Ebook: Bioinformatics for systems biology

The biological sciences are now in the midst of a true life sciences revolution akin to what physics experienced just after the turn of the last century. We are now in a phase of unparalleled growth that is reflected by the amount of data generated from each experiment. At the time of this writing, the rate of data acquisition was approaching 2 terabytes over the course of 5 days with first pass analysis proceeding over the following 2-3 week period. This fundamental shift has provided unprecedented opportunities that for the first time afford us the ability, i.e., means, breadth, and depth of data, to truly address human biology at the systems level. This wealth of information from seemingly disparate datasets and its integration is being realized through bioinformatics. It is with this philosophy that the text Bioinformatics for Systems Biology was born. This revolution has spawned true personalized medicine that encompasses diagnostics and treatment through to cure.

For the physical and computer scientist, this text provides an introduction to the basic biological principles governing a cell. This quickly moves from the fundamentals to exploring the underlying genetic processes. While providing a rudimentary and necessary overview for the life scientist, the physical and computer scientist will be apprised of various nuances within the field reflecting the reality of "wet-bench" science. For those in the life sciences, it I rapidly becoming appreciated that we now progressing from examining our favorite "pet" gene to the system. Statistics is now an essential component to understand the vast datasets and this is emphasized throughout the text.

The majority of the text is devoted to the common ground that these groups share. It provides rich examples of tools, databases, and strategies to mine the databases to reveal novel insights. A host of examples of parsing the data into a series of overlays that use various presentation systems are reviewed. The goal is to provide a representation most comfortable to the user to enable the user to thoroughly explore the data. The text concludes with examples of how the systems information is used to inform personalized medicine in a true "bench to bedside" manner.

Bioinformatics for Systems Biology bridges and unifies many disciplines. It presents the life scientist, computational biologist, and mathematician with a common framework. Only by linking the groups together may the true life sciences revolution move forward in the mostly uncharted and emerging field of Systems Biology.

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With the completion of the human genome project, followed by the rise in high-throughput technologies like the various microarray and now high throughput genomic sequencing platforms,weexperiencedthebirthofSystemsBiologyafteritslonggestation. Thisrevolutionis markedbyachangeintheresearchparadigmfromthesinglesmall-scaleexperiment,i. e. ,following thechangeofacomponentinamulticomponentsystem,toonethatattemptstosimultaneously monitorthechangeoftensofthousandsofmoleculeswithinthisbody. Thisclearlynecessitatesthe unparalleleduseofproject-specificinformatictools,which,todate,requiresanunprecedentedlevel ofdevelopmenttocollect,manageandminethedataforinterestingassociations. Tobegintounderstandthisinformationwenowrelyonstatisticalanalysistoaidinourselection ofthefruitfromthetree. However,thisoftentakesusonajourneyintoanewfieldforwhichweare notyetprepared. SamuelJohnson(1709–1784)foreshadowedthedilemmawewouldfaceand characterizeditasfollows:‘‘Knowledgeisoftwokinds. Weknowasubjectourselves,orweknow wherewecanfindinformationonit. ’’Itisforthelatterthatweroutinelyturntotheliterature. The rateofgrowthoftheliteratureparallelsthatofsequencingdataandthearraydataplacingan almostimpossibletaskbeforeeachinvestigator. Topartiallyeasethisburdenweareagainturning towardsdevelopinginformaticaidsthatminetheliteratureanddatatodevelopsummariesand associationstodirectlyaddressthequestionsposedandthenewhypothesesthataretobetested. Althoughmoreclearlyarticulated,weagainfacesimilarchallengesasthosetackledduringthe courseofthehumangenomeproject. Itisessentialthatthetrainingofthebiologistandcomput- scientistoccurinaninterdisciplinaryenvironmentofcross-fertilization. Withthisgoalinmindthe textbook‘‘BioinformaticsforSystemsBiology’’wasundertaken. WebeginthisexplorationwithPartI,toprovidethecomputerscientistwithanintroductiontothe underlyingprinciplesofcellbiology. ThisisfollowedbyabriefintroductioninPartIIasameansfor thebiologisttobecomefamiliarwithconceptsandthestatisticalanalysisoflargedatasets. PartIII thendescribes,todate,thebestcharacterizeduseofthemicroarrayplatformthatisnowmoving towardswholegenomeanalysis. Withallofthisdata,howdowebeginanalysisforcommonelements guidingtheunderlyingprinciples?ThisisdiscussedinPartIVwhichleadstoPartVandPartVIto test,insilico,therelationshipsonawidescaleinordertoassesstheirapplicability. Upondeveloping theassociations,PartVIIaskshowdoesthisinformationrelatetowhatwasmeasured?Asthesebasic principlesaredevelopedfroman‘‘omics’’drivenbiologicalsystemsapproach,theyareappliedin PartVIIItotranslationalmedicine. Anexcellentexampleisthenewterm‘‘personalizedmedicine’’ thatisbeginningtoreverberateinclinicalcare. ItistheculminationoftheSystemsBiologyrevolution wheretechnologicaladvancesandcross-fertilizationhavedriventhefieldtomaturetothepoint whereitisbeingincorporatedinatruebench-to-bedsidemanner. Asyoureadthechapters,youwillfindthattheycanstandalone,yetcanbecombinedto emphasizetheintegralroleofinformaticsinSystemsBiology. Mostofthefiguresandtablesarein greyscale. IwouldencourageyoutoviewthosethatbenefitfromcolorontheaccompanyingCD. ThematerialcontainedontheCDprovidesanexcellentsourceofslidesforyourlecturesand presentations. v vi Preface Thechapter-relatedGlossaryandAbbreviationssectionwillassistinfamiliarizingyouwiththe terms. Youwillalsofindtheliteratureandsuggestedreadingsections,includingkeyreferences, veryusefulasyoudelveintothesubjectmatter. Technology,byitsverymeaningimpliesrefinement and change. The informatics approaches used in systems biology are continually subject to refinement. Withthisreality,youareencouragedtoutilizethewebsiteinformationprovidedin variouschapterstohelpaccessthemostcurrentinformationandresourcesavailable. AsSystems Biologydevelopsweareabletowitnessgrowingpainsandmilestones. Withcontinuedinformatic andbiologicalcross-fertilization,advancementsinSystemsBiologywillrevolutionizepersonalized medicineansweringquestionsbyintegratinginformationinunexpectedways. Contents PartI LifeofaCellandItsAnalysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1 StructureandFunctionoftheNucleusandCellOrganelles. . . . . . . . . . . . . . . . . . . . . . . . 3 JonHolyandEdPerkins 2 TranscriptionandtheControlofGeneExpression. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 NadineWiper-BergeronandIlonaS. Skerjanc 3 RNAProcessingandTranslation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 ChristinaKaramboulas,NadineWiper-Bergeron,andIlonaS. Skerjanc 4 DNAReplication,Recombination,andRepair. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 LindaB. Bloom 5 CellSignaling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 DanielA. RappoleeandD. RandallArmant 6 EpigeneticsofSpermiogenesis–CombiningInSilicoandProteomicApproaches intheMouseModel. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 SophieRousseauxandMyriamFerro 7 GenomicToolsforAnalyzingTranscriptionalRegulatoryNetworks. . . . . . . . . . . . . . . . . 119 JohnJ. Wyrick PartII StatisticalToolsandTheirApplication. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 8 ProbabilityandHypothesisTesting. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 MichaelL. Kruger 9 StochasticModelsforBiologicalPatterns. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 GautamB. Singh 10 PopulationGenetics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 JillS. Barnholtz-SloanandHemantK. Tiwari 11 StatisticalToolsforGeneExpressionAnalysisandSystemsBiology andRelatedWebResources. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181 ChiaraRomualdiandGerolamoLanfranchi vii viii Contents PartIII TranscriptomeAnalysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207 12 WhatGoesinisWhatComesOut:HowtoDesignandImplementaSuccessful MicroarrayExperiment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209 JeffreyA. LoebandThomasL. Beaumont 13 ToolsandApproachesforanEnd-to-EndExpressionArrayAnalysis. . . . . . . . . . . . . . . . 227 AdrianE. PlattsandStephenA. Krawetz 14 AnalysisofAlternativeSplicingwithMicroarrays. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267 JingyiHui,ShivendraKishore,AmitKhanna,andStefanStamm PartIV StructuralandFunctionalSequenceAnalysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281 15 AnIntroductiontoMultipleSequenceAlignment—andtheT-CoffeeShop. BeyondJust AligningSequences:HowGoodcanyouMakeyourAlignment,andsoWhat?. . . . . . . . . 283 StevenM. Thompson 16 ASpectrumofPhylogenetic-BasedApproachesforPredictingProtein FunctionalSites. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315 DukkaBahadurK. C. andDennisR. Livesay 17 TheRoleofTranscriptionFactorBindingSitesinPromotersandTheir InSilicoDetection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339 ThomasWerner 18 InSilicoDiscoveryofDNARegulatorySitesandModules. . . . . . . . . . . . . . . . . . . . . . . . 353 PanayiotisV. Benos PartV LiteratureMiningforAssociationandMeaning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367 19 MiningtheResearchLiteratureinSystemsBiology. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 369 KeirT. Reavie 20 GoPubMed:ExploringPubMedwithOntologicalBackgroundKnowledge. . . . . . . . . . . . 385 HeikoDietze,DimitraAlexopoulou,MichaelR