International Journal of Scientific & Engineering Research, Volume 5, Issue 2, February-2014 960

ISSN 2229-5518

# A Fangled Greedy Approach For Protein Pattern Matching

Ramya.V* VidyaPriya.V*

* Assistant Professor, Department of Computer Science, Meenakshi College for women, Chennai-24, India.

*Assistant Professor,Department of ComputerScience, Quaid-e-Millath College for women, Chennai-2, India.

E-mail: * ramyars89@gmail.com

*vdy_priya@yahoo.co.in

## KEYWORDS: Pattern Matching, DNA and Protein Sequences, comparison per character

I. INTRODUCTION
Proteins are a chain of amino acids which are the
main parameter for pattern matching. There are about 20 common amino acids. They share a common structure except for one chemical group (R, side chain) attached to the central carbon atom [1]. The amino acids are taken to form a sequence called the protein sequence. The proposed algorithm takes the sequence and the pattern as input, matches the pattern with the sequence. Greedy method is used for performing matching of the given pattern.

### PATTERN MATCHING (FGAPM)

A greedy algorithm is that follows the problem
solving heuristic of making the locally optimal choice at each stage with the hope of finding a global optimum [6]. The most common approach is to improve efficiency is the idea of using the greedy method for matching of the pattern to the sequence. The method gets the sequence and the input from the user and stores each in different arrays and calculates the length of the pattern and stores it in a variable .The sequence, the pattern, pattern length and the starting value are inputs for processing of the retrieval of protein pattern. Now the method extracts the protein data length as the control has to drive through the entire sequence for finding the matching pattern, its position and the number of occurrence .Then once the start position and all necessary data are given the greedy method starts the matching process, it gets the first set of characters and stores in a variable likewise the process done for the entire sequence, for every time the identified pattern is returned and its position in the pattern along with the no of times it occurs in the pattern are retrieved.

### ALGORITHM:

Step 1: Get Input String As Pattern (IP) Step 2: Get Protein Data Source As (PD) Step 3: Get IPlength as Input Pattern Length Step 4: Store: Step 3 Result->IPLENGTH
Step 5: Greedy (IP,PD,IPLENGTH,STARTVAL){

### II.PATTERN MATCHING

Pattern matching is the problem of finding a
subsequence with some property in a sequence of symbols, the simplest case being finding a given string inside the sequence. This is one of the oldest and most pervasive problems in computer science. Applications requiring some form of string matching can be found virtually everywhere. However, recent years have witnessed a dramatic increase in interest in string matching problems, particularly from the rapidly growing communities of information retrieval and computational biology. Not only are these communities facing a drastic increase in the text sizes they have to manage, but they are demanding more and more sophisticated searches. The types of patterns of interest are not just simple strings but also include wild cards, gaps and regular expressions. One of the major problems in genomic field is to perform pattern comparison on protein sequences.
Step 6: Extract Data from PD[IPLENGTH]
Step 7: Store :Step 6 Result -> PARTDATA Step 8: Compute Length[PD]
Step 9: Store Step 8 Result-> PDLENGTH Step 10: Loop ( PDLENGTH !=0) do
Step 11: if'(Compare(IP,PARTDATA,IPLENGTH)) then
Step 12: FIND(START,END)-
>STORE :STARTPOS,ENDPOS Step 13: Store :Output As IPATTERN[IP,STARTPOS,ENDPOS]
Step 14 :Greedy(IP,PD,IPLENGTH,STARTVAL+ENDPOS)
Step 15 : End Loop}

International Journal of Scientific & Engineering Research, Volume 5, Issue 2, February-2014 961

ISSN 2229-5518

Step 16 :Return IPATTERN
Step 17:Compute Length[IPATTERN] as IPALENGTH Step 18:Loop(IPALENGTH!=0)
Step 19:Highlight Pattern[PD,IP,STARTPOS,ENDPOS] Step 20: END Loop
Step 21:END

### SAMPLE DATASET: PD=JKLTVSHIBTVSGYRTVSPOTTVSJFETVSLOPTVSU NVTVSKIZTVSRF

IP=TVS IPLENGTH=3
GREEDY(TVS,PD,S,1)
PD[3] THE FIRST SET OF RESIDUES FROM PD =JKL PARTDATA=JKL
Now length of the sequence is calculated ,till the end checking for the pattern occurrence is performed by, Compare(TVS,JKL,3)
The start and end value of the pattern if occurred is stored and displayed with a multidimensional
array IPATTERN ,as TVS4 6
TVS1012,TVS1618,TVS2224,………

### CONCLUSION:

. The FGAPM is for matching of the pattern with the
sequence given, the future work can be enhanced in many ways like the comparison can be carried out for genomes or for different medical data and also the use of numerous medical language tools like UMLS.
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