Bioinformatics. Four Basic Types of Mutations. Why is an understanding of sequence variation important?

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1 Bioinformtis Sequene Alignment Spring 8 Why is n understnding of sequene vrition importnt? Sequene lignment, bsed on n understnding of sequene vrition, is one of the most fundmentl phenomen explored by bioinformtis dt mining, round whih mny tools re built Alignments llow reserhers to ompre gene (nd protein) sequenes in order to: infer the funtion of genes infer the struture of genes nd gene produts infer the evolutionry history of genes nd orgnisms identify vrition responsible for disese nd other omplex phenotypes Where does nuleotide sequene vrition ome from? Muttions re spontneous hnges in sequene used by replition (or other) errors. Muttion rtes vry, nd n be influened by mny ftors. There re three min types: Substitution Insertion/deletion (indel) Inversion Rtes of muttions re influened by: Substitution lss (trnsition/trnsversion) Coding site (synonymous/nonsynonymous) Length of insertion/deletion Codon usge bis Nuleotide onsist (GC ontent) Stbility & fte of vrition depends upon: Drift Seletion (positive Drwinin/purifying, sexul, rtifiil) Other muttions (reversions re not unommon) Four Bsi Types of Muttions A. Substitution: Thr Tyr Leu Leu ACC TAT TTG CTG ACC TCT TTG CTG Thr Ser Leu Leu Thr Tyr Leu Leu ACC TAT TTG CTG ACC TAC TTT GCT G Thr Tyr Phe Al B. Deletion D. Inversion Thr Tyr Leu Leu Thr Tyr Leu Leu ACC TAT TTG CTG ACC TAT TTG CTG ACC TAT TGC TG Thr Tyr Cys C. Insertion ACC TTT ATG CTG Thr Phe Met Leu

2 Sequene Vritions How do we detet sequene vrition? The lol lignment inludes only regions of identity (or strong similrity). The fvors finding onserved regions. The globl lignment is strethed over the entire sequene length, inluding s mny mthes s possible. Sequene lignment theory nd prtie How to perform lol nd globl lignments Aligning by hnd nd by omputer progrm Hndling gps, missing informtion nd other problems Using known reltionships to improve nd test lignment preditions Pir wise Alignment A DNA sequene of length N n be viewed s subset of strings generted from set of lphbets {A, C, T G} A best lignment my be viewed s optimiztion problem of mximizing sore of ligning the pir Soring sheme Mthing hs rewrd () Mismth hs penlty () Mthing with gp lso hs penlty ()

3 Fiboni Sequene fib()= nd fib() = fib(n) = fib(n) + fib(n) Reursive lgorithms Fiboni(int N) { If (N == ) Return ; If (N == ) Return ; Return Fiboni(N) + Fiboni(N); } Bottom up pproh Fiboni(int N){ If (N == ) Return ; If(N==) Return ; FibN=; FibN=; For (I= to N) Do { Fib = Fib + Fib; Fib = Fib; Fib = Fib; } Return Fib; } Sequene Comprison To determine whih prt of the sequene re like nd whih prts differ. Similrity nd Alignment Alignment between pir of sequenes insertion of spes long the sequenes end up with the sme size Soring A mth (identil hrter) + A mismth (different hrter) A spe in olumn Exmple (totl sore of 9*+*()+*() =6) GACGGATTAG GATCGGAATAG Dynmi Progrmming A tehnique often used to solve ombintoril optimiztion problem A problem is deomposed nd the optiml solutions to the deomposed problems re reomposed to obtin the optiml solution to the originl problem. It is bottom up pproh: solutions to the deomposed problem is found first nd the finl solution is the problem is obtined by using the solutions to the subproblems

4 Applition Compre two strings s (size m) nd t (size n) We deompose the strings into prefixes of size,,, et nd ompute the sore of the strings from the solutions of the prefixes s hs m+ prefixes nd t hs n+ prefixes Consider the best sore of prefixes s[..i] nd t[..j] Best of s[..i] nd t[..j] +p(s[i],t[j]) or Best of s[..i] nd t[..j]+p(,t[j]) or Best of s[..i] nd t[..j] +p(s[i],) Representtion Let is two dimensionl rry with interprettion A[i,j]: the best sore of s[..i] nd t[..j] A[i,j] = mx of A[i,j]+ p(s[i],t[j]) (dig) A[i,j] + p(,t[j]) (horiz) A[i,j] +p(s[i],) (vertil) A[m,n] hs the sore of the best lignment Where p(,b) is the soring of the hrter nd b 4

5 A[i,j] = mx of g A[i,j]+ p(s[i],t[j]) (dig) A[i,j] + p(,t[j]) (horiz) A[i,j] +p(s[i],) (vertil) Mth Gp Mis mth g t g t g t 4 g g t g g A[i,j] = mx of A[i,j]+ p(s[i],t[j]) (dig) A[i,j] + p(,t[j]) (horiz) A[i,j] +p(s[i],) (vertil) Mth Gp t Mis mth 4 g g t 7 Lol Alignment Given the sequenes S nd T, lol lignment finds the subsequenes nd b of S nd T respetively whose similrity is mximum over ll suh pirs of subsequenes. Motivtion When long strethes of nonoding DNA re ompred Proteins from different fmilies often shre the sme funtionl nd struturl subunits Ide Suffix of S[..I] (possibly empty) is ligned with the suffix b of T[..j] (possibly empty) suh tht the sore is mximum over ll the suffixes of S[..I] nd T[..j] Lol Alignment Sore: mismth, mth, mth with spe The bsi steps of the lgorithm Find the mximum similrity between the suffixes of S[..i] nd T[..j] Disrd the prefixes whose prefixes S[..i] nd T[..j] whose similrity is less thn Find the best index I* nd j* of S nd T respetively fter whih similrity dereses. 5

6 Lol Alignment Initilize A[i,] = nd A[,j] = for ll i nd j A[i,j] = mximum of, A[i, j] + p(s[i],t[j]), A[i, j] + p(,t[j]), A[i, j] + p(s[i],) Compute A[i*,j*] so tht A[I,j] mximum over ll the rnges of I nd j A[i,j] = mx of A[i,j]+ p(s[i],t[j]) (dig) A[i,j] + p(,t[j]) (horiz) A[i,j] +p(s[i],) (vertil) Mth Gp Mis mth g t g t Lol Alignment 4 g g t g t End freespe Alignment The lignment t the beginning or the end ontribute weight Initilize A[i,] = nd A[,j] = for ll I nd j For i in to n nd J in to m A[i,j] = mximum of A[i, j] + p(s[i],t[j]), A[i, j] + p(,t[j]), A[i, j] + p(s[i],) Look for A[n,i*] tht is the mximum of A[n,I] for ll I Look for A[j*,m] tht is the mximum of A[j,m] for ll j* The similrity is defined s mx of A[n,i*] nd A[j*,m] 6

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