A study on biomolecular sequence alignment using machine learning techniques

Pairwise sequence alignment is used to compare the sequence of nucleotides or protein with the aims of inferring structural, functional and evolutionary relationships. The main reason of sequence alignment is to find an optimal alignment. The most used method in research and have been certify to pro...

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Main Authors: Othman, Muhamad Razib, Salim, Naomie, Abdul Jalil, Rozita, Deris, Safaai, Mat Yatim, Safie, Md. Illias, Rosli
Format: Monograph
Language:English
Published: Faculty of Cmputer Sience and Information System 2004
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Online Access:http://eprints.utm.my/id/eprint/4400/3/75079.pdf
http://eprints.utm.my/id/eprint/4400/
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Institution: Universiti Teknologi Malaysia
Language: English
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spelling my.utm.44002017-08-07T03:32:22Z http://eprints.utm.my/id/eprint/4400/ A study on biomolecular sequence alignment using machine learning techniques Othman, Muhamad Razib Salim, Naomie Abdul Jalil, Rozita Deris, Safaai Mat Yatim, Safie Md. Illias, Rosli ZA4050 Electronic information resources Pairwise sequence alignment is used to compare the sequence of nucleotides or protein with the aims of inferring structural, functional and evolutionary relationships. The main reason of sequence alignment is to find an optimal alignment. The most used method in research and have been certify to produce an optimal sequence alignment are dynamic programming methods Smith-Waterman for local alignment. Based from the previous research, scoring schemes in dynamic programming can be improved by using substitutions matrices and introduction of gap in alignment with gap penalty function. The reasons are to optimize result of alignments with perpetuate biology concept like evolution changes in molecular structures caused by mutation. Today, no general theory guides the selection of substitution matrices and gap penalties for local sequence alignment. Because of that, this project will implement dynamic programming method Smith-Waterman with different parameter of substitution matrices and gap penalty function in scoring schemes. Substitution matrices that will be used are BLOSUM45, BLOSUM62 and BLOSUM80. While linear gap penalty with range values parameter from (–d=1 to –d=10) or affine gap penalty with range values parameter for opening gap from (–d=1 to –d=12) and extension gap from (–e=1 to–e=5). Intensive comparison will be done to test the efficiency and determine the effective substitution matrices and gap penalty parameter for sequence alignment. 27 sets of data protein sequences categorized by length and percentage similarity identity will be used for sequence alignment. The results will give the guideline for the selection of effective substitution matrices and gap penalty parameter for sequence alignment. Faculty of Cmputer Sience and Information System 2004 Monograph NonPeerReviewed application/pdf en http://eprints.utm.my/id/eprint/4400/3/75079.pdf Othman, Muhamad Razib and Salim, Naomie and Abdul Jalil, Rozita and Deris, Safaai and Mat Yatim, Safie and Md. Illias, Rosli (2004) A study on biomolecular sequence alignment using machine learning techniques. Project Report. Faculty of Cmputer Sience and Information System, Skudai, Johor. (Unpublished)
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic ZA4050 Electronic information resources
spellingShingle ZA4050 Electronic information resources
Othman, Muhamad Razib
Salim, Naomie
Abdul Jalil, Rozita
Deris, Safaai
Mat Yatim, Safie
Md. Illias, Rosli
A study on biomolecular sequence alignment using machine learning techniques
description Pairwise sequence alignment is used to compare the sequence of nucleotides or protein with the aims of inferring structural, functional and evolutionary relationships. The main reason of sequence alignment is to find an optimal alignment. The most used method in research and have been certify to produce an optimal sequence alignment are dynamic programming methods Smith-Waterman for local alignment. Based from the previous research, scoring schemes in dynamic programming can be improved by using substitutions matrices and introduction of gap in alignment with gap penalty function. The reasons are to optimize result of alignments with perpetuate biology concept like evolution changes in molecular structures caused by mutation. Today, no general theory guides the selection of substitution matrices and gap penalties for local sequence alignment. Because of that, this project will implement dynamic programming method Smith-Waterman with different parameter of substitution matrices and gap penalty function in scoring schemes. Substitution matrices that will be used are BLOSUM45, BLOSUM62 and BLOSUM80. While linear gap penalty with range values parameter from (–d=1 to –d=10) or affine gap penalty with range values parameter for opening gap from (–d=1 to –d=12) and extension gap from (–e=1 to–e=5). Intensive comparison will be done to test the efficiency and determine the effective substitution matrices and gap penalty parameter for sequence alignment. 27 sets of data protein sequences categorized by length and percentage similarity identity will be used for sequence alignment. The results will give the guideline for the selection of effective substitution matrices and gap penalty parameter for sequence alignment.
format Monograph
author Othman, Muhamad Razib
Salim, Naomie
Abdul Jalil, Rozita
Deris, Safaai
Mat Yatim, Safie
Md. Illias, Rosli
author_facet Othman, Muhamad Razib
Salim, Naomie
Abdul Jalil, Rozita
Deris, Safaai
Mat Yatim, Safie
Md. Illias, Rosli
author_sort Othman, Muhamad Razib
title A study on biomolecular sequence alignment using machine learning techniques
title_short A study on biomolecular sequence alignment using machine learning techniques
title_full A study on biomolecular sequence alignment using machine learning techniques
title_fullStr A study on biomolecular sequence alignment using machine learning techniques
title_full_unstemmed A study on biomolecular sequence alignment using machine learning techniques
title_sort study on biomolecular sequence alignment using machine learning techniques
publisher Faculty of Cmputer Sience and Information System
publishDate 2004
url http://eprints.utm.my/id/eprint/4400/3/75079.pdf
http://eprints.utm.my/id/eprint/4400/
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