dorsal/arxiv
View SchemaAlignment Metric Accuracy
| Authors | Ariel S. Schwartz, Eugene W. Myers, Lior Pachter |
|---|---|
| Categories | |
| ArXiv ID | q-bio/0510052 |
| URL | https://arxiv.org/abs/q-bio/0510052 |
Abstract
We propose a metric for the space of multiple sequence alignments that can be used to compare two alignments to each other. In the case where one of the alignments is a reference alignment, the resulting accuracy measure improves upon previous approaches, and provides a balanced assessment of the fidelity of both matches and gaps. Furthermore, in the case where a reference alignment is not available, we provide empirical evidence that the distance from an alignment produced by one program to predicted alignments from other programs can be used as a control for multiple alignment experiments. In particular, we show that low accuracy alignments can be effectively identified and discarded. We also show that in the case of pairwise sequence alignment, it is possible to find an alignment that maximizes the expected value of our accuracy measure. Unlike previous approaches based on expected accuracy alignment that tend to maximize sensitivity at the expense of specificity, our method is able to identify unalignable sequence, thereby increasing overall accuracy. In addition, the algorithm allows for control of the sensitivity/specificity tradeoff via the adjustment of a single parameter. These results are confirmed with simulation studies that show that unalignable regions can be distinguished from homologous, conserved sequences. Finally, we propose an extension of the pairwise alignment method to multiple alignment. Our method, which we call AMAP, outperforms existing protein sequence multiple alignment programs on benchmark datasets. A webserver and software downloads are available at http://bio.math.berkeley.edu/amap/ .
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"abstract": "We propose a metric for the space of multiple sequence alignments that can be\nused to compare two alignments to each other. In the case where one of the\nalignments is a reference alignment, the resulting accuracy measure improves\nupon previous approaches, and provides a balanced assessment of the fidelity of\nboth matches and gaps. Furthermore, in the case where a reference alignment is\nnot available, we provide empirical evidence that the distance from an\nalignment produced by one program to predicted alignments from other programs\ncan be used as a control for multiple alignment experiments. In particular, we\nshow that low accuracy alignments can be effectively identified and discarded.\nWe also show that in the case of pairwise sequence alignment, it is possible to\nfind an alignment that maximizes the expected value of our accuracy measure.\nUnlike previous approaches based on expected accuracy alignment that tend to\nmaximize sensitivity at the expense of specificity, our method is able to\nidentify unalignable sequence, thereby increasing overall accuracy. In\naddition, the algorithm allows for control of the sensitivity/specificity\ntradeoff via the adjustment of a single parameter. These results are confirmed\nwith simulation studies that show that unalignable regions can be distinguished\nfrom homologous, conserved sequences. Finally, we propose an extension of the\npairwise alignment method to multiple alignment. Our method, which we call\nAMAP, outperforms existing protein sequence multiple alignment programs on\nbenchmark datasets. A webserver and software downloads are available at\nhttp://bio.math.berkeley.edu/amap/ .",
"arxiv_id": "q-bio/0510052",
"authors": [
"Ariel S. Schwartz",
"Eugene W. Myers",
"Lior Pachter"
],
"categories": [
"q-bio.QM",
"math.ST",
"stat.TH"
],
"title": "Alignment Metric Accuracy",
"url": "https://arxiv.org/abs/q-bio/0510052"
},
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"variant": "snapshot-2026-03-01",
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