Related Experiment Video
Updated: Jul 9, 2026
09:37
An Integrated Approach for Microprotein Identification and Sequence Analysis
Published on: July 12, 2022
Analysing the performance of personal computers based on Intel microprocessors for sequence aligning bioinformatics
Pradeep S Nair1, Eugene B John
1Department of Electrical and Computer Engineering, University of Texas, San Antonio, TX 78249, USA. pradeep.nair@utsa.edu
International Journal of Bioinformatics Research and Applications
|December 1, 2007
Summary
Bioinformatics sequence alignment on personal computers using BLAST and FASTA shows performance gains are possible. Optimizing the L1-cache can significantly improve the speed of these crucial bioinformatics benchmarks.
Area of Science:
- Bioinformatics
- Computational Biology
- Computer Science
Background:
- Sequence alignment is fundamental in bioinformatics.
- Personal computers are increasingly used for local sequence alignment.
- Performance analysis of bioinformatics tools on local hardware is essential.
Purpose of the Study:
- To analyze the performance of personal computers for sequence alignment.
- To evaluate popular bioinformatics benchmarks like BLAST and FASTA.
- To identify potential performance bottlenecks and optimization strategies.
Main Methods:
- Benchmarking personal computer performance.
- Analyzing the computational and memory operations of BLAST and FASTA.
- Identifying recurring operations within the alignment algorithms.
Main Results:
- Sequence alignment benchmarks exhibit numerous recurring operations.
- Extensive use of memory operations was observed.
- Performance appears to be sensitive to L1-cache size.
Conclusions:
- Personal computer performance for sequence alignment is influenced by hardware factors like L1-cache.
- Optimizing cache memory could enhance the efficiency of bioinformatics sequence alignment.
- Further research into hardware-specific optimizations for bioinformatics tools is warranted.
