Lecture 3: Biology Basics Continued · 2019. 8. 23. · Lecture 3: Biology Basics Continued Fall...

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Lecture 3: Biology Basics Continued Fall 2019 September 3, 2019

Transcript of Lecture 3: Biology Basics Continued · 2019. 8. 23. · Lecture 3: Biology Basics Continued Fall...

  • Lecture3:BiologyBasicsContinued

    Fall2019September3,2019

  • Genotype/PhenotypePhenotype:

    Blueeyes Browneyes

    Genotype:

    Recessive:bb Dominant:BborBB

  • •  Genesareshowninrelativeorderanddistancefromeachotherbasedonpedigreestudies.

    •  ThechanceofthechromosomebreakingbetweenA&CishigherthanthechanceofthechromosomebreakingbetweenA&Bduringmeiosis

    •  Similarly,thechanceofthechromosomebreakingbetweenE&FishigherthanthechanceofthechromosomebreakingbetweenF&G

    •  Theclosertwogenesare,themorelikelytheyaretobeinheritedtogether(co-occurrence)

    •  Ifpedigreestudiesshowahighincidenceofco-occurrence,thosegeneswillbelocatedclosetogetheronageneticmap

  • •  Pleiotropy:whenonegeneaffectsmanydifferenttraits.

    •  Polygenictraits:whenonetraitisgovernedbymultiplegenes,whichmaybeonthesamechromosomeorondifferentchromosomes.– Theadditiveeffectsofnumerousgenesonasinglephenotypecreateacontinuumofpossibleoutcomes.

    – Polygenictraitsarealsomostsusceptibletoenvironmentalinfluences.

  • Pleiotropyinhumans:PhenylketonuriaAdisorderthatiscausedbyadeficiencyoftheenzymephenylalaninehydroxylase,whichisnecessarytoconverttheessentialaminoacidphenylalaninetotyrosine.AdefectinthesinglegenethatcodesforthisenzymethereforeresultsinthemultiplephenotypesassociatedwithPKU,includingmentalretardation,eczema,andpigmentdefectsthatmakeaffectedindividualslighterskinned

  • PolygenicInheritanceinHumans•  Heightiscontrolledbypolygenesforskeletonheight,buttheir

    effectmaybeaffectedbymalnutrition,injury,anddisease.•  Weight,skincolor,andintelligence.•  Birthdefectslikeclubfoot,cleftpalate,orneuraltubedefectsare

    alsotheresultofmultiplegeneinteractions.•  Complexdiseasesandtraitshaveatendencytohavelow

    heritability(tendencytobeinherited)comparedtosinglegenedisorders(i.e.sickle-cellanemia,cysticfibrosis,PKU,Hemophelia,manyextremelyraregeneticdisorders).

  • Selection•  Somegenesmaybesubjecttoselection,whereindividualswithadvantagesor“adaptive”traitstendtobemoresuccessfulthantheirpeersreproductively.

    •  Whenthesetraitshaveageneticbasis,selectioncanincreasetheprevalenceofthosetraits,becausetheoffspringwillinheritthosetraits.Thismaycorrelatewiththeorganism'sabilitytosurviveinitsenvironment.

    •  Severaldifferentgenotypes(andpossiblyphenotypes)maythencoexistinapopulation.Inthiscase,theirgeneticdifferencesarecalledpolymorphisms.

  • GeneticMutation•  Thesimplestisthepointmutationorsubstitution;here,asingle

    nucleotideinthegenomeischanged(singlenucleotidepolymorphisms(SNPs))

    •  Othertypesofmutationsincludethefollowing:–  Insertion.ApieceofDNAisinsertedintothegenomeatacertainposition

    –  Deletion.ApieceofDNAiscutfromthegenomeatacertainposition

    –  Inversion.ApieceofDNAiscut,flippedaroundandthenre-inserted,therebyconvertingitintoitscomplement

    –  Translocation.ApieceofDNAismovedtoadifferentposition.–  Duplication.AcopyofapieceofDNAisinsertedintothegenome

  • MutationsandSelection

    •  Whilemutationscanbedetrimentaltotheaffectedindividual,theycanalso,inrarecases,bebeneficial;morefrequently,neutral.

    •  Oftenmutationshavenoornegligibleimpactonsurvivalandreproduction.

    •  Therebymutationscanincreasethegeneticdiversityofapopulation,thatis,thenumberofpresentpolymorphisms.

    •  Incombinationwithselection,thisallowaspeciestoadapttochangingenvironmentalconditionsandtosurviveinthelongterm.

  • RawSequenceData•  4bases:A,C,G,T+other(i.e.N=any,etc.)

    – kb(=kbp)=kilobasepairs=1,000bp– Mb=megabasepairs=1,000,000bp– Gb=gigabasepairs=1,000,000,000bp.

    •  Size: – E.Coli4.6Mbp(4,600,000)– Fish130Gbp(130,000,000,000)– Parisjaponica(Plant)150Gbp– Human3.2Gbp

  • FastaFile•  AsequenceinFASTAformatbeginswithasingle-line

    description,followedbylinesofsequencedata(fileextensionis.fa).

    •  Itisrecommendedthatalllinesoftextbeshorterthan80charactersinlength.

  • FastqFile•  Typicallycontain4lines:

    –  Line1beginswitha'@'characterandisfollowedbyasequenceidentifierandanoptionaldescription.

    –  Line2isthesequence.–  Line3isthedelimiter‘+’,withanoptionaldescription.–  Line4isthequalityscore.–  fileextensionis.fq

    @SEQ_IDGATTTGGGGTTCAAAGCTTCAAAGCTTCAAAGC

    +!''*((((***+))%%%++++++++!!!++***

  • CentralDogma

  • DiscoveryofDNA•  DNA Sequences

    –  Chargaff and Vischer, 1949 •  DNA consisting of A, T, G, C

    –  Adenine, Guanine, Cytosine, Thymine –  Chargaff Rule

    •  Noticing #A≈#T and #G≈#C –  A “strange but possibly meaningless”

    phenomenon. •  Wow!! A Double Helix

    –  Watson and Crick, Nature, April 25, 1953 – 

    –  Rich, 1973 •  Structural biologist at MIT. •  DNA’s structure in atomic resolution.

    Crick Watson

    1 Biologist 1 Physics Ph.D. Student 900 words Nobel Prize

  • Watson&Crick–“…thesecretoflife”•  Watson: a zoologist, Crick: a physicist •  “In 1947 Crick knew no biology and

    practically no organic chemistry or crystallography..” – www.nobel.se

    •  Applying Chagraff’s rules and the X-ray

    image from Rosalind Franklin, they constructed a “tinkertoy” model showing the double helix.

    •  Their 1953 Nature paper: “It has not

    escaped our notice that the specific pairing we have postulated immediately suggests a possible copying mechanism for the genetic material.”

    Watson & Crick with DNA model

    Rosalind Franklin with X-ray image of DNA

  • Superstructure

    Lodish et al. Molecular Biology of the Cell (5th ed.). W.H. Freeman & Co., 2003.

  • Superstructureimplications•  DNA in a living cell is in a highly compacted and

    structured state. •  Transcription factors and RNA polymerase need

    ACCESS to do their work. •  Transcription is dependent on the structural state

    – SEQUENCE alone does not tell the whole story.

  • RNA•  RNA is similar to DNA chemically. It is usually only

    a single strand. T(hyamine) is replaced by U(racil)

    •  RNA can form secondary structures by “pairing up”

    http://www.cgl.ucsf.edu/home/glasfeld/tutorial/trna/trna.gif tRNA linear and 3D view:

  • RNA,continued •  Several types exist, classified by function •  mRNA – carries a gene’s message out of

    the nucleus. •  tRNA – transfers genetic information from

    mRNA to an amino acid sequence •  rRNA – ribosomal RNA. Part of the

    ribosome machine.

  • Protein

    •  A polymer composed of amino acids. •  There are 20 naturally occurring amino

    acids.

    •  Usually functions through molecular motion or binding with other molecules.

  • Proteins:PrimaryStructure

    •  Peptidesequence:– Sequenceofaminoacids=sequencesfroma20letteralphabet(i.e.ACDEFGHIKLMNPQRSTVWY)

    – Averageproteinhas~300aminoacids– Typicallystoredasfastafiles

    >gi|5524211|gb|AAD44166.1| cytochrome b [Elephas maximus maximus]LCLYTHIGRNIYYGSYLYSETWNTGIMLLLITMATAFMGYVLPWGQMSFWGATVITNLFSAIPYIGTNLVEWIWGGFSVDKATLNRFFAFHFILPFTMVALAGVHLTFLHETGSNNPLGLTSDSDKIPFHPYYTIKDFLGLLILILLLLLLALLSPDMLGDPDNHMPADPLNTPLHIKPEWYFLFAYAILRSVPNKLGGVLALFLSIVILGLMPFLHTSKHRSMMLRPLSQALFWTLTMDLLTLTWIGSQPVEYPYTIIGQMASILYFSIILAFLPIAGXIENY

  • NaturallyOccurringAminoAcids

  • Proteins:SecondaryStructure

    •  Polypeptidechainsfoldintoregularlocalstructures– Commontypes:alphahelix,betasheet,turn,loop– Definedbythecreationofhydrogenbonds

  • Proteins:TertiaryStructure

    •  3Dstructureofapolypeptidesequence–  interactionsbetweennon-localandforeignatoms

  • Proteins:QuaternaryStructure

    •  Arrangementofproteinsubunits

  • Conclusions

  • ChallengesinBioinformatics

    •  Needtofeelcomfortableininterdisciplinaryarea

    •  Dependonothersforprimarydata•  Needtoaddressimportantbiologicalandcomputerscienceproblems

  • BasicStepsinBioinformaticsResearch

    1.  Datamanagementproblem:storage,transfer,transformation(InformationTechnology)

    2.  Dataanalysisproblem:mapping,assembly–  algorithmscaling(ComputerScience)

    3.  Statisticalchallenges:traditionalstatisticsisnotwellsuitedformodelingsystematicerrorsoverlargenumberofobservations(Biostatistics)

    4.  Biologicalhypothesistesting–  datainterpretation(LifeScience)

  • BasicSkills

    •  Artificialintelligenceandmachinelearning•  Statisticsandprobability•  Algorithms•  Databases•  Programming•  Biology/Chemistryknowledge

  • Genomics:-  Assembly-  Detectionofvariation-  GWAS

    RNA:-  Geneexpression-  Transcriptomeassembly-  Pathwayanalysis-  RNA-RNAinteraction

    Protein:-  Massspectrometry-  Structureprediction-  Protein-Protein

    interaction