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A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
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
Summary
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.
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.
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