PICS-Ord: unlimited coding of ambiguous regions by pairwise identity and cost scores ordination
Background We present a novel method to encode ambiguously aligned regions in fixed multiple sequence alignments by 'Pairwise Identity and Cost Scores Ordination' (PICS-Ord). The method works via ordination of sequence identity or cost scores matrices by means of Principal Coordinates Anal...
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| Vydáno v: | BMC bioinformatics Ročník 12; číslo 1; s. 10 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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London
BioMed Central
07.01.2011
BioMed Central Ltd Springer Nature B.V BMC |
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| ISSN: | 1471-2105, 1471-2105 |
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| Abstract | Background
We present a novel method to encode ambiguously aligned regions in fixed multiple sequence alignments by 'Pairwise Identity and Cost Scores Ordination' (PICS-Ord). The method works via ordination of sequence identity or cost scores matrices by means of Principal Coordinates Analysis (PCoA). After identification of ambiguous regions, the method computes pairwise distances as sequence identities or cost scores, ordinates the resulting distance matrix by means of PCoA, and encodes the principal coordinates as ordered integers. Three biological and 100 simulated datasets were used to assess the performance of the new method.
Results
Including ambiguous regions coded by means of PICS-Ord increased topological accuracy, resolution, and bootstrap support in real biological and simulated datasets compared to the alternative of excluding such regions from the analysis a priori. In terms of accuracy, PICS-Ord performs equal to or better than previously available methods of ambiguous region coding (e.g., INAASE), with the advantage of a practically unlimited alignment size and increased analytical speed and the possibility of PICS-Ord scores to be analyzed together with DNA data in a partitioned maximum likelihood model.
Conclusions
Advantages of PICS-Ord over step matrix-based ambiguous region coding with INAASE include a practically unlimited number of OTUs and seamless integration of PICS-Ord codes into phylogenetic datasets, as well as the increased speed of phylogenetic analysis. Contrary to word- and frequency-based methods, PICS-Ord maintains the advantage of pairwise sequence alignment to derive distances, and the method is flexible with respect to the calculation of distance scores. In addition to distance and maximum parsimony, PICS-Ord codes can be analyzed in a Bayesian or maximum likelihood framework. RAxML (version 7.2.6 or higher that was developed for this study) allows up to 32-state ordered or unordered characters. A GTR, MK, or ORDERED model can be applied to analyse the PICS-Ord codes partition, with GTR performing slightly better than MK and ORDERED.
Availability
An implementation of the PICS-Ord algorithm is available from
http://scit.us/projects/ngila/wiki/PICS-Ord
. It requires both the statistical software, R
http://www.r-project.org
and the alignment software Ngila
http://scit.us/projects/ngila
. |
|---|---|
| AbstractList | We present a novel method to encode ambiguously aligned regions in fixed multiple sequence alignments by 'Pairwise Identity and Cost Scores Ordination' (PICS-Ord). The method works via ordination of sequence identity or cost scores matrices by means of Principal Coordinates Analysis (PCoA). After identification of ambiguous regions, the method computes pairwise distances as sequence identities or cost scores, ordinates the resulting distance matrix by means of PCoA, and encodes the principal coordinates as ordered integers. Three biological and 100 simulated datasets were used to assess the performance of the new method.BACKGROUNDWe present a novel method to encode ambiguously aligned regions in fixed multiple sequence alignments by 'Pairwise Identity and Cost Scores Ordination' (PICS-Ord). The method works via ordination of sequence identity or cost scores matrices by means of Principal Coordinates Analysis (PCoA). After identification of ambiguous regions, the method computes pairwise distances as sequence identities or cost scores, ordinates the resulting distance matrix by means of PCoA, and encodes the principal coordinates as ordered integers. Three biological and 100 simulated datasets were used to assess the performance of the new method.Including ambiguous regions coded by means of PICS-Ord increased topological accuracy, resolution, and bootstrap support in real biological and simulated datasets compared to the alternative of excluding such regions from the analysis a priori. In terms of accuracy, PICS-Ord performs equal to or better than previously available methods of ambiguous region coding (e.g., INAASE), with the advantage of a practically unlimited alignment size and increased analytical speed and the possibility of PICS-Ord scores to be analyzed together with DNA data in a partitioned maximum likelihood model.RESULTSIncluding ambiguous regions coded by means of PICS-Ord increased topological accuracy, resolution, and bootstrap support in real biological and simulated datasets compared to the alternative of excluding such regions from the analysis a priori. In terms of accuracy, PICS-Ord performs equal to or better than previously available methods of ambiguous region coding (e.g., INAASE), with the advantage of a practically unlimited alignment size and increased analytical speed and the possibility of PICS-Ord scores to be analyzed together with DNA data in a partitioned maximum likelihood model.Advantages of PICS-Ord over step matrix-based ambiguous region coding with INAASE include a practically unlimited number of OTUs and seamless integration of PICS-Ord codes into phylogenetic datasets, as well as the increased speed of phylogenetic analysis. Contrary to word- and frequency-based methods, PICS-Ord maintains the advantage of pairwise sequence alignment to derive distances, and the method is flexible with respect to the calculation of distance scores. In addition to distance and maximum parsimony, PICS-Ord codes can be analyzed in a Bayesian or maximum likelihood framework. RAxML (version 7.2.6 or higher that was developed for this study) allows up to 32-state ordered or unordered characters. A GTR, MK, or ORDERED model can be applied to analyse the PICS-Ord codes partition, with GTR performing slightly better than MK and ORDERED.CONCLUSIONSAdvantages of PICS-Ord over step matrix-based ambiguous region coding with INAASE include a practically unlimited number of OTUs and seamless integration of PICS-Ord codes into phylogenetic datasets, as well as the increased speed of phylogenetic analysis. Contrary to word- and frequency-based methods, PICS-Ord maintains the advantage of pairwise sequence alignment to derive distances, and the method is flexible with respect to the calculation of distance scores. In addition to distance and maximum parsimony, PICS-Ord codes can be analyzed in a Bayesian or maximum likelihood framework. RAxML (version 7.2.6 or higher that was developed for this study) allows up to 32-state ordered or unordered characters. A GTR, MK, or ORDERED model can be applied to analyse the PICS-Ord codes partition, with GTR performing slightly better than MK and ORDERED.An implementation of the PICS-Ord algorithm is available from http://scit.us/projects/ngila/wiki/PICS-Ord. It requires both the statistical software, R http://www.r-project.org and the alignment software Ngila http://scit.us/projects/ngila.AVAILABILITYAn implementation of the PICS-Ord algorithm is available from http://scit.us/projects/ngila/wiki/PICS-Ord. It requires both the statistical software, R http://www.r-project.org and the alignment software Ngila http://scit.us/projects/ngila. We present a novel method to encode ambiguously aligned regions in fixed multiple sequence alignments by 'Pairwise Identity and Cost Scores Ordination' (PICS-Ord). The method works via ordination of sequence identity or cost scores matrices by means of Principal Coordinates Analysis (PCoA). After identification of ambiguous regions, the method computes pairwise distances as sequence identities or cost scores, ordinates the resulting distance matrix by means of PCoA, and encodes the principal coordinates as ordered integers. Three biological and 100 simulated datasets were used to assess the performance of the new method. Including ambiguous regions coded by means of PICS-Ord increased topological accuracy, resolution, and bootstrap support in real biological and simulated datasets compared to the alternative of excluding such regions from the analysis a priori. In terms of accuracy, PICS-Ord performs equal to or better than previously available methods of ambiguous region coding (e.g., INAASE), with the advantage of a practically unlimited alignment size and increased analytical speed and the possibility of PICS-Ord scores to be analyzed together with DNA data in a partitioned maximum likelihood model. Advantages of PICS-Ord over step matrix-based ambiguous region coding with INAASE include a practically unlimited number of OTUs and seamless integration of PICS-Ord codes into phylogenetic datasets, as well as the increased speed of phylogenetic analysis. Contrary to word- and frequency-based methods, PICS-Ord maintains the advantage of pairwise sequence alignment to derive distances, and the method is flexible with respect to the calculation of distance scores. In addition to distance and maximum parsimony, PICS-Ord codes can be analyzed in a Bayesian or maximum likelihood framework. RAxML (version 7.2.6 or higher that was developed for this study) allows up to 32-state ordered or unordered characters. A GTR, MK, or ORDERED model can be applied to analyse the PICS-Ord codes partition, with GTR performing slightly better than MK and ORDERED. An implementation of the PICS-Ord algorithm is available from http://scit.us/projects/ngila/wiki/PICS-Ord. It requires both the statistical software, R http://www.r-project.org and the alignment software Ngila http://scit.us/projects/ngila. We present a novel method to encode ambiguously aligned regions in fixed multiple sequence alignments by 'Pairwise Identity and Cost Scores Ordination' (PICS-Ord). The method works via ordination of sequence identity or cost scores matrices by means of Principal Coordinates Analysis (PCoA). After identification of ambiguous regions, the method computes pairwise distances as sequence identities or cost scores, ordinates the resulting distance matrix by means of PCoA, and encodes the principal coordinates as ordered integers. Three biological and 100 simulated datasets were used to assess the performance of the new method. Including ambiguous regions coded by means of PICS-Ord increased topological accuracy, resolution, and bootstrap support in real biological and simulated datasets compared to the alternative of excluding such regions from the analysis a priori. In terms of accuracy, PICS-Ord performs equal to or better than previously available methods of ambiguous region coding (e.g., INAASE), with the advantage of a practically unlimited alignment size and increased analytical speed and the possibility of PICS-Ord scores to be analyzed together with DNA data in a partitioned maximum likelihood model. Advantages of PICS-Ord over step matrix-based ambiguous region coding with INAASE include a practically unlimited number of OTUs and seamless integration of PICS-Ord codes into phylogenetic datasets, as well as the increased speed of phylogenetic analysis. Contrary to word- and frequency-based methods, PICS-Ord maintains the advantage of pairwise sequence alignment to derive distances, and the method is flexible with respect to the calculation of distance scores. In addition to distance and maximum parsimony, PICS-Ord codes can be analyzed in a Bayesian or maximum likelihood framework. RAxML (version 7.2.6 or higher that was developed for this study) allows up to 32-state ordered or unordered characters. A GTR, MK, or ORDERED model can be applied to analyse the PICS-Ord codes partition, with GTR performing slightly better than MK and ORDERED. An implementation of the PICS-Ord algorithm is available from http://scit.us/projects/ngila/wiki/PICS-Ord. It requires both the statistical software, R http://www.r-project.org and the alignment software Ngila http://scit.us/projects/ngila. We present a novel method to encode ambiguously aligned regions in fixed multiple sequence alignments by 'Pairwise Identity and Cost Scores Ordination' (PICS-Ord). The method works via ordination of sequence identity or cost scores matrices by means of Principal Coordinates Analysis (PCoA). After identification of ambiguous regions, the method computes pairwise distances as sequence identities or cost scores, ordinates the resulting distance matrix by means of PCoA, and encodes the principal coordinates as ordered integers. Three biological and 100 simulated datasets were used to assess the performance of the new method. Including ambiguous regions coded by means of PICS-Ord increased topological accuracy, resolution, and bootstrap support in real biological and simulated datasets compared to the alternative of excluding such regions from the analysis a priori. In terms of accuracy, PICS-Ord performs equal to or better than previously available methods of ambiguous region coding (e.g., INAASE), with the advantage of a practically unlimited alignment size and increased analytical speed and the possibility of PICS-Ord scores to be analyzed together with DNA data in a partitioned maximum likelihood model. Advantages of PICS-Ord over step matrix-based ambiguous region coding with INAASE include a practically unlimited number of OTUs and seamless integration of PICS-Ord codes into phylogenetic datasets, as well as the increased speed of phylogenetic analysis. Contrary to word- and frequency-based methods, PICS-Ord maintains the advantage of pairwise sequence alignment to derive distances, and the method is flexible with respect to the calculation of distance scores. In addition to distance and maximum parsimony, PICS-Ord codes can be analyzed in a Bayesian or maximum likelihood framework. RAxML (version 7.2.6 or higher that was developed for this study) allows up to 32-state ordered or unordered characters. A GTR, MK, or ORDERED model can be applied to analyse the PICS-Ord codes partition, with GTR performing slightly better than MK and ORDERED. Background We present a novel method to encode ambiguously aligned regions in fixed multiple sequence alignments by 'Pairwise Identity and Cost Scores Ordination' (PICS-Ord). The method works via ordination of sequence identity or cost scores matrices by means of Principal Coordinates Analysis (PCoA). After identification of ambiguous regions, the method computes pairwise distances as sequence identities or cost scores, ordinates the resulting distance matrix by means of PCoA, and encodes the principal coordinates as ordered integers. Three biological and 100 simulated datasets were used to assess the performance of the new method. Results Including ambiguous regions coded by means of PICS-Ord increased topological accuracy, resolution, and bootstrap support in real biological and simulated datasets compared to the alternative of excluding such regions from the analysis a priori. In terms of accuracy, PICS-Ord performs equal to or better than previously available methods of ambiguous region coding (e.g., INAASE), with the advantage of a practically unlimited alignment size and increased analytical speed and the possibility of PICS-Ord scores to be analyzed together with DNA data in a partitioned maximum likelihood model. Conclusions Advantages of PICS-Ord over step matrix-based ambiguous region coding with INAASE include a practically unlimited number of OTUs and seamless integration of PICS-Ord codes into phylogenetic datasets, as well as the increased speed of phylogenetic analysis. Contrary to word- and frequency-based methods, PICS-Ord maintains the advantage of pairwise sequence alignment to derive distances, and the method is flexible with respect to the calculation of distance scores. In addition to distance and maximum parsimony, PICS-Ord codes can be analyzed in a Bayesian or maximum likelihood framework. RAxML (version 7.2.6 or higher that was developed for this study) allows up to 32-state ordered or unordered characters. A GTR, MK, or ORDERED model can be applied to analyse the PICS-Ord codes partition, with GTR performing slightly better than MK and ORDERED. Availability An implementation of the PICS-Ord algorithm is available from Abstract Background We present a novel method to encode ambiguously aligned regions in fixed multiple sequence alignments by 'Pairwise Identity and Cost Scores Ordination' (PICS-Ord). The method works via ordination of sequence identity or cost scores matrices by means of Principal Coordinates Analysis (PCoA). After identification of ambiguous regions, the method computes pairwise distances as sequence identities or cost scores, ordinates the resulting distance matrix by means of PCoA, and encodes the principal coordinates as ordered integers. Three biological and 100 simulated datasets were used to assess the performance of the new method. Results Including ambiguous regions coded by means of PICS-Ord increased topological accuracy, resolution, and bootstrap support in real biological and simulated datasets compared to the alternative of excluding such regions from the analysis a priori. In terms of accuracy, PICS-Ord performs equal to or better than previously available methods of ambiguous region coding (e.g., INAASE), with the advantage of a practically unlimited alignment size and increased analytical speed and the possibility of PICS-Ord scores to be analyzed together with DNA data in a partitioned maximum likelihood model. Conclusions Advantages of PICS-Ord over step matrix-based ambiguous region coding with INAASE include a practically unlimited number of OTUs and seamless integration of PICS-Ord codes into phylogenetic datasets, as well as the increased speed of phylogenetic analysis. Contrary to word- and frequency-based methods, PICS-Ord maintains the advantage of pairwise sequence alignment to derive distances, and the method is flexible with respect to the calculation of distance scores. In addition to distance and maximum parsimony, PICS-Ord codes can be analyzed in a Bayesian or maximum likelihood framework. RAxML (version 7.2.6 or higher that was developed for this study) allows up to 32-state ordered or unordered characters. A GTR, MK, or ORDERED model can be applied to analyse the PICS-Ord codes partition, with GTR performing slightly better than MK and ORDERED. Availability An implementation of the PICS-Ord algorithm is available from http://scit.us/projects/ngila/wiki/PICS-Ord. It requires both the statistical software, R http://www.r-project.org and the alignment software Ngila http://scit.us/projects/ngila. Background We present a novel method to encode ambiguously aligned regions in fixed multiple sequence alignments by 'Pairwise Identity and Cost Scores Ordination' (PICS-Ord). The method works via ordination of sequence identity or cost scores matrices by means of Principal Coordinates Analysis (PCoA). After identification of ambiguous regions, the method computes pairwise distances as sequence identities or cost scores, ordinates the resulting distance matrix by means of PCoA, and encodes the principal coordinates as ordered integers. Three biological and 100 simulated datasets were used to assess the performance of the new method. Results Including ambiguous regions coded by means of PICS-Ord increased topological accuracy, resolution, and bootstrap support in real biological and simulated datasets compared to the alternative of excluding such regions from the analysis a priori. In terms of accuracy, PICS-Ord performs equal to or better than previously available methods of ambiguous region coding (e.g., INAASE), with the advantage of a practically unlimited alignment size and increased analytical speed and the possibility of PICS-Ord scores to be analyzed together with DNA data in a partitioned maximum likelihood model. Conclusions Advantages of PICS-Ord over step matrix-based ambiguous region coding with INAASE include a practically unlimited number of OTUs and seamless integration of PICS-Ord codes into phylogenetic datasets, as well as the increased speed of phylogenetic analysis. Contrary to word- and frequency-based methods, PICS-Ord maintains the advantage of pairwise sequence alignment to derive distances, and the method is flexible with respect to the calculation of distance scores. In addition to distance and maximum parsimony, PICS-Ord codes can be analyzed in a Bayesian or maximum likelihood framework. RAxML (version 7.2.6 or higher that was developed for this study) allows up to 32-state ordered or unordered characters. A GTR, MK, or ORDERED model can be applied to analyse the PICS-Ord codes partition, with GTR performing slightly better than MK and ORDERED. Availability An implementation of the PICS-Ord algorithm is available from http://scit.us/projects/ngila/wiki/PICS-Ord . It requires both the statistical software, R http://www.r-project.org and the alignment software Ngila http://scit.us/projects/ngila . |
| ArticleNumber | 10 |
| Audience | Academic |
| Author | Lücking, Robert Cartwright, Reed A Stamatakis, Alexandros Hodkinson, Brendan P |
| AuthorAffiliation | 4 Department of Biology and Biochemistry, University of Houston, 369 Science & Research Bldg 2, Houston, TX 77204-5001, USA 1 Department of Botany, The Field Museum, 1400 South Lake Shore Drive, Chicago, IL 60605-2496, USA 3 The Exelixis Lab, Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, D-69118, Heidelberg, Germany 2 Department of Biology, Box 90338, Duke University, Durham, NC 27708-0338, USA |
| AuthorAffiliation_xml | – name: 3 The Exelixis Lab, Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, D-69118, Heidelberg, Germany – name: 2 Department of Biology, Box 90338, Duke University, Durham, NC 27708-0338, USA – name: 1 Department of Botany, The Field Museum, 1400 South Lake Shore Drive, Chicago, IL 60605-2496, USA – name: 4 Department of Biology and Biochemistry, University of Houston, 369 Science & Research Bldg 2, Houston, TX 77204-5001, USA |
| Author_xml | – sequence: 1 givenname: Robert surname: Lücking fullname: Lücking, Robert email: rlucking@fieldmuseum.org organization: Department of Botany, The Field Museum – sequence: 2 givenname: Brendan P surname: Hodkinson fullname: Hodkinson, Brendan P organization: Department of Biology, Duke University – sequence: 3 givenname: Alexandros surname: Stamatakis fullname: Stamatakis, Alexandros organization: The Exelixis Lab, Scientific Computing Group, Heidelberg Institute for Theoretical Studies – sequence: 4 givenname: Reed A surname: Cartwright fullname: Cartwright, Reed A organization: Department of Biology and Biochemistry, University of Houston |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/21214904$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_3732_ajb_1200146 crossref_primary_10_3852_12_338 crossref_primary_10_5598_imafungus_2018_09_01_09 crossref_primary_10_1016_j_ympev_2016_02_010 crossref_primary_10_1093_sysbio_syw065 crossref_primary_10_1007_s11557_023_01912_6 crossref_primary_10_2179_11_042 crossref_primary_10_1016_j_funbio_2015_07_003 crossref_primary_10_1093_sysbio_syy036 crossref_primary_10_1016_j_ympev_2013_04_005 crossref_primary_10_1017_S0024282916000232 crossref_primary_10_1639_007_2745_115_1_183 crossref_primary_10_1017_S0024282912000825 crossref_primary_10_1007_s11557_024_02011_w crossref_primary_10_1017_S0024282913000212 |
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| ContentType | Journal Article |
| Copyright | Lücking et al; licensee BioMed Central Ltd. 2011 COPYRIGHT 2011 BioMed Central Ltd. 2011 Lücking et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Copyright ©2011 Lücking et al; licensee BioMed Central Ltd. 2011 Lücking et al; licensee BioMed Central Ltd. |
| Copyright_xml | – notice: Lücking et al; licensee BioMed Central Ltd. 2011 – notice: COPYRIGHT 2011 BioMed Central Ltd. – notice: 2011 Lücking et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. – notice: Copyright ©2011 Lücking et al; licensee BioMed Central Ltd. 2011 Lücking et al; licensee BioMed Central Ltd. |
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| Keywords | Simulated Dataset Internal Transcribe Spacer Ambiguous Region Phylogenetic Signal Biological Dataset |
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| Snippet | Background
We present a novel method to encode ambiguously aligned regions in fixed multiple sequence alignments by 'Pairwise Identity and Cost Scores... We present a novel method to encode ambiguously aligned regions in fixed multiple sequence alignments by 'Pairwise Identity and Cost Scores Ordination'... Background We present a novel method to encode ambiguously aligned regions in fixed multiple sequence alignments by 'Pairwise Identity and Cost Scores... Abstract Background: We present a novel method to encode ambiguously aligned regions in fixed multiple sequence alignments by 'Pairwise Identity and Cost... Abstract Background We present a novel method to encode ambiguously aligned regions in fixed multiple sequence alignments by 'Pairwise Identity and Cost Scores... |
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| SubjectTerms | Algorithms Bioinformatics Biomedical and Life Sciences Cladistic analysis Computational Biology - methods Computational Biology/Bioinformatics Computer Appl. in Life Sciences Computer Simulation DNA - genetics DNA sequencing Lichens - genetics Life Sciences Likelihood Functions Methodology Methodology Article Methods Microarrays Nucleotide sequencing Ordination Performance assessment Phylogeny Principal Component Analysis Sequence Alignment - methods Sequence Analysis, DNA - methods |
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| Title | PICS-Ord: unlimited coding of ambiguous regions by pairwise identity and cost scores ordination |
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