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Related Concept Videos

Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

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Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved...
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PSAR-align: improving multiple sequence alignment using probabilistic sampling.

Jaebum Kim1, Jian Ma

  • 1Department of Animal Biotechnology, UBITA Center for Biotechnology Research (CBRU), Konkuk University, Seoul 143-701, Korea, Department of Bioengineering and Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.

Bioinformatics (Oxford, England)
|November 14, 2013
PubMed
Summary
This summary is machine-generated.

PSAR-Align refines multiple sequence alignments using probabilistic sampling to improve accuracy. This novel tool enhances results from existing alignment methods, aiding biological sequence analysis.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Multiple sequence alignment (MSA) is crucial for understanding evolutionary relationships and functional sites in biological sequences.
  • Existing MSA tools may produce suboptimal alignments, limiting downstream analyses.
  • Refining MSAs is essential for improving the accuracy of phylogenetic inference and functional annotation.

Purpose of the Study:

  • To introduce PSAR-Align, a novel tool for refining multiple sequence alignments.
  • To leverage probabilistic sampling for generating suboptimal alignments to enhance MSA quality.
  • To evaluate the performance of PSAR-Align against existing MSA tools.

Main Methods:

  • Developed PSAR-Align, a multiple sequence realignment tool implemented primarily in C++.
  • Utilized probabilistic sampling to generate suboptimal alignments for refinement.
  • Evaluated PSAR-Align's performance using various benchmark datasets and compared it with established MSA tools.

Main Results:

  • PSAR-Align successfully refines existing multiple sequence alignments.
  • The tool demonstrates improved alignment accuracy compared to various standard MSA methods.
  • Probabilistic sampling effectively contributes to the refinement process.

Conclusions:

  • PSAR-Align is an effective tool for improving the quality of multiple sequence alignments.
  • The method offers a valuable enhancement for existing bioinformatics pipelines.
  • PSAR-Align is freely available, promoting its adoption in biological research.