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：ThegeneticcodonUGAhasadualfunction:servingasaterminatorandencodingselenocysteine.However,mostpopulargeneannotationprogramsonlytakeitasastopsignal,resultinginmisannotationorcompletelymissingselenoproteingenes.WedevelopedacomputationalmethodnamedAsec-Predictionthatisspecificforthepredictionofarchaealselenoproteingenes.Toevaluateitseffectiveness,wefirstapplieditto14archaealgenomeswithpreviouslyknownselenoproteingenes,andAsec-Predictionidentifiedallreportedselenoproteingeneswithoutredundantresults.Whenweapplieditto12archaealgenomesthathadnotbeenresearchedforselenoproteingenes,Asec-PredictiondetectedanovelselenoproteingeneinMethanosarcinaacetivorans.Furtherevidencewasalsocollectedtosupportthatthepredictedgeneshouldbearealselenoproteingene.TheresultshowsthatAsec-Predictioniseffectiveforthepredictionofarchaealselenoproteingenes.

简介：TheNetAcetmethodhasbeendevelopedtomakepredictionsofN-terminalacetylationsites,butmoreinformationofthedatasetcouldbeutilizedtoimprovetheperformanceofthemodel.Byemployinganewwaytoextractpatternsfromsequencesandusingasamplebalancingmechanism,weobtainedacorrelationcoefficientof0.85,andasensitivityof93%onanindependentmammaliandataset.Awebserverutilizingthismethodhasbeenconstructedandisavailableathttp://166.111.24.5/acetylation.html.

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

简介：单个核苷酸多型性(SNP)是决定任何二个无关的个人之间的差别的基因变化。各种各样的人口组能用SNP与对方被区分开来。例如，HapMap数据集与大约1000万SNP有四个人口组。为人的进化，种族变化，和人口赋值上的更多的卓见，我们建议发现哪个SNP在决定人口组是重要的然后作为输入特征用这些相关SNP分类不同人口。在这研究，我们开发了评价措施的修改t测试并且把它用于HapMap遗传型数据。第一，我们为赋值包括F统计和增进知识的海角与另外的特征重要性措施比较评价所有SNP。第二，我们作为输入选择最高度评价的SNP的不同数字到一个分类器，例如支持向量机器，以便发现最好的特征相应于最好的分类精确性的子集。试验性的结果证明建议方法在发现在决定人口组是重要的SNP是很有效的，与减少的计算负担和更好的分类精确性。

简介：高原一种短耳野兔(Ochotonacurzoniae)的一个充实microsatellite的图书馆根据与biotin和streptavidin的强壮的亲密关系被构造。第一，genomicDNA被ultrasonication碎裂，它是在传统的方法上的主要改进。绑扎连接器的DNA碎片是有biotinylatedmicrosatellite探针的hybridized，然后受到streptavidin涂的磁性的祷告。PCR扩大被执行获得包含microsatellites的双strandedDNA碎片。结扎和转变被使用pGEM-T向量系统执行我和EscherichiacoliDH10B能干的房间。定序结果证明80.2%克隆包含了microsatellite重复主题。几修正使这个协议比传统的时间有效、技术上容易；特别地，在高原一种短耳野兔染色体的作文和相对许多microsatellite重复真正通过优化PCR条件被代表。这个方法成功地也被使用了构造中国仓鼠(Cricetulusgriseus)的充实microsatellite的genomic图书馆，表明它的可行性和稳定性并且小鲍鱼[Haliotisdiversicolor(穿)]与积极克隆的高率。

简介：Predictingprotein-codinggenesstillremainsasignificantchallenge.Althoughavarietyofcomputationalprogramsthatusecommonlymachinelearningmethodshaveemerged,theaccuracyofpredictionsremainsalowlevelwhenimplementinginlargegenomicsequences.Moreover,computationalgenefindinginnewlyse-quencedgenomesisespeciallyadifficulttaskduetotheabsenceofatrainingsetofabundantvalidatedgenes.Herewepresentanewgene-findingprogram,SCGPred,toimprovetheaccuracyofpredictionbycombiningmultiplesourcesofevidence.SCGPredcanperformbothsupervisedmethodinpreviouslywell-studiedgenomesandunsupervisedoneinnovelgenomes.BytestingwithdatasetscomposedoflargeDNAsequencesfromhumanandanovelgenomeofUstilagomaydi,SCG-Predgainsasignificantimprovementincomparisontothepopularabinitiogenepredictors.WealsodemonstratethatSCGPredcansignificantlyimprovepredic-tioninnovelgenomesbycombiningseveralforeigngenefinderswithsimilarityalignments,whichissuperiortootherunsupervisedmethods.Therefore,SCG-Predcanserveasanalternativegene-findingtoolfornewlysequencedeukaryoticgenomes.Theprogramisfreelyavailableathttp://bio.scu.edu.cn/SCGPred/.

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：在这研究，被尝试了从他们的氨基酸序列预言克否定的细菌的蛋白质的主要功能。使用训练并且测试的数据集由670非冗余的克否定的细菌的蛋白质(255细胞的过程，60个信息分子，285新陈代谢，和70个毒力因素)组成。首先，我们开发了使用氨基酸和dipeptide作文的一个基于SVM的方法并且分别地完成了52.39%和47.01%的全面精确性。我们基于tetrapeptides，我们在识别了显著地在蛋白质的一个班上发现的唯一的tetrapeptides为蛋白质的分类介绍了一个新概念。这些tetrapeptides作为输入特征被使用预言蛋白质的功能并且完成了68.66%的全面精确性。我们也开发了tetrapeptide信息与氨基酸作文在被使用并且完成了70.75%的全面精确性的一个混合方法。五倍的生气确认被用来评估这些方法的表演。网服务器VICMpred为预言克否定的细菌的蛋白质(http://www.imtech.res.in/raghava/vicmpred/)的函数被开发了。

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.