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Updated: Jul 7, 2026

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
07:09

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Published on: May 28, 2021

Searching for evolutionary distant RNA homologs within genomic sequences using partition function posterior

Usman Roshan1, Satish Chikkagoudar, Dennis R Livesay

  • 1Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA. usman@cs.njit.edu

BMC Bioinformatics
|January 30, 2008
PubMed
Summary
This summary is machine-generated.

Probalign, using partition function match probabilities, significantly improves RNA-genome alignment accuracy over existing methods, especially for distantly related sequences. This approach enhances the identification of RNA homologs in large genomic segments.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Identifying RNA homologs in genomic sequences is challenging due to low sequence identity and unalignable flanking regions.
  • Traditional structure-sequence or profile-sequence alignment methods are often unreliable for these cases.
  • Local sequence-sequence alignment programs are commonly employed as an alternative.

Purpose of the Study:

  • To evaluate the effectiveness of Probalign, a program utilizing maximal expected accuracy alignments with partition function match probabilities, for local RNA-genome alignment.
  • To compare Probalign's performance against established local alignment tools like HMMER, SSEARCH, and BLAST, as well as ClustalW.
  • To assess the accuracy and discriminatory power of Probalign's alignment probabilities.

Main Methods:

  • A pairwise RNA-genome alignment benchmark was constructed using RFAM families with varying sequence identities.
  • Query RNAs were embedded within genomic sequences with simulated 5' and 3' flanks.
  • Probalign's local alignment accuracy was compared against HMMER, SSEARCH, BLAST, and ClustalW.
  • Parameters for each program were optimized on a subset of the benchmark data.

Main Results:

  • Probalign demonstrated the highest overall accuracy across the benchmark datasets.
  • Probalign outperformed SSEARCH by 10% accuracy on 5 out of 22 families.
  • On datasets with ≤30% sequence identity, Probalign showed a significantly lower median error rate (71.2%) compared to SSEARCH (83.4%).
  • Probalign's mean posterior probability was a better indicator of alignment quality than SSEARCH's normalized Z-score.

Conclusions:

  • Partition function match probabilities, as implemented in Probalign, offer a statistically significant improvement for RNA-genome alignment.
  • This method enhances the identification of distantly related RNA sequences within large genomic contexts.
  • Probalign represents a superior approach for local RNA-genome alignment compared to current standard methods.