An enhanced RNA alignment benchmark for sequence alignment programs

<p>Abstract</p> <p>Background</p> <p>The performance of alignment programs is traditionally tested on sets of protein sequences, of which a reference alignment is known. Conclusions drawn from such protein benchmarks do not necessarily hold for the RNA alignment problem...

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Main Authors: Steger Gerhard, Mainz Indra, Wilm Andreas
Format: Article
Language:English
Published: BMC 2006-10-01
Series:Algorithms for Molecular Biology
Online Access:http://www.almob.org/content/1/1/19
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spelling doaj-fd6214ebd46d4186841b1f5ed46831a22020-11-24T23:57:27ZengBMCAlgorithms for Molecular Biology1748-71882006-10-01111910.1186/1748-7188-1-19An enhanced RNA alignment benchmark for sequence alignment programsSteger GerhardMainz IndraWilm Andreas<p>Abstract</p> <p>Background</p> <p>The performance of alignment programs is traditionally tested on sets of protein sequences, of which a reference alignment is known. Conclusions drawn from such protein benchmarks do not necessarily hold for the RNA alignment problem, as was demonstrated in the first RNA alignment benchmark published so far. For example, the twilight zone – the similarity range where alignment quality drops drastically – starts at 60 % for RNAs in comparison to 20 % for proteins. In this study we enhance the previous benchmark.</p> <p>Results</p> <p>The RNA sequence sets in the benchmark database are taken from an increased number of RNA families to avoid unintended impact by using only a few families. The size of sets varies from 2 to 15 sequences to assess the influence of the number of sequences on program performance. Alignment quality is scored by two measures: one takes into account only nucleotide matches, the other measures structural conservation. The performance order of parameters – like nucleotide substitution matrices and gap-costs – as well as of programs is rated by rank tests.</p> <p>Conclusion</p> <p>Most sequence alignment programs perform equally well on RNA sequence sets with high sequence identity, that is with an average pairwise sequence identity (APSI) above 75 %. Parameters for gap-open and gap-extension have a large influence on alignment quality lower than APSI ≤ 75 %; optimal parameter combinations are shown for several programs. The use of different 4 × 4 substitution matrices improved program performance only in some cases. The performance of iterative programs drastically increases with increasing sequence numbers and/or decreasing sequence identity, which makes them clearly superior to programs using a purely non-iterative, progressive approach. The best sequence alignment programs produce alignments of high quality down to APSI > 55 %; at lower APSI the use of sequence+structure alignment programs is recommended.</p> http://www.almob.org/content/1/1/19
collection DOAJ
language English
format Article
sources DOAJ
author Steger Gerhard
Mainz Indra
Wilm Andreas
spellingShingle Steger Gerhard
Mainz Indra
Wilm Andreas
An enhanced RNA alignment benchmark for sequence alignment programs
Algorithms for Molecular Biology
author_facet Steger Gerhard
Mainz Indra
Wilm Andreas
author_sort Steger Gerhard
title An enhanced RNA alignment benchmark for sequence alignment programs
title_short An enhanced RNA alignment benchmark for sequence alignment programs
title_full An enhanced RNA alignment benchmark for sequence alignment programs
title_fullStr An enhanced RNA alignment benchmark for sequence alignment programs
title_full_unstemmed An enhanced RNA alignment benchmark for sequence alignment programs
title_sort enhanced rna alignment benchmark for sequence alignment programs
publisher BMC
series Algorithms for Molecular Biology
issn 1748-7188
publishDate 2006-10-01
description <p>Abstract</p> <p>Background</p> <p>The performance of alignment programs is traditionally tested on sets of protein sequences, of which a reference alignment is known. Conclusions drawn from such protein benchmarks do not necessarily hold for the RNA alignment problem, as was demonstrated in the first RNA alignment benchmark published so far. For example, the twilight zone – the similarity range where alignment quality drops drastically – starts at 60 % for RNAs in comparison to 20 % for proteins. In this study we enhance the previous benchmark.</p> <p>Results</p> <p>The RNA sequence sets in the benchmark database are taken from an increased number of RNA families to avoid unintended impact by using only a few families. The size of sets varies from 2 to 15 sequences to assess the influence of the number of sequences on program performance. Alignment quality is scored by two measures: one takes into account only nucleotide matches, the other measures structural conservation. The performance order of parameters – like nucleotide substitution matrices and gap-costs – as well as of programs is rated by rank tests.</p> <p>Conclusion</p> <p>Most sequence alignment programs perform equally well on RNA sequence sets with high sequence identity, that is with an average pairwise sequence identity (APSI) above 75 %. Parameters for gap-open and gap-extension have a large influence on alignment quality lower than APSI ≤ 75 %; optimal parameter combinations are shown for several programs. The use of different 4 × 4 substitution matrices improved program performance only in some cases. The performance of iterative programs drastically increases with increasing sequence numbers and/or decreasing sequence identity, which makes them clearly superior to programs using a purely non-iterative, progressive approach. The best sequence alignment programs produce alignments of high quality down to APSI > 55 %; at lower APSI the use of sequence+structure alignment programs is recommended.</p>
url http://www.almob.org/content/1/1/19
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