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SIFTER search: a web server for accurate phylogeny-based protein function prediction.

Sayed M Sahraeian1, Kevin R Luo2, Steven E Brenner3

  • 1Department of Plant and Microbial Biology, University of California, Berkeley, CA 94720, USA.

Nucleic Acids Research
|May 17, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a user-friendly web server for the SIFTER algorithm, enabling accurate protein function prediction. The server provides precomputed predictions for millions of proteins, making advanced computational analysis accessible to researchers.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Genomics

Background:

  • High-throughput sequencing has generated vast numbers of proteins, but most lack experimental characterization.
  • Computational methods are essential for automated protein annotation and function prediction.
  • Existing methods may require significant computational resources, limiting accessibility for large-scale analyses.

Purpose of the Study:

  • To provide a user-friendly web interface for accurate protein function prediction using the SIFTER algorithm.
  • To make the SIFTER algorithm's predictions accessible to a wider research community.
  • To facilitate exploration of protein function predictions for a large dataset.

Main Methods:

  • Development of a web server for the SIFTER algorithm.
  • SIFTER utilizes a statistical model of function evolution across phylogenetic trees for sequence-based prediction.
  • Precomputation of predictions for over 16 million proteins across numerous species.

Main Results:

  • A publicly accessible web server (http://sifter.berkeley.edu/) for protein function prediction.
  • Precomputed SIFTER predictions for 16,863,537 proteins from 232,403 species.
  • Functionality for users to query proteins, species, functions, and homologs.

Conclusions:

  • The SIFTER web server democratizes access to advanced protein function prediction.
  • It significantly lowers the barrier for researchers to analyze large-scale protein datasets.
  • The tool supports exploration and discovery of molecular functions for uncharacterized proteins.