit controversial, so I won't make any claims there. Disadvantages of NoSQL databases is really the lack of SQL. SQL is simple and ubiquitous. SQL allows you to slice and dice your data easier to get aggregate results, whereas it's a bit more complicated in NoSQL databases (you'll probably use things like MapReduce, for which there is a bit of a learning curve). This ties into the ideas of ACID and CAP ; See presentation NoSQL Databases here: http://www.slideshare.net/jonmeredith/front-range-php-nosql-databases; And explanation from wikipedia; My thoughts on NoSQL from Eric Florenzanos Blog; (to be continued)
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