简介：Genomicspromisestoenrichtheinvestigationsofbiologyandbiochemistry.Currentadvancementsingenomicshavemajorimplicationsforgeneticimprovementinanimals,plants,andmicroorganisms,andforourunderstandingofcellgrowth,development,differentiation,andcommunication.Significantprogresshasbeenmadeintheunderstandingofplantgenomicsinrecentyears,andtheareacontinuestoprogressrapidly.Functionalgenomicsoffersenormouspotentialtotreeimprovementandtheunderstandingofgeneexpressioninthisareaofscienceworldwide.Inthisreviewwefocusonfunctionalgenomicsofwoodqualityandpropertiesintrees,mainlybasedonprogressesmadeingenomicsstudyofPinusandPopulus.Theaimsofthisreviewaretosummarizethecurrentstatusoffunctionalgenomicsincluding:(1)Genediscovery;(2)ESTandgenomicsequencing;(3)FromESTtofunctionalgenomics;(4)Approachestofunctionalanalysis;(5)Engineeringligninbiosynthesis;(6)Modificationofcellwallbiogenesis;and(7)Molecularmodelling.Functionalgenomicshasbeengreatlyinvestedworldwideandwillbeimportantinidentifyingcandidategeneswhosefunctioniscriticaltoallaspectsofplantgrowth,development,differentiation,anddefense.Forestbiotechnologyindustrywillsignificantlybenefitfromtheadventoffunctionalgenomicsofwoodqualityandproperties.

简介：Inviewofpotentialapplicationtobiomedicaldiagnosis,tighttranscriptomedataqualitycontroliscompulsory.Usually,qualitycontrolisachievedusinglabelingandhybridizationcontrolsaddedatdifferentstagesthroughouttheprocessingofthebiologicRNAsamples.Thesecontrolmeasures,however,onlyreflecttheperformanceoftheindividualtechnicalmanipulationsduringtheentireprocessandhavenobearingastothecontinuedintegrityoftheRNAsampleitself.Herewedemonstratethatintrinsicstatisticalpropertiesoftheresultingtranscriptomedatasignalandsignal-variancedistributionsandtheirinvariancecanbeidentifiedindependentlyoftheanimalspeciesstudiedandthelabelingprotocolused.Fromtheseinvariantpropertieswehavedevelopedadatamodel,theparametersofwhichcanbeestimatedfromindividualexperimentsandusedtocomputerelativequalitymeasuresbasedonsimilaritywithlargereferencedatasets.Thesequalitymeasuresaddsupplementary,non-redundantinformationtostandardqualitycontrolestimatesbasedonspike-inandhybridizationcontrols,andareexploitableindataanalysis.AsoftwareapplicationforanalyzingdatasetsaswellasareferencedatasetforAB1700arraysareprovided.TheyshouldallowAB1700userstoeasilyintegratethismethodintotheiranalysispipeline,andmightinstigatesimilardevelopmentsforothertranscriptomeplatforms.

简介：技术显著地改进了定序产量并且减少的下一代的定序的出现(NGS)花费。然而，短读的长度，副本读并且数据的巨大的体积使处理的数据比归化为美国人的定序技术更困难、复杂。尽管有包裹开发了估计数据质量的某软件，那些包裹任何一个不对用户容易可得到或要求生物信息学技巧和计算机资源。而且，当前可得到的几乎所有优秀评价软件当在NGS数据处理副本评价时，考虑定序的错误。这里，我们在场一个新用户友好的优秀评价软件包裹叫了BIGpre，它为Illumina和454个平台工作。BIGpre包含另外的优秀评价软件的所有函数，例如关联在之间前面、反向读，读GC内容分发，和基础N质量。更重要地，BIGpre合并联系程序检测并且搬迁副本在订定序错误进报道并且整修低质量以后读也从未加工的数据读。BIGpre首先在Perl被写并且从统计包裹R集成图形的能力。这个包裹为从Illumina和454个平台定序数据集生产数据质量的平坦、图形的摘要。处理几百百万在分钟以内读，这个包裹提供立即的诊断信息让用户操作为下游的分析定序数据。BIGpre在http://bigpre.sourceforge.net是自由地可得到的。