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GUIDANCE2: accurate detection of unreliable alignment regions accounting for the uncertainty of multiple parameters.

Sela I, Ashkenazy H, Katoh K, Pupko T - Nucleic Acids Res. (2015)

Bottom Line: Here we developed GUIDANCE2, a new integrative methodology that accounts for: (i) uncertainty in the process of indel formation, (ii) uncertainty in the assumed guide tree and (iii) co-optimal solutions in the pairwise alignments, used as building blocks in progressive alignment algorithms.We compared GUIDANCE2 with seven methodologies to detect unreliable MSA regions using extensive simulations and empirical benchmarks.We show that GUIDANCE2 outperforms all previously developed methodologies.

View Article: PubMed Central - PubMed

Affiliation: Department of Cell Research and Immunology, George S. Wise Faculty of Life Sciences, Tel-Aviv University, Tel-Aviv 6997801, Israel.

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ROC curve for the performance of each GUIDANCE2 component (gap opening penalty variation is denoted as gap penalty) in detecting unreliably aligned regions for (A) BAliBASE, (B) OrthoMaM simulations and (C) simulations of the ZORRO paper (the ZORRO simulated data set) are shown. AUC-ROC for each component is indicated in parentheses.
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Figure 3: ROC curve for the performance of each GUIDANCE2 component (gap opening penalty variation is denoted as gap penalty) in detecting unreliably aligned regions for (A) BAliBASE, (B) OrthoMaM simulations and (C) simulations of the ZORRO paper (the ZORRO simulated data set) are shown. AUC-ROC for each component is indicated in parentheses.

Mentions: As an improved combined method, GUIDANCE2 outperforms all its components when considered separately on MAFFT alignments (Figure 3): relaying on uncertainty in the guide trees (GUIDANCE), uncertainty over alternative co-optimal solutions (HoT) or uncertainty in gap penalty values. This analysis suggests that in order to generate alternative MSAs for the purpose of detecting unreliable alignment regions, for the BAliBASE data the most important factor is the gap opening score, followed by the guide tree, and the HoT component (sampling alternative MSAs with the highest score) is the least important factor (Figure 3A). For the OrthoMaM simulations, the most important factor was the guide tree uncertainty, the second was the HoT component, and the gap opening score contributed the least (Figure 3B). The contribution of each component was calculated also for the ZORRO-simulated data set. Here, the guide-tree uncertainty component (GUIDANCE) contributed the most, followed by the gap opening score, and the least was contributed by the HoT component (Figure 3C). This demonstrates that the contributions of the different components vary among data sets, further suggesting the need to integrate them within a single methodology. Notably, the gap extension score was insignificant in its contribution to the generation of alternative MSAs and was thus not included in GUIDANCE2 (data not shown).


GUIDANCE2: accurate detection of unreliable alignment regions accounting for the uncertainty of multiple parameters.

Sela I, Ashkenazy H, Katoh K, Pupko T - Nucleic Acids Res. (2015)

ROC curve for the performance of each GUIDANCE2 component (gap opening penalty variation is denoted as gap penalty) in detecting unreliably aligned regions for (A) BAliBASE, (B) OrthoMaM simulations and (C) simulations of the ZORRO paper (the ZORRO simulated data set) are shown. AUC-ROC for each component is indicated in parentheses.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4489236&req=5

Figure 3: ROC curve for the performance of each GUIDANCE2 component (gap opening penalty variation is denoted as gap penalty) in detecting unreliably aligned regions for (A) BAliBASE, (B) OrthoMaM simulations and (C) simulations of the ZORRO paper (the ZORRO simulated data set) are shown. AUC-ROC for each component is indicated in parentheses.
Mentions: As an improved combined method, GUIDANCE2 outperforms all its components when considered separately on MAFFT alignments (Figure 3): relaying on uncertainty in the guide trees (GUIDANCE), uncertainty over alternative co-optimal solutions (HoT) or uncertainty in gap penalty values. This analysis suggests that in order to generate alternative MSAs for the purpose of detecting unreliable alignment regions, for the BAliBASE data the most important factor is the gap opening score, followed by the guide tree, and the HoT component (sampling alternative MSAs with the highest score) is the least important factor (Figure 3A). For the OrthoMaM simulations, the most important factor was the guide tree uncertainty, the second was the HoT component, and the gap opening score contributed the least (Figure 3B). The contribution of each component was calculated also for the ZORRO-simulated data set. Here, the guide-tree uncertainty component (GUIDANCE) contributed the most, followed by the gap opening score, and the least was contributed by the HoT component (Figure 3C). This demonstrates that the contributions of the different components vary among data sets, further suggesting the need to integrate them within a single methodology. Notably, the gap extension score was insignificant in its contribution to the generation of alternative MSAs and was thus not included in GUIDANCE2 (data not shown).

Bottom Line: Here we developed GUIDANCE2, a new integrative methodology that accounts for: (i) uncertainty in the process of indel formation, (ii) uncertainty in the assumed guide tree and (iii) co-optimal solutions in the pairwise alignments, used as building blocks in progressive alignment algorithms.We compared GUIDANCE2 with seven methodologies to detect unreliable MSA regions using extensive simulations and empirical benchmarks.We show that GUIDANCE2 outperforms all previously developed methodologies.

View Article: PubMed Central - PubMed

Affiliation: Department of Cell Research and Immunology, George S. Wise Faculty of Life Sciences, Tel-Aviv University, Tel-Aviv 6997801, Israel.

Show MeSH
Related in: MedlinePlus