A Compressing Method for Genome Sequence Cluster using Sequence Alignment

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1 A Compressng Method for Genome Sequence Cluster usng Sequence Algnment Kwang Su Jung 1, Nam Hee Yu 1, Seung Jung Shn 2, Keun Ho Ryu 1 1 Database/Bonformatcs Laboratory, Chungbuk Natonal Unversty, Korea 2 Dvson of IT, Hanse Unversty, Korea 1 {ksung,nam,khryu}@dblab.chungbuk.ac.kr, 2 expersn@hanse.ac.kr Abstract After dentfyng the functon of a proten, bologsts produce new useful protens by substtutng some resdues of the dentfed proten. These new protens have hgh sequence homology (smlarty). We defne a sequence cluster as a cluster that s consttuted of smlar sequences. As another example of a Sequence Cluster, we consder a SNP (Sngle Nucleotde Polymorphsm) Cluster. A SNP s a DNA sequence varaton occurrng when a sngle nucleotde n the genome (or other shared sequence) dffers between members of a speces (or between pared chromosomes n an ndvdual). We suggest a new compressng technque for these sequence clusters usng a sequence algnment method. We select a representatve sequence whch has a mnmum sequence dstance n the cluster by scannng dstances of all sequences. The dstances are obtaned by calculatng a sequence algnment score. The result of ths sequence algnment s utlzed to author converson nformaton called an Edt-Scrpt between the two sequences. We only stored representatve sequences and Edt-Scrpts of each cluster nto a database. Member sequences of each cluster can then be easly created usng representatve sequences and Edt-Scrpts. 1. Introducton When desgnng and producng a useful proten, bologsts use a well-know proten whch s utlzed as a target and a template proten. After dentfyng the functon of a proten, bologsts produce new useful protens by substtutng some resdues of the dentfed proten. In the case of a DNA (Deoxyrbonuclec Acd) sequence, bologsts select nuclec acd to substtute. From ths substtuton, they then synthesze a new proten. These new protens or DNA sequences have hgh sequence homology (smlarty). We defne a sequence cluster as a cluster whch s consttuted of smlar sequences. As another example of a sequence cluster, we consder a SNP (Sngle Nucleotde Polymorphsm) Cluster. A SNP s a DNA sequence varaton occurrng when a sngle nucleotde n the genome (or other shared sequence) dffers between members of a speces (or between pared chromosomes n an ndvdual). We suggest a new compressng technque for these sequence clusters usng the Smth-Waterman [13] sequence algnment method. We select a representatve sequence whch has a mnmum sequence dstance (the Smth-Waterman algnment score) n a cluster by scannng the dstances of all sequences. The dstances are obtaned by calculatng the sequence algnment result. Specfc substtuton matrces for the DNA sequence and proten sequence are appled to score the algnment. The result of the sequence algnment s utlzed to make converson nformaton named the Edt-Scrpt between the two sequences. We, then, only store representatve sequences and Edt-Scrpts for each cluster nto a database. Member sequences of each cluster are easly created usng representatve sequences and Edt-Scrpts. Ths work can be adapted to any sequence clusters whch have a hgh sequence smlarty. 2. Related work Sequence algnments are algnng the sequences of nuclec acd or proten n order to ndcate the relatonshp among sequences. The homology of sequences s well presented. These sequences are classfed as havng par-wse or multple algnments accordng to the number of sequences at once. Parwse algnment [8, 10, 13, 17, 18] s algnng two sequences at once and n case of more than two Correspondng Author /08/$ IEEE 520 CIT 2008

2 sequences at once, we have multple sequence algnment [3, 4, 7, 9, 14, 15, 16], respectvely. Also, sequence algnments are categorzed as ether global or local algnment accordng to the type of homology. When comparng two sequences such that these sequences are the same type as the DNA sequences or amno acd sequences, f we want to get a maxmum homology, then we algn two whole sequences. We could say that ths type of homology s that of global smlarty. Ths algnment s called global algnment [10, 12]. When predctng 3D structures of unknown proten from a sequence, global algnment s used to select the target template of the proten. On the other hand, f we want to know whch part of the sequences s smlar when algnng, local homology s adopted and we call ths algnment local algnment [1, 2, 11, 13]. It s effectve to use local algnment when fndng the functonal sharng part of sequences. Current sequence algnment algorthms [1, 2, 11] as well as heurstc approaches to scan whole databases are fast but these approaches have low accuracy. The sequence algnment algorthms n an early stage usng dynamc programmng [10, 11, 12] and have hgh accuracy. These algorthms are not acceptable for whole database scannng when the database has a large number of entres. Compresson of a sequence cluster does not need fast response unlke a database search. The number of sequences n a cluster s sgnfcantly fewer than entres n the whole database. The algorthms [10, 13] whch have hgher accuracy can generate shorter Edt-Scrpts. When the sequences n a cluster are globally smlar, however durng algnment, global algnment yelds a number of sequence gaps whch makes Edt-Scrpts longer because global algnment has a propensty to make the length of two sequences the same wth many gaps. The character, hyphen (-), s used to express the gaps. Therefore, the Smth-Waterman algorthm [13] s qute sutable for a sequence cluster compresson. 3. The proposed method We suggest a new compresson method to reduce sequence cluster sze. To acheve ths, we frst select the representatve sequence of a cluster. In ths procedure, we calculate the average sequence dstance of all member sequences wth a Smth-Waterman algnment score. The specfc substtuton matrces for nuclec acd and amno acd (BLOSUM62 [6]) are utlzed when scorng. We create an Edt-Scrpt,, from the Smth-Waterman algnment results. The changed nformaton s wrtten as an Edt-Scrpt. Only a representatve sequence of a cluster and each Edt- Scrpt of a member sequence are stored nto the database. In ths secton, we explan these procedures n detal The representatve sequence of a cluster The purpose for whch we select the representatve sequence of a cluster s to fnd whch sequence n the cluster makes the shortest Edt-Scrpt. For ths, we need to choose the sequence close to the center of the cluster. Ths work s acheved by calculatng each dstance, d, n Fgure 1. We assume that a sequence cluster conssts of genome sequences whch have hgh sequence smlarty (homology). Fgure 1 shows an example of a sequence cluster, SC = {s 0,s 1,s 2,s 3,,s n }, where S n denotes the member sequences of the cluster and n denotes the number of sequences n the cluster. Fgure 1. A Sequence Cluster. We then calculate the average dstance of each member sequence S n. Each sequence n the cluster has (n-1) dstances to other sequences. The average dstance s obtaned from a summaton of the total dstances of one member sequence by the followng Equaton (1) where d, denotes the dstance between sequences S and S, and S 0 denotes the current member sequence. For smplfyng Equaton (1), f we substtute "d s0,s " to "d s ", we fnally get Equaton (2). n 1 ( ds 0, s d 1 s0, s d 2 s0, s d 3 s0, s d 4 s0, sn 1 n 1 Average Dstance of S 0 = n 1 1 d n 1 ) (1) s (2) The representatve sequence of the cluster means the member sequence S whch has mnmum average dstance. A detaled method to calculate the dstance between sequences s explaned n the next secton. 521

3 3.2. The sequence dstance The Eucldean dstance s wdely used when calculatng dstance between obects n a common cluster. In the case of a genome sequence, applyng the Eucldean dstance s not acceptable because sequences do not have any absolute poston. Thus, sequence algnment methods are employed to calculate the dstance between sequences. We use the Smth- Waterman algnment score [13] to get sequence dstances. The reason we adopt the Smth-Waterman algnment method s explaned n the related work, Secton 2. Now, we dscuss how the Smth-Waterman algnment method s appled n detal. M, M M max M 0 1, 1,, 1 1 s( a, b ), s(a, b ) 2, s(a, b ) -1, f a f a b b s(a,-) s(-,b ) -2 (3) Gven sequences, S x ={GGCTCAATCA} and S y ={ACCTAAGG}, Fgure 2(a) shows the algnment matrx wth dynamc programmng. Each value of the cell, M, s obtaned from Equaton (3). Here, each cell has three canddate values: the score from above, the left and the dagonal. Fgure 2(b) denotes these three canddate values. The scores, s(a,b ), for matches and msmatches are +2 for a match and -1 for a msmatch, respectvely. The gap penalty score, s(a,-), and s(-,b ) are the same, -2, when the score comes from above or the left cell value. If a cell value. M,, falls below zero, the score s replaced by zero. For example, cell M 7,6 has three canddate values where = 6 from the dagonal, 5-2 = 3 from the left, and 3-2 = 1 from above. Among these three values, the value from dagonal (6) s assgned because t s maxmum. Every cell value n Fgure 2(a) memorzes the drecton where the cell value comes from. The drectons of M, are dsplayed n Fgure 2(a) as arrows. For clarty, we have ncluded only the arrows around the hghest-scorng path. These arrows are mandatory when tracng the algnment path. The best local algnment s the one that ends n the matrx element havng the hghest score (M 7,6 =6). Arrows from the left or above denote gaps (nserton and deleton) whch are represented by a hyphen (-) n Fgure 3. The hghlghtng areas, S x {CTCAA} and S y {CT-AA}, are locally smlar. Calculatng the score for ths algnment s shown n the next secton. Fgure 3. The result of smth-waterman algorthm 3.3. The sorng matrx We have used algnments of nuclec acds as examples. One very mportant applcaton of algnment s proten algnment. The scorng matrx for amno acds s much more complcated than that of nuclec acds (DNA : 4 characters, Proten : 20 Characters). Fgure 4 ndcates the scorng matrx for a DNA sequence. Ths scorng matrx contans the assumpton that algnng A wth G s not ust as bad as algnng A wth T because studes of mutatons n a homologous gene ndcate that transton mutatons (AG, GA, CT, or TC) occur approxmately twce as frequently as do transversons (AT, TA, AC, GT). Thus, the algnment score for the Fgure 3 algnment s 0.5 ( ) whereas the score for the match s +2. The score for the gap s - 2 and the score for the msmatch s -1 or -0.5 shown n Fgure 4. In our proposed method, ths score s called the sequence dstance. Fgure 2. The smth-waterman algorthm. Fgure 4. The scorng matrx for DNA sequence 522

4 Fgure 5 denotes the BLOSUM62 substtuton matrx for amno acds whch s commonly used. BLOSUM (BLOcks SUbsttuton Matrces) matrces [6] are based on algned proten sequence blocks wthout assumptons about mutaton rates. Dfferent levels of the BLOSUM matrx can be created by dfferentally weghtng the degree of smlarty between sequences. For example, a BLOSUM62 matrx s calculated from proten blocks such that f two sequences are more than 62% dentcal, then the contrbuton of these sequences s weghted to sum to one. In ths way, the contrbutons of multple entres of closely related sequences are reduced. We appled BLOSUM 35, 45, 62, and 80 matrces n selectng a representatve sequence, but the representatve sequence was not changed accordng to the types of BLOSUM matrx. In order to create an Edt-Scrpt we more need three operators whch transform the Matched Sequence of S x nto the Matched Sequence of S y. Focusng on Matched Sequences, there exst three knds of events such as nserton, deleton, and substtuton. A specfc poston on whch an event occurs n a Matched Sequence, and changed resdue (or nuclec acd) are nputted as parameters of these operators. Now, we can smply express the Edt-Scrpt, = { {Pror MatSeq}, Startng Index, {operator1, operator2, operator3, }, Endng Index, {Posteror MatSeq}. For example, the Edt-Scrpt for Fgure 6 s = { {TI}, 2, { ns(2,r), sub(5,i), del(7,t) }, 12, {WP} }. Wth ths Edt-Scrpt, we can transform Sequence S x nto Sequence S y. Here, Sequence S x ndcates a representatve sequence of the cluster. Sequence S y means a member sequence of the cluster. In ths example shown n Fgure 6, the sequence length of the two sequences looks qute short for charty of explanaton, but the real length of the DNA sequence reaches to thousands of nuclec acds and hundreds for amno acds respectvely Creatng a member sequence from a representatve sequence Fgure 5. The BLOSUM62 substtuton matrx 3.4. The Edt-Scrpt, In ths secton, we wll descrbe how to create an Edt-Scrpt from a gven algnment such as Fgure 6. The nformaton we extract from an algnment corresponds to the followng defntons. Startng Index n Fgure 6 s an ndex that starts a matched sequence n sequence S x. The Endng Index ndcates an ndex that ends the matched sequence S y. Pror MatSeq denotes a pror sequence of the matched sequence n sequence S y. Posteror MatSeq descrbes the posteror sequence of the matched sequence n sequence S y. Fnally, Matched Sequences mean the locally smlar area between Sequence S x and S y. The nformaton whch s stored nto the database s a representatve sequence of cluster and Edt-Scrpts of each member sequence. When users want to show the member sequence, our system retreves the representatve sequence and Edt-Scrpt of the member sequence upon a user's request. Fgure 7 descrbes ths procedure n detal. Frst, a user gets the representatve sequence of the cluster and the Edt-Scrpt of the member sequence where the user wants to create the sequence cluster from database searchng. Next, the system extracts the Matched Sequence of the representatve sequence wth startng and endng ndces n the Edt-Scrpt. Then, the Matched Sequence of the representatve sequence s transformed nto a matched sequence of the member sequence usng Change Operators. Fnally, Pror MatSeq and Posteror MatSeq are added nto the Matched Sequence of the member sequence. Then the gaps n the member sequence are removed. Fgure 6. The compostons of Edt-Scrpt Fgure 7. Creatng a member sequence from a representatve sequence 523

5 4. Evaluaton In ths secton, we estmated the compressed sze usng our method. Estmatng compressed sze between two DNA sequences was performed n two ways. One way was the compressed sze by sequence length such as 2KB, 4KB, and 8KB. Another way was by sequence dentty such as 40%, 50%, 60%, 70%, 80%, and 90%. Data Sze(KB) KB 4KB 8KB Length of Sequence 90% 80% 70% 60% 50% 40% total sze Fgure 8. Compressed cluster sze by sequence length and smlarty. In Fgure 8, the total sze (ndcate by ) denotes the total sze of two sequences wthout applyng any Edt-Scrpt. The sze reducton was dscovered when the homology (sequence dentty) between sequences was over 50% for 2KB, 55% for 4KB, and 52% for 8KB. The data reducton rate got better when the sequence dentty was hgher. Generally speakng n ths experment, the sze of the applyng Edt-Scrpt was reduced when the sequence dentty was over 55%. The sze was somewhat ncreased when the sequence dentty was lower than 50%. Ths s because the Edt- Scrpt was longer than the sequence when the sequence dentty was lower than 50%. In order to store the nformaton that a nuclec acd was changed, the Edt- Scrpt needed 3 Bytes (type of event, the poston, and nuclec acd) 5. Concluson In ths paper, we suggest a new compresson method to reduce sequence cluster sze. To acheve ths reducton, we frst select the representatve sequence of a cluster. In ths procedure, we calculate the average sequence dstance of all member sequences usng the Smth-Waterman algnment score. The specfc substtuton matrces for nuclec acd and amno acd (BLOSUM62 [6]) are utlzed when scorng. We create an Edt-Scrpt,, from the Smth-Waterman algnment result. The changed nformaton s wrtten n Edt- Scrpt. Only a representatve sequence of the cluster and each Edt-Scrpt of the member sequence are stored nto the database. Through expermental results, the sze reducton s acheved when the sequence dentty (homology) s over 55%. Not only sequences n the SNP cluster and results of proten engneerng whch makes useful proten from well-known proten, but also any sequence clusters whch have hgh sequence smlarty are good examples to apply our work to. In gong work, we are extendng our method to manage versons of sequences usng temporal concepts. 6. Acknowledgement Ths work was supported by a Korea Research Foundaton Grant funded by the Korean Government(MOEHRD) (The Regonal Research Unverstes Program/Chungbuk BIT Research- Orented Unversty Consortum) and the Korea Scence and Engneerng Foundaton (KOSEF) grant funded by the Korea government (MOST) (R ) 7. References [1] Altschul, S.F. et. al.: Basc local algnment search tool., J.Mol.Bol., 215, 1990, pp [2] Altschul, S.F., et. al.: Gapped BLAST and PSI-BLAST:a new generaton of proten database search programs. Nuclec Acds Res., 25, 1997, pp [3] Bans, W.: MULTAN : A program to algn multple DNA sequences., Nucl.Acds.Res., 14, 1986, pp [4] Barton, G.J., and Sternberg, M.J.E.: A strategy for the rapd multple algnment of proten sequences: confdence levels from tertary structure comparsons., J.Mol.Bol., 198, 1987, pp [5] Dayhoff, M.O., Schwartz, R.M., and Orcutt. B.C.: A model of evolutonary change n protens, Atlas of Proten sequence and Structure, 5(3), 1978, pp [6] Henkoff, S., and Henkoff, J.G.: Amno acd substtuton matrces from proten blocks. Proc.Natl.Acad.Sc.USA, 89, 1992, pp [7] Hggns, D.G., Bleasby, A.J., and Fuchs, R.: CLUSTAL V: Improved software for multple sequence algnment., Comp.Appl.Bosc., 8, 1992, pp [8] Lpman, D.J., and Pearson, W.R.: Rapd and senstve proten smlarty searches., Scence, 227, 1985, pp

6 [9] Murata, M.J., Rchardson, J.S., and Sussman, J.L.: Smultaneous comparson of three proten sequences., Proc.Natl.Acad.Sc.USA, 82, 1985, pp [10] Needleman, S.B., and Wunsch, C.D.: A general method applcable to the search for smlartes n the amno acd sequences of two protens., J.Mol.Bol., 48, 1970, pp [11] Pearson, W.R., and Lpman, D.J.: Improved tools for bologcal sequence comparson. Proc.Natl.Acad.Sc. USA, 85, 1988, pp [12] Sellers, P. H.: An algorthm for the dstance between two fnte sequences., J.Comb.Th.A, 16, 1974, pp [13] Smth, T.F., and Waterman, M.S.: Identfcaton of common molecular subsequences. J.Mol.Bol., 147, 1981, pp [14] Sobel, E., and Martnez, H.M.: A multple sequence algnment program, Nucl.Acds.Res., 14, 1986, pp [15] Taylor, W.R.: Identfcaton of proten sequence homology by consensus template algnment. J.Mol.Bol., 188, 1986, pp [16] Tompson, J.D., Hggns, D.G., and Gbson, T.J.: ClustalW: Improved senstvty of profle searches through the use of sequence weghts and gap excson. Comput. Applc. Bosc. 10, 1994, pp [17] Wlbur, W.J., and Lpman, D.J.: Rapd smlarty searches of nuclec acd and proten data banks. Proc.Natl. Acad.Sc.USA, 80, 1983, pp [18] Wlbur, W.J., and Lpman, D.J.: The context dependent comparson of bologcal sequences. Sam.J.Appl. Math., 44, 1984, pp [19] Km, S., Jung K.S., Ryu K.H.: Automatc Orthologous- Proten-Clusterng from Multple Complete-Genomes by the Best Recprocal BLAST Hts, LNBI, 3916, 2006, pp [20] Jung, K.S., Km, S., Ryu, K.H.: A Personalzed Bologcal Data Management System Based on BSML, LNBI, 4115, 2006, pp