简介：双向强风是一条简单途径检测，注解，并且分析候选人orthologous或在一个单身者的paralogous序列去。这个过程通常被限制到设定的Perl脚本的领域，通常为象UNIX一样调节了环境。移植那些脚本到另外的操作系统涉及重新分解他们，并且另外有要求的图书馆的Perl编程环境的安装。克服这些限制，一条数据管道在Java被实现。这应用提交序列的二批到NCBI强风工具的本地版本，管理结果表，并且精制双向、简单的点击。变细长术语被纳入点击，几统计被导出，并且分子的进化率通过PAML被估计。结果被写给为进一步的分析打算的一套限定的文章表格。提供的图形的用户接口用这个应用程序允许一个友好相互作用，它被记录并且对在http://moodle.fct.unl.pt/course/view.php的下载可得到?在GNUGPL许可证下面的id=2079或https://sourceforge.net/projects/bidiblast/。

简介：G-proteincoupledreceptors(GPCRs)representoneofthemostimportantclassesofdrugtargetsforpharmaceuticalindustryandplayimportantrolesincellularsignaltransduction.PredictingthecouplingspecificityofGPCRstoG-proteinsisvitalforfurtherunderstandingthemechanismofsignaltransductionandthefunctionofthereceptorswithinacell,whichcanprovidenewcluesforpharmaceuticalresearchanddevelopment.Inthisstudy,thefeaturesofaminoacidcompositionsandphysiochemicalpropertiesofthefull-lengthGPCRsequenceshavebeenanalyzedandextracted.Basedonthesefeatures,classifiershavebeendevelopedtopredictthecouplingspecificityofGPCRstoG-proteinsusingsupportvectormachines.Thetestingresultsshowthatthismethodcouldobtainbetterpredictionaccuracy.

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简介：Microarrayhasbecomeapopularbiotechnologyinbiologicalandmedicalresearch.However,systematicandstochasticvariabilitiesinmicroarraydataareexpectedandunavoidable,resultingintheproblemthattherawmeasurementshaveinherent"noise"withinmicroarrayexperiments.Currently,logarithmicratiosareusuallyanalyzedbyvariousclusteringmethodsdirectly,whichmayintroducebiasinterpretationinidentifyinggroupsofgenesorsamples.Inthispaper,astatisticalmethodbasedonmixedmodelapproacheswasproposedformicroarraydataclusteranalysis.TheunderlyingrationaleofthismethodistopartitiontheobservedtotalgeneexpressionlevelintovariousvariationscausedbydifferentfactorsusinganANOVAmodel,andtopredictthedifferentialeffectsofGV(genebyvariety)interactionusingtheadjustedunbiasedprediction(AUP)method.ThepredictedGVinteractioneffectscanthenbeusedastheinputsofclusteranalysis.Weillustratedtheapplicationofourmethodwithageneexpressiondatasetandelucidatedtheutilityofourapproachusinganexternalvalidation.

简介：Annotationsofcompletegenomesequencessubmitteddirectlyfromsequencingprojectsarediverseintermsofannotationstrategiesandupdatefrequencies.Theseinconsistenciesmakecomparativestudiesdifficult.Toallowrapiddataprepara-tionofalargenumberofcompletegenomes,automationandspeedareimpor-tantforgenomere-annotation.Hereweintroduceanopen-sourcerapidgenomere-annotationsoftwaresystem,Restauro-G,specializedforbacterialgenomes.Restauro-Gre-annotatesagenomebysimilaritysearchesutilizingtheBLAST-LikeAlignmentTool,referringtoproteindatabasessuchasUniProtKB,NCBInr,NCBICOGs,Pfam,andPSORTb.Re-annotationbyRestauro-Gachievedover98%accuracyformostbacterialchromosomesincomparisonwiththeoriginalmanuallycuratedannotationofEMBLreleases.Restauro-GwasdevelopedinthegenericbioinformaticsworkbenchG-languageGenomeAnalysisEnvironmentandisdistributedathttp://restauro-g.iab.keio.ac.jp/undertheGNUGeneralPublicLicense.

简介：Thestudyofsmalldrugmoleculesinteractingwithnucleicacidsisanareaofintenseresearchthathasparticularrelevanceinourunderstandingofrelativemechanisminchemotherapeuticapplicationsandtheassociationbetweengenetics(includingsequencevariation)anddrugresponse.Inthiscontribution,wedemonstratehowthesequence-specificbindingofananticancerdrugDacarbazine(DTIC)tosinglebase(A-G)mismatchcouldbesensitivelydetectedbycombiningelectrochemicaldetectionwithbiosensingsurfacebasedongoldnanoparticles.

简介：UnderstandingthecouplingspecificitybetweenGprotein-coupledreceptors(GPCRs)andspecificclassesofGproteinsisimportantforfurtherelucidationofreceptorfunctionswithinacell.IncreasinginformationonGPCRsequencesandtheGproteinfamilywouldfacilitatepredictionofthecouplingpropertiesofGPCRs.Inthisstudy,wedescribeanovelapproachforpredictingthecouplingspecificitybetweenGPCRsandGproteins.ThismethodusesnotonlyGPCRsequencesbutalsothefunctionalknowledgegeneratedbynaturallanguagepro-cessing,andcanachieve92.2%predictionaccuracybyusingtheC4.5algorithm.Furthermore,rulesrelatedtoGPCR-Gproteincouplingaregenerated.Thecom-binationofsequenceanalysisandtextminingimprovesthepredictionaccuracyforGPCR-Gproteincouplingspecificity,andalsoprovidescluesforunderstandingGPCRsignaling.

简介：AcomputationalsystemforthepredictionandclassificationofhumanG-proteincoupledreceptors(GPCRs)hasbeendevelopedbasedonthesupportvectormachine(SVM)methodandproteinsequenceinformation.ThefeaturevectorsusedtodeveloptheSVMpredictionmodelsconsistofstatisticallysignificantfeaturesselectedfromsingleaminoacid,dipeptide,andtripeptidecompositionsofproteinsequences.Furthermore,thelengthdistributiondifferencebetweenGPCRsandnon-GPCRshasalsobeenexploitedtoimprovethepredictionperformance.ThetestingresultswithannotatedhumanproteinsequencesdemonstratethatthissystemcangetgoodperformanceforbothpredictionandclassificationofhumanGPCRs.

简介：G-proteincoupledreceptors(GPCRs)areaclassofseven-helixtransmembraneproteinsthathavebeenusedinbioinformaticsasthetargetstofacilitatedrugdiscoveryforhumandiseases.AlthoughthousandsofGPCRsequenceshavebeencollected,theligandspecificityofmanyGPCRsisstillunknownandonlyonecrystalstructureoftherhodopsin-likefamilyhasbeensolved.Therefore,identifyingGPCRtypesonlyfromsequencedatahasbecomeanimportantresearchissue.Inthisstudy,anoveltechniqueforidentifyingGPCRtypesbasedontheweightedLevenshteindistancebetweentworeceptorsequencesandthenearestneighbormethod(NNM)isintroduced,whichcandealwithreceptorsequenceswithdifferentlengthsdirectly.Inourexperimentsforclassifyingfourclasses(acetylcholine,adrenoceptor,dopamine,andserotonin)oftherhodopsin-likefamilyofGPCRs,theerrorratesfromtheleave-one-outprocedureandtheleave-half-outprocedurewere0.62%and1.24%,respectively.Theseresultsarepriortothoseofthecovariantdiscriminantalgorithm,thesupportvectormachinemethod,andtheNNMwithEuclideandistance.

简介：Werecentlyreportedtheuseofagene-trappingapproachtoisolatecellclonesinwhichareportergenehadintegratedintogenesmodulatedbyT-cellactivation.WehavenowtestedapanelofclonesfromthatreportandidentifiedtheonethatrespondstoavarietyofG-proteincoupledreceptors(GPCR).TheβlactamasetaggedEGR-3JurkatcellwasusedtodissectspecificGPCRsignalinginvivo.ThreeGPCRswerestudied,includingthechemokinereceptorCXCR4(Gicoupled)thatwasendogenouslyexpressed,theplateletactivationfactor(PAF)receptor(Gq-coupled),andβ2adrenergicreceptor(Gs-coupled)thatwasbothstablytransfected.Agonistsforeachreceptoractivatedtranscriptionoftheβ-lactamasetaggedEGR-3gene.InductionofEGR-3throughCXCR4wasblockedbypertussistoxinandPD58059,aspecificinhibitorofMEK(MAPK/ERKkinase).NeitheroftheseinhibitorsblockedisoproterenolorPAF-mediatedactivationofEGR-3.Conversely,β2-andPAF-mediatedEGR-3activationwasblockedbythep38,specificinhibitorSB580.Inaddition,bothβ2-andPAF-mediatedEGR-3activationcouldbesynergisticallyactivatedbyCXCR4activation.ThiscombinedresultindicatesthatEGR-3canbeactivatedthroughdistinctsignaltransductionpathwaysbydifferentGPCRsandthatsignalscanbeintegratedandamplifiedtoefficientlytunethelevelofactivation.

简介：在cis的第二上的一个部件的transcriptional活动的直接否定的影响被叫作transcriptional干扰(TI)。U6是通常使用在基于向量的RNAi驾驶小发卡RNA(shRNA)表示的一个类型IIIRNA聚合酶III倡导者。在病毒的向量的设计和构造，多重抄写单位可以在空间有限向量在靠近的最近被安排。决定U6倡导者活动是否能被TI影响为在基因治疗的目标shRNA的表示是批评的或loss-of-function学习。在这研究，我们设计了并且实现shRNA和外长的基因表情由二个独立transcriptional单位被驾驶的一个修改retroviral系统。我们安排了在二倡导者安排驾驶shRNA表示和UbiC倡导者的U6倡导者。在主要巨噬细胞，我们发现U6倡导者活动被UbiC倡导者禁止什么时候在分叉的安排然而并非在双人脚踏车。相反，PKG倡导者没有如此的否定影响。而不是提高U6倡导者活动，CMVenhancer面对UbiC倡导者在U6倡导者活动有重要否定影响。我们的结果显示那项U6倡导者活动能被TI影响在一近似倡导者特定并且安排依赖者举止。