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Updated: Jun 5, 2026

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens
09:14

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Published on: June 28, 2018

A novel approach to multiple sequence alignment using hadoop data grids.

G Sudha Sadasivam1, G Baktavatchalam

  • 1Department of Computer Science and Engineering, PSG College of Technology, Coimbatore 641004, Tamil Nadu, India. sudhasadhasivam@yahoo.com

International Journal of Bioinformatics Research and Applications
|January 13, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a faster, accurate protein sequence alignment method using Hadoop. It enhances evolutionary linkage determination and molecular structure prediction for large datasets.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Multiple sequence alignment is crucial for understanding protein evolution and structure.
  • Accurate alignment methods like dynamic programming are often computationally intensive.
  • There is a need for efficient yet accurate sequence alignment techniques.

Purpose of the Study:

  • To propose a time-efficient approach for multiple protein sequence alignment.
  • To improve both the speed and accuracy of sequence alignment processes.
  • To enable the alignment of very large protein sequences.

Main Methods:

  • Developed a dynamic algorithm leveraging data and computational parallelism within Hadoop data grids.
  • Implemented a block-splitting principle within the Hadoop framework.
  • Utilized the scalability of Hadoop for handling large-scale sequence data.

Main Results:

  • Achieved significant improvements in both speed and accuracy compared to traditional methods.
  • Demonstrated the effectiveness of the dynamic algorithm with Hadoop parallelism.
  • Successfully aligned very large protein sequences efficiently.

Conclusions:

  • The proposed Hadoop-based approach offers a time-efficient and accurate solution for multiple sequence alignment.
  • This method enhances the feasibility of analyzing large genomic and proteomic datasets.
  • The dynamic algorithm with Hadoop integration advances computational biology tools.