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by: Reed Aufderhar


Reed Aufderhar
Utah State University
GPA 3.85


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Class Notes
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This 44 page Class Notes was uploaded by Reed Aufderhar on Wednesday October 28, 2015. The Class Notes belongs to CS 5890 at Utah State University taught by Staff in Fall. Since its upload, it has received 7 views. For similar materials see /class/230455/cs-5890-utah-state-university in ComputerScienence at Utah State University.

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Date Created: 10/28/15
Ancillary lectures 1 Computational analyses of human disease mutations continued Understanding additional variation within proteins Previous analyses Focused on understanding patterns associated with disease mutations from an evolutionary perspective In those analyses we effectively treated all amino acid sites in the multiple sequence alignment equally But are all parts of the protein similar NO Genes frequently have different domains that perform different roles associated with the overall function of the protein The CFTR protein is part of a general category of proteins called ion channels More specifically CFTR regulates Cl39 transport across cell membranes M301 M302 h hh l gugdnnm 1 PM 2 ATP 393 FIG 1 Model showing proposed domain structure of cystic bmsis transmembrane conductance regulator CFTR MEI membranespan ning domain NED nucleotidebinding domain H mgt ator domain FHAa cADrIPdependent protein kinase Over 900 diseaseassociated human genes have been identi ed over 1800 different putative types of domains have been identi ed in human genes JimenezSanchez et al 2001 A hypothesis Since different regions within a gene vary in their roles with respect to overall gene function amino acid sites in different domains within a gene may vary in their ability to produce a given disease phenotype if mutated Genes examined in this study E k r rm m x pmquot 1k Ham as 3 1 mp WW mmquot M lmlku m rm Two different null hypotheses were constructed Mutations are distributed uniformly among domains Mutations are distributed among domains according to evolutionary expectations Alternative hypothesis Since different regions within a gene vary in their roles with respect to overall gene function amino acid sites in different domains within a gene may vary in their ability to produce a given disease phenotype if mutated Uniform distributions Domain Undesignated region 1 Membrane spanning 1 Undesignated region 2 Nucleotide binding 1 RDomain Undesignated region 3 Membrane spanning 2 Undesignated region 4 Nucleotide binding 2 inclusive amino acid sites 149 50404 405440 441684 685831 832849 8501 150 1 1 511226 12271480 We need to account for size variation of domains Simplest model the number of human replacement mutations in a domain is a function of the number of amino acids in the domain I I I I I I I I I l M801 llNBo RD 111 M802 IINBDZI UND1 UNDZ UND3 UND4 Correct bl First beau mu GUI G GUA cue U Phanylalanirla Phenylalanine Laueine Leucine Laucine Leucine Leucine Laucine tseleucina isolaucina lmleuclne Start Methaninel llama Va ina Vallila Vanni Cth r39l A lir39lO mild NO but it s close Strictly speaking we need to account for properties of the genetic code and quantify the number of replacement sites in each domain UCU up UCG ECU ccc CCA DEG ECU ACE Smcmd hilt C Sarina Senna l Sarina Sarina Prn na Proms Prchne Pmllina Thrannina Threunina GA T39hreanine A ACE 39GCU ECG GCA GGG Threanine Alanine Alanine Alanine Alanine UAU U C UM LINE CAU CWO BAA BAG M U MD M Ma GA U GAG GAA GAG A Tymsina Tyrmlm Stop Stop Histidine Htslivzline Glutamme Glutamia39is Asparagine Asparagine Lyme Lys zna Aspartnc Acid Asparlic Acid Glutam Acid Glulamzc Mid CcDynghtEQDD Pearacn F meenlfca Hall Inc G Cystaina Ststains Stop Trypluphan Arginina Arginlne Arginine Arginine Sarina Sarina Afglmil39llft Arginine Glycine Glycine Glycine Glycine Thind base DFDC DFUC 13311 Dram Each codon has a different number of replacement sites An expectation for the number of replacement mutations in domainj 1 Assume that R is the total number of replacement sites in the gene where D total human replacement mutations have been observed 2 If gene regionjcontains Rj replacement sites then on average we expect to observe the fraction Rj R of the D total human mutations within region j Djexpected Rj R X D Note that Sj and S can be substituted for Rj and R when silent mutations are being analyzed A more complex model the number of human replacement mutations in a domain is a function ofthe number of conserved and variable amino acids in the domain of disease mutations in on o m o c observed expected Site variability ie the observed number of mutations at a site of type i where 139 reflects the site variability category from analyses of the whole gene can also be used to derive expectations for the number of human replacement mutations in a given domain Calculating expected number of replacement mutations for domainj under the evolutionaryinfluenced distribution 1 If domainj has aij sites of type i 2 And if there are a sites of type iwithin the whole gene 3 And if D is the total number of amino acid altering mutations found at sites of type i in the gene then 4 We expect gene region j to have a a X Dof the D total replacement mutations of type ipresent within it xP ted lid film x E yb tr39y d t H a Summed over sites of type i we obtain the total expected value for region j Relationships between two models 514 anquot r 439 p14 u U Random 39 expectations L112 n Evolutionary a 1211 expectations 39D EJEI a Eij ii of mutations on a per replacement she hams Elll DU I12 04 035 LEE JS39 392 11 quot 5 Average nun39txer of mm mrm per site in region Average site variability in domain Some results analyses of silent mutations with respect to the uniform distribution model 15 r 39 39 39 39 39quot is U I 12 CFTR m Tso2 Fquot E quot z E mi 2 4 1 5 L r w 1 u oft II 39 i 39 r39 I V I I quota 9r a E 77 1 7 9 7 E mumsm quot5391quot U ll 39i39ifn No significant difference from the uniform distribution model What about disease associated mutations Inserted E391Jvlliu39 r y Elp l l tsr39ls i lnirum Expe a ane G6PD type i39 The data are a better fit to the evolutionary influence model relative to the uniform distribution but nonetheless show significant departures from both modelsllll of disease mutations Gene region domain Suggests a nonadditive effect of site variability In other words a variable amino acid site may be more likely to produce disease when found in an overall more conserved gene region domain An unusual pattern for PAX6 filtlbei a39eu 3939 iE1nuniiunquotvay Ei39ili1iI39I39 ilriiha rn Exp lmiyna of disease mutations I U i HI I I EII LL Gene region Underabundance ofdisease The homeodomain HD of PAX6 is glutath in most conserved extremely conserved only a single oma39n amino acid change found in domain among all sequences in MSA Are mutations within HD possibly lethal What about polymorphic replacement mutations Again being evaluated relative to the uniform mutation distribution rm he lpa Ti39l39u itl39i mummyquot rr39u aiarrs An overabundance of polymorphic mutations in NBD1 Why Possible selective advantage to some mutations Could possibly explain maintenance of HIGH deleterious allele frequencies in human populations Most common CFTR mutation AF508 Deletion of a phenylalanine at position 508 of the normal CFTR protein produces cytic fibrosis Position 508 is located within MSR1 where overabundance of polymorphic mutations were detected Laboratory work has illustrated that the AF508 mutation is associated with resistance to typhoid fever some bacterial intestinal diseases and reduced incidence of asthma ls resistance to disease also the reason for the presence of more polymorphic human mutations in MSR1 A selective advantage CS 5890 Installing and Using R and BioConductor For gene expression analysis7 we can use a variety of tools in R These notes are to help students install R on their own computer or CD or ash drive7 and then run a few simple commands Installing R Run the executable le available at the following website httpCranr projectorgbinwindowsbaseR 231 win32exe Follow the installation wizard that pops up Note that you will probably choose to follow the English directions and accept the installation agreement It7s probably best to select the default User Installation77 rather than customizing If you want7 you can put R on the start menu and create a desktop icon This will all take just a few minutes Note that you can install R on a CD probably best to use a rewritable CD with nothing else on it or a ash drive To do this7 install R on a campus computer7s desktop or somewhere else where you can get to the installed les7 and dont worry about creating a start menu folder or desktop icon Once this is done7 access the binRguiexe le in the created folder You might want to create a shortcut from this le to somewhere else in the created folder you can easily access Then copy the entire created folder to your CD or ash drive This will allow you to run R on any machine that can read your disk Note that some older machines might not be able to read rewritable CD7s7 but that shouldnt be a problem in any student computer lab on campus Running R A Simple Example You can run R for Windows by either double clicking on the desktop icon or the shortcut you created to access the binRguiexe le in the folder created during installation This will open up an interactive environment where we will be using many features of R Use the Introduction to R77 pdf le available through manuals portion of the help menu in R as a good reference for the basic syntax of common commands in R The search facility through GRAN httpCranr project org is also very useful Consider the following simple example to look at a few commands in R Suppose we have midterm and nal exam scores for ve students Let x be the midterm score and y be the nal score Student 1 2 3 4 5 Midterm 50 66 72 75 92 Final 65 65 89 79 97 To de ne these in R use the following commands id lt c12345 x lt C5066727592 y lt C6565897997 This will create three vectors named id x and y Think of a lt b as being an arrow assigning the right hand side b value to the left hand side name a Note that we can type these commands directly into R or type them in a text editor like Notepad where we can save them for later use If we have commands in a text le we can either copy and paste them into R or we can reference them using the source command more on that later Suppose instead that the data were in some external le that we wanted to read in For exam ple suppose the data are in the comma delimited le found in adatafilesscores csv with three columns labeled student midterm and nal Then the following commands would do the same as the commands given above dataset lt readtablequotadatafilesscores csvquot sepquot quot headerT id lt datasetstudent X lt datasetmidterm y lt datasetfinal Note that the is like a pointer to the variable inside the data set Look at a simple graphical representation of these data using a scatterplot by using the plot command plot Xymain Scores Xlab Midterm ylab Final pch16 plot Note that the main xlab ylab and pch are options to affect the appearance of the plot and the 7 will give a pop up window with a description of the plot command and its options As an aside the graphical abilities of R are one of its great strengths You can create very nice graphics in a variety of formats type Devices for more We could add points or lines to the plot by doing the following points XC6060 yc7075 pch1col red abline v70 col7blue7 lty2 Note that col and lty are options to affect the appearance of the plot The v is for a vertical reference line use h for horizontal We will use the square brackets l to subset objects in R For example suppose we wanted to print out the nal scores for all students who scored more than 70 on the midterm t lt X gt 70 Then t is a vector of TRUEFALSE yt Or do it by hand y yc35 Note that the semicolon allows us to put multiple commands on a single line7 while the pound sign comments out a line It is straightforward to program77 in R using such approaches as for loops and if then statements For example7 suppose that we wanted to de ne a new variable called high that is 1 if the student scored the highest nal score and 0 otherwise There are several ways to do this Approach 1 high lt rep05 So high is a vector of 0 s repeated 5 times fori in 15 if y i maX 31 highi lt 1 Approach 2 high lt repO5 t lt ymaxy hight lt 1 Approach 3 high lt rep05 highwhichmaxy lt 1 Note that in general7 explicit for loops and if then statements are relatively inef cient time wise Things will usually work fastest if we can do them with straightforward matrix manipulations We can also write functions to do speci c things Here is a simple function called sqmed that takes a vector and returns the square of the median sqmed lt functionv med lt medianv medsquared lt med 2 return med squared Call this function sqmedx Note that we could include other features in this function7 such as a check that the argument v is in fact a vector To quit R type q and for now click No7 when asked if you want to save the workspace image Equivalently type q no lf you7ve done a lot of work that would take a long time to run again and you will want to pick up where you left off in another R session you can save the workspace image either by saying Yes7 to this option or by an explicit command in R either way these RData les can be quite large save f ilequota worksinprogresshistoryname RDataquot Then read this back in later using loadf ilequota worksinprogresshistoryname RDataquot Bioconductor Packages There are many automatic functions that come with R however most of what Well use in this class will require specialized functions dealing with gene expression data These func tions are included in separate packages that must be downloaded and installed Once you have installed R you will need to get the packages necessary to do the analyses for this course You can get them one at a time as needed during the course This is why you7d want to use a rewritable CD if you7re going that route for installing R For example suppose you know you need the R package called affy either because you know what it does or because you tried to do something in R and R stopped and told you it needed this package There are many ways to get packages such as installing them using the drop down menus in R However you can use a pre written function to get most packages Sysputenvquothttpproxyquotquothttpproxyusuedu80quot Sysgetenvquothttpproxyquot These first two lines may be necessary on campus to access online resources sourcequothttpwwwstatusuedu jrstevensgetpackagesRquot needpkgs lt cquotaffyquot or cquotaffyquotquotvsnquot for example getpackagespkgsneedpkgs Note that the source command will go to the referenced le and run the code there in R Running this the rst time may take a minute or two because R will also go install other dependent packages Once a package has been downloaded and installed it will only be used in an R session if you explicitly request that it be loaded to save on memory space For example if we want to look at an image of a particular microarray we would rst need to load the affy package using the library function and then call the necessary functions To see which CEL les are available in a directory libraryaffy cels lt listcelfilesquotC folderquot cels To read in particular CEL les dataO lt ReadAffyfilenamesquotCfoldercelfileCELquot data1 lt ReadAffyfilenamesCquotCfolderce11CELquot quotCfoldercel2CELquot data2 lt ReadAffyCelfilepathquotCfolderquot lengthdata2 This creates an A yBatch object in R lts length tells how many arrays are in it To select a speci c array from the A yBatch object array1 lt data11 To look at the array image with a title as given imagearray1main mouse array image To look at gene names probe set names gn lt geneNamesdata1 gn1 Name of first gene pn lt probeNamesdata1 pn113 Names of first 13 probes But how does R know which genes or probesets are on this array We need a decoder ring77 Probe information is available in CDF les R will automatically download and install the necessary packages when it encounters a CEL le Alternatively you can download and install the packages yourself7 by hand You can also create your own CDF le for custom arrays To look at intensities for speci c probe sets and to put them together in one matrix pmgn1 lt pmdata1gn1 mmgn3 lt mmdata1gn3 mat lt Cbindpmgn1mmgn3 colnamesmat lt C7pm1gn1 7pm2gn1 7mm1gn3 mm2gn3 mat A few graphical summaries of these data four arrays here provides motivation for normal ization libraryRColorBrewer May need first getpackagespkgsquotRColorBrewerquot parmfrowc22 cols lt brewerpal4 quotSet3quot boxplotdata2colcolsnames14 histdata2 colcols XlabquotLog2 intensitiesquot lwd2ltyc1121 legend120214colcolslwd2ltyc1121 Biosinformatics 39 39 O U z 0 Problems 8 Q j 0 Solutions I 55990 5 Summerzn MW121uu20 39 M 39 117 am hequot niwxumn 1 HIV in quotJogquot ln l l lt l Instructors Charles Yan Computer Science cyanccusuedu Bart Weimer Center for Integrated BioSystems bcweimerccusuedu Mark Miller Department of Biology MarkMiIIerusuedu John Stevens Math and Statistics JohnRStevensusuedu Bioinformatics Bioinformatics is the field of science in which biology computer science and information technology merge into a single discipline The sum of the computational approaches to analyze manage and store biological data Bioinformatics involves the analysis of biological information using computers and statistical techniques the science of developing and utilizing computer databases and algorithms to accelerate and enhance biological research Bioinformatics is the analysis of biological information using computers and statistical techniques Bioinformatics is more of a tool than a discipline the tools for analysis of Biological Data The use of computer science mathematics and information theory to model and analyze biological systems especially systems involving genetic material The mathematical statistical and computing methods that aim to solve biological problems using DNA and amino acid sequences and related information Source various web sites Some generalizations Bioinformatics involves biology statistics and computers Bioinformatics research most generally involves understanding genetic systems DNA RNA proteins Basic terminology Genetics 101 Genome all of the genetic information contained in an organism In general each cell in an organism has a complete copy of the entire genome Genomes are comprised of chromosomes Chromosomes large physical aggregations of DNA found within cells Chromosome numbers vary among different species ex humans have 23 pairs of chromosomes Chromosome numbers in some plants Chromosome numbers in some animals Plant Species 1 Species Species Arabidopsis 10 Fruit y 8 Guinea Pig 16 Rye 14 Dove 16 Snail 24 Maize 20 Worm 36 Fox 36 Einkorn wheat 14 Cat 38 Pig 38 Pollard wheat 23 Mouse 40 Rat 42 Bread wheat 42 Rabbit 44 Hamster 44 Wild tobacco 24 Hare 46 Human 46 Cultivated tobacco 48 Ape 43 Sheep 54 Fern 1200 Elephant 56 Cow 60 Donkey 62 Horse 64 Dog 78 Chicken 78 Carp 104 Butterflies 380 Basic terminology Genetics 101 cont Locus specific physical location on a chromosome The term Locus or loci plural is o en used to refer to the individual genes that play speci c roles for organismal func ion The term gene is sometimes used synonymoust with locus Alleles the specific DNA sequence variants observed at IOCI loci are extremely diverse ie there are lots of different alleles that may he observed in a sample of individuals from a popula ion Tyeterm gene is sometimes used synonymoust with a e e Q Single chromosome Chromosomes in cell Basic Mendelian Genetics In a typical diploid animal progeny from a cross contain one maternally and one paternally inherited set of chromosomes If organism has 2 identical copies of an allele at a locus they are homozygous If an organism has 2 different copies of an allele at a locus they are heterozygous Basic Mendelian Genetics cont male x female crosses A homozygous male and a homozygous female that contain different alleles Female B Male X Female 0A AB AB AA x BB g A AB AB All progeny are heterozygous Basic Mendelian Genetics cont male x female crosses Two heterozygous parents Female Male X Female 0A AA AB AB x AB g B AB BB 1A of the progeny are homozygous AA Half of the progeny are heterozygous 1A of the progeny are homozygous BB Basic Mendelian Genetics cont male x female crosses One heterozygous parent and one homozygous parent Female Male X Female 0A AA AB AA x AB g A AA AB Half of the progeny are homozygous Half of the progeny are heterozygous Basic Mendelian Genetics cont male x female crosses A homozygous male and a homozygous female that contain the same alleles Female A Male X Female Male AA X AA A All progeny are homozygous Mendel published his work in the Transactions of the Brunn Society of Natural History in 1866 Mendel s work was rediscovered in 1902 by William Bateson Round 039 Wmlkl d ripe 385d v V zl mow or gmn seed lnlariors Purple or whlla news I Innaiea or pinched ripe nods O O Gregor Mendel Long or short mm Axial ov luminll wars From DNA to proteins DNA duume warmed mmecme mum m chmmusumes Ymvsmmm mammary Nw m RNA smmewandedmmecmemc s mnmmnmm mm Pvmem na mum DNA 51mm mam mmawvs WWW daub thehx 151m swims u mum 5 a n gm mm mm nun RNA Palynmrue Inmanon T Elongation Termination u gt W lniarma39iion tow rnFiNA Note that Uracil U is substituted for Thymine T in the synthesized RNA strand Transcription Example R A E G T C If 7 J I i V TE W 3 L U 5 ll 393 3 C quot Translation from RNA to protein Codon l Codon 2 Codon 3 Codon 4 Codon 5 G u U o c A G G c U r I U x L quotW H 7 W W i Cysteine Sarina The Universal genetic code Suuv rdlhlw Fival TifJ um U 5 n hm UUU phengulsnne LIEJ 11an um thymus Grimm Li I IJUE Fmgahnrw um Sum Ll C FW D UGC trauma 5 J ULl Ha SIM LIAl Sim UGA 51411 d IJLlB Lunch U03 Sana um aim UGG Tvyclanv39nn a Leoztro CW FmInz GIALI n m WEH WE L LinusH I201 Funn L34 Huidim Anirru 3939 m From 301 caugan MalPI A COG Purim DI mman m rh 1 at m u unujmu 5quot L A M NSC MK Matron 5gth 1 RM lsnnxnn ACA man man harm a Air sxmmIxmnq Am AM wm Arqrrll z 39Uii 1mm EGLI maan U r mm GED marine t a c 3911 win an Manna AM alumnaad Kn 139quoter 1 ELIE Vilma GEE Mler ENE GuiarncA d EGG GMT 5 CD51 NIHquot Jon Not so universal The Standard Code The Vertebrate Mitochondrial Code The Yeast Mitochondrial Code The Mold Protozoan and Coelenterate Mitochondrial Code The Invertebrate Mitochondrial Code The Ciliate Dasycladacean and Hexamita Nuclear Code The Echinoderm and Flatworm Mitochondrial Code The Euplotid Nuclear Code The Bacterial and Plant Plas id Code The Alterna ive Yeast Nuclear Code The Ascidian Mitochondrial Code The Alternative Flatworm Mitochondrial Code Blepharisma Nuclear Code Chlorophycean Mitochondrial Code Trematode Mitochondrial Code Scenedesmus Obliquus Mitochondrial Code Thraustochytrium Mitochondrial Code Note that most codes differ from one another only slightly Invertebrate Mito Standard AGA Ser Arg AGG Ser Arg AUA Met e UGA Trp Ter httpwwwncbinlmnihgovTaxonomyUtilswprintgccgimodec a u A 32 x a rag Dmvemumamsms 4 l w 3 canhmmmm 519 s mauvesumesame i w lt15 15 humumguuswmlem 5 A x m am mum Mamms lt5 A variety of questions HEW mam 927125 m an umamsm s uenum Huwdunwuen an 2W enemnctmnse hawansu hauenumeav mews u mumWsma anangemgmmggngm chmmusumes Huwduestmsananuememvawamun spamesv HEW ave ummm genes Emma Wm WM cwcum ancesuvemwunmema cundnwunw gamma 2mmquot m mm genesv n n Huw Imam vanauun m gene expressway andu mmem 51mm a ect an Dvuamsm s phenuwpw oanen mu 9mm wanud gene lt lt lt 1m yd mun 35m cycle lemplale DNA 5 z M 4 cuplns x topics m cupiv 32 cnpla 2 am when mpics mm W 39 J m mm READING DNA DEFERMINING DNA SEQUENCE I ian usingddG I ian usingddc I ian usingddA I ian usingddT Modified nucleotides that prevent additional molecule extension 3 5 I bottom ddG dedC T A deCddA synthesis ADD deCdAddT tempiate A T mp5 iuw ddXTP s T A DNAPDWWE etc c G A T e r 5 5 Scan gei by iaser excitation and uorescence emission aimw i m agrm iBTR IBKG39E EL IR TTCRC ll YlEL GYIEEGEVSIEYECCCGEYII 23 25 200 T 220 D 210 Whole genome sequencing Onigirml DNA E fragmc ms 1 Break up genome into small fragments 2 Sequence fragments man i mung 2 rmsemtls E ngmuiLs 7 AA A ACFCGCC39I GCTI ATCA ACCGATCCCCCGCTACCFFCFACAGCCATCA39HT AAAACICLlCCl Ul IA39LCAAdCGAICKECCGCt AL lXl ACUCCA I CA39IT 3 Assemble fragments based on sequence similarity a computationallyintensive procedure many different approaches Other types of genetic DNA data you may encounter Single Nucleotide Polymorphisms SNPs Variation A C C GTGTC G C Used for general genotyping Va anon a purposes or for genetic mapping G T s r c c T e c l Minisequencing approaches for characterizing SN Ps Individual l ATGCTATGCTATCTTTATGCTAAATGG Individual 2 ATGCTATGCTATCTTTATGCTAACTGG Modified nucleotides A C T G Primer TACGATACGATAGAAATACGATT T lndividual l ATGCTATGCTATCTTTATGCTAAATGG Primer TACGATACGATAGAAATACGATTG Individual 2 ATGCTATGCTATCTTTATGCTAACTGG Other types of genetic data genetic markers Microsatellites otandomlyrepeated stretches of DNA may be dinucleotide trinucleotide tetranucleotide etc oMicrosatellite loci have high mutation rates and are highly variable 7 jEnucl tide repeat 15 GEA s Used for general genotyping genetic mapping forensics WNW mmmmv A few notes on genetic mapping Humans 46 chromosomes Some ferns have gt120 chromosomes Create a genetic mapping population using strains that differ in a phenotype of interest Parental strains A B Backcross 39 progeny F1 progeny F2 progeny unvmauwu Wm 15min n Alma595a IEI gzmw a u m m AWWSW 2 1 Anmwumao u A wmmmsn 55551235 54 uasmwc may m I K L i Ahmawla mammm MIItEMMS39 H mm Annamamon vs Nun1517 quotst509 mm m s 2 umsuo A4st


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