Split alignment. Martin C. Frith April 13, 2012
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1 Splt algnment Martn C. Frth Aprl 13, Introducton Ths document s about algnng a query sequence to a genome, allowng dfferent parts of the query to match dfferent parts of the genome. Here are some possble applcatons: Detectng genome rearrangements n cancer, by algnng DNA reads from cancer cells to a reference genome, and lookng for reads that span rearrangement breakponts. Detectng novel transposon nsertons, by lookng for DNA reads that span nserton boundares. Detectng trans-splcng, by algnng RNA reads to a genome, and lookng for reads whose algnment s splt between dsjont genome locatons. Splced algnment (algnment of cs-splced RNA to DNA) s an mportant specal case. 2 Local versus sem-global algnment Splt algnment can be ether local or sem-global. Sem-global means that all of the query must partcpate n the algnment. Local means that a contguous segment of the query s algned. 3 Scorng scheme I use the standard affne-gap scorng scheme, wth one addtonal parameter: a negatve jump score whch s ncurred when the algnment jumps from one part of the genome to another. For now, let us assume that the jump score s constant. Later, we wll consder usng dfferent scores for dfferent jumps. S(x, y): score for algnng genomc base x to query base y. a: gap exstence score. b: gap extenson score. (A gap of length k scores a + b k.) d: jump score. Note that a, b, and d are negatve. 1
2 4 Probablstc nterpretaton Algnment scores are often nterpreted as scaled log probabltes: score = t ln(probablty) (1) Here, t s an arbtrary scale factor. (If we multply all the score parameters by a constant factor, t makes no dfference to the algnment.) Ths may provde gudance for choosng the jump score. If the probablty of a rearrangement breakpont occurrng mmedately after any base s δ, the jump score s: d = t ln(δ/(2g)) (2) Here, g s the genome sze, and 1/(2g) s the probablty of jumpng to any one base on ether strand. 5 Mnmum matches on each sde of a jump Typcally, the match score S(x, x) for DNA s t ln(4). A maxmal-score local algnment can nclude a jump only f there are at least d/s(x, x) log 4 (2g/δ) matches on each sde of the jump. So we cannot detect jumps that are too near the edge of the query. Table 1: Mnmum matches on each sde of a jump δ g mnmum matches Exact algorthm Here s an algorthm that guarantees to fnd a maxmal-scorng splt algnment. For smplcty, let us consder a genome wth just one sequence and one strand. (The extenson to multple sequences and strands s straghtforward.) 6.1 Defntons G 1,..., G m : genome sequence of length m. Q 1,..., Q n : query sequence of length n. : ranges from 1 to m. j: ranges from 1 to n. 2
3 6.2 Local algnment W 0,0 = 0 (3) W,0 = 0 Y,0 = Z,0 = (4) W 0,j = 0 Y 0,j = Z 0,j = (5) X,j = max(w 1,j 1, W j 1 + d) + S(G, Q j ) (6) Y,j = max(w 1,j + a, Y 1,j ) + b (7) Z,j = max(w,j 1 + a, Z,j 1 ) + b (8) W,j = max(x,j, Y,j, Z,j, 0) (9) W j = max(w,j ) (10) Optmal algnment score = max,j (W,j) (11) Ths algorthm fnds the maxmum possble algnment score. In order to fnd an algnment that has ths score, we can perform a traceback, smlarly to standard algnment methods. Ths algorthm s smlar to the classc Gotoh algorthm for affne-gap algnment. If we set d =, t becomes dentcal to the Gotoh algorthm. The tme complexty s O(mn), whch s practcal for small genomes and small query datasets, but not for large genomes and mult-ggabase query data. 6.3 Sem-global algnment W 0,0 = 0 (12) W,0 = 0 Y,0 = Z,0 = (13) W 0,j = Y 0,j = Z 0,j = (14) X,j = max(w 1,j 1, W j 1 + d) + S(G, Q j ) (15) Y,j = max(w 1,j + a, Y 1,j ) + b (16) Z,j = max(w,j 1 + a, Z,j 1 ) + b (17) W,j = max(x,j, Y,j, Z,j ) (18) W j = max(w,j ) (19) Optmal algnment score = max(w,n ) (20) 3
4 7 Allowed postons for jumps In the precedng algorthms, jumps cannot occur mmedately before algnment gaps (nsertons or deletons). They can only occur mmedately before algned bases. Ths restrcton cannot prevent us from fndng the optmal algnment score, because a jump followed by a gap has the same score as a gap followed by a jump. Moreover, optmal algnments never have jumps adjacent to deletons (n the query relatve to the genome). Ths s because such a deleton could be absorbed nto the jump, mprovng the algnment score. 8 Fast heurstc algorthm To cope wth huge datasets, I propose a fast but nexact algorthm (for local splt algnment). Ths has two steps: 1. Fnd local, non-splt algnments between the query and the genome usng a fast algner such as LAST. Let us call these ntal algnments. 2. Fnd a maxmal-scorng splt algnment usng (parts of) the ntal algnments. Step 2 s descrbed here. 8.1 Defntons m: the number of ntal algnments. n: the length of the query sequence. Q 1,..., Q n : the query sequence. I,j B : 1 f ntal algnment begns at query base j, 0 otherwse. I,j E : 1 f ntal algnment ends at query base j, 0 otherwse. I assume that the ntal algnments never begn or end wth gaps (nsertons or deletons). For the next three defntons, conceptually convert the ntal algnments to sem-global algnments, by regardng unalgned query bases as nsertons. A,j : the algnment score for query base j n algnment. If t s algned to a genomc base x, the score s S(x, Q j ). If t s algned to an ntal gap, the score s a + b. If t s algned to a non-ntal gap, the score s b. D,j : the deleton score between query bases j and j + 1 n algnment. If there are no deleted bases then D,j = 0. Otherwse, f there k deleted bases, D,j = a + b k. I,j X : 1 f query base j s algned to a non-ntal gap n algnment, 0 otherwse. Fnally, we wll use logs of the ndcator varables: J B,j = log I B,j J E,j = log I E,j (21) 4
5 8.2 Algorthm W,0 = (22) W 0 = (23) W,j = max(w,j 1 + D,j 1, J B,j, W j 1 + d + a I X,j) + A,j (24) W j = max(w,j ) (25) Optmal algnment score = max,j (W,j + J E,j) (26) Ths algorthm only consders splt algnments that begn at the begnnng of one of the ntal algnments. Ths s because scores > can arse only when J,j B >. Lkewse, t only consders splt algnments that end at the end of one of the ntal algnments (by usng J,j E ). The algorthm does not consder jumps adjacent to deletons (n the query relatve to the genome). Ths restrcton s harmless, because jumps adjacent to deletons have sub-optmal score. It does consder jumps adjacent to nsertons. The tme complexty s O(mn), whch s no more than needed just to read the algnments. 9 Varable jump scores For splced algnment, we would lke to vary the jump score, to model ntron length dstrbutons and splce sgnals (such as GT-AG). Here s a modfcaton of the heurstc algorthm to allow ths. Unfortunately, the tme complexty ncreases to O(m 2 n). d k,,j : the score for jumpng from query base j n ntal algnment k to query base j + 1 n ntal algnment. W,0 = (27) W 0 = (28) M,j = max k,j 1 + d k,,j 1 ) k (29) W,j = max(w,j 1 + D,j 1, J,j, B M,j + a I,j) X + A,j (30) 5
6 Optmal algnment score = max,j (W,j + J E,j) (31) Ths algorthm also does not consder jumps adjacent to deletons. Unfortunately, ths restrcton s no longer harmless. For example, an adjacent deleton may permt a jump to use a consensus splce sgnal. It mght be possble to consder adjacent deletons wthn the calculaton of d k,,j. 10 Algnment ambguty Often, one query wll have several alternatve algnments that are almost as good as the best algnment. In other words, the algnment s ambguous. For many applcatons, however, we would lke to fnd (parts of) algnments that are hghly unambguous. To do ths, I use the probablstc nterpretaton of algnment scores to assgn a probablty to each algnment. We can then calculate varous margnal probabltes for parts of algnments. Algnment parts wth hgh margnal probabltes (e.g ) can be regarded as hghly unambguous. I assume that each algnment s probablty s proportonal to exp(s/t), where s s ts score. The key step s to calculate the normalzaton factor z, the sum of exp(s/t) over all algnments. The followng verson of the heurstc algorthm calculates z. a = exp(a/t) b = exp(b/t) d = exp(d/t) (32) A,j = exp(a,j /t) D,j = exp(d,j /t) (33) W,j = W,0 = 0 (34) W 0 = 0 (35) ( ) W,j 1 D,j 1 + I,j B + W j 1 d (a ) IX,j A,j (36) W j = (W,j) (37) z =,j (W,j I E,j) (38) 6
7 11 Lmtatons The heurstc algorthms do not realgn bases near jumps. For example, suppose the true algnment has a msmatch adjacent to a jump. The ntal algnment wll exclude the msmatch, because dong so mproves the local algnment score. The fnal algnment wll probably have a sngle-base nserton nstead of the msmatch. 7
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