Distributed Approach for Peptide Identification

<p>A crucial step in protein identification is peptide identification. The Peptide Spectrum Match (PSM) information set is enormous. Hence, it is a time-consuming procedure to work on a single machine. PSMs are situated by a cross connection, a factual score, or a probability that the match between the trial and speculative is right and original. This procedure takes quite a while to execute. So, there is demand for enhancement of the performance to handle extensive peptide information sets. Development of appropriate distributed frameworks are expected to lessen the processing time.</p> <p>The designed framework uses a peptide handling algorithm named C-Ranker, which takes peptide data as an input then identifies the accurate PSMs. The framework has two steps: Execute the C-Ranker algorithm on servers specified by the user and compare the correct PSM’s data generated via the distributed approach with the normal execution approach of C-Ranker.</p> <p>The objective of this framework is to process expansive peptide datasets utilizing a distributive approach. The nature of the solution calls for parallel execution and hence a decision to implement the same in Java has been taken. The results clearly show that distributed C-Ranker executes in less time as compared to the conventional centralized CRanker application. Around 66.67% of the overall reduction in execution time is shown with this approach. Besides, there is a reduction in the average memory usage with the distributed system running C-Ranker on multiple servers.</p> <p>A great significant benefit that may get overlooked is the fact the distributed CRanker can be used to solve extraordinarily large problems without incurring expenses for a powerful computer or a super computer. Comparison of this approach with An Apache Hadoop Framework for peptide identification with respect to the cost, execution times and flexibility were discussed.</p>

Name thesis:degree_name

Master of Science

Discipline thesis:degree_discipline

Department of Computer Science

Year

2015

Author dc:creator

• Vedanbhatla, Naga V K Abhinav

Contributors dc:contributor

• Dr. Zhonghang Xia (Director), Dr. Rong Yang, Dr. Qi Li

• C-Ranker
• machine learning
• Analytical Chemistry
• Computer Engineering
• Computer Sciences

Repository record dc:identifier

https://digitalcommons.wku.edu/theses/1546

OAI identifier oai:identifier

oai:digitalcommons.wku.edu:theses-2550