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A Practical Guide to Phylogenetics for Nonexperts
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Published on: February 5, 2014

Similarity searching using BLAST.

Kit J Menlove1, Mark Clement, Keith A Crandall

  • 1Department of Biology, Brigham Young University, Provo, UT, USA.

Methods in Molecular Biology (Clifton, N.J.)
|April 21, 2009
PubMed
Summary
This summary is machine-generated.

Similarity searches are vital for bioinformatics, enabling structural motif and gene identification. This chapter reviews search methods, databases, and parameters for effective data exploration in rapidly growing genetic datasets.

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Area of Science:

  • Bioinformatics and Computational Biology
  • Genomics and Genetics

Background:

  • Similarity searches are fundamental to bioinformatics, supporting structural motif and gene identification.
  • The exponential growth of genetic data necessitates efficient methods for accessing and interpreting information from diverse databases.

Purpose of the Study:

  • To provide a comprehensive overview of similarity searching approaches in bioinformatics.
  • To discuss relevant databases and parameter optimization for diverse applications.
  • To offer a practical worked example and considerations for effective similarity searches.

Main Methods:

  • Review of established similarity searching algorithms and strategies.
  • Exploration of key biological databases relevant to sequence and structure similarity.
  • Analysis of parameter tuning for optimizing search sensitivity and specificity.

Main Results:

  • Detailed explanation of various similarity search methodologies.
  • Guidance on selecting appropriate databases and parameters for specific bioinformatic tasks.
  • Illustrative worked example demonstrating practical application of similarity searches.

Conclusions:

  • Effective similarity searches are crucial for navigating and extracting knowledge from large-scale biological data.
  • Understanding search approaches, databases, and parameters enhances the utility of bioinformatics tools.
  • The chapter equips researchers with the knowledge to perform informative and productive similarity searches.