EMMA: a new method for computing multiple sequence alignments given a constraint subset alignment
Abstract Background Adding sequences into an existing (possibly user-provided) alignment has multiple applications, including updating a large alignment with new data, adding sequences into a constraint alignment constructed using biological knowledge, or computing alignments in the presence of sequ...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-12-01
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| Series: | Algorithms for Molecular Biology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13015-023-00247-x |
| _version_ | 1797398132879785984 |
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| author | Chengze Shen Baqiao Liu Kelly P. Williams Tandy Warnow |
| author_facet | Chengze Shen Baqiao Liu Kelly P. Williams Tandy Warnow |
| author_sort | Chengze Shen |
| collection | DOAJ |
| description | Abstract Background Adding sequences into an existing (possibly user-provided) alignment has multiple applications, including updating a large alignment with new data, adding sequences into a constraint alignment constructed using biological knowledge, or computing alignments in the presence of sequence length heterogeneity. Although this is a natural problem, only a few tools have been developed to use this information with high fidelity. Results We present EMMA (Extending Multiple alignments using MAFFT--add) for the problem of adding a set of unaligned sequences into a multiple sequence alignment (i.e., a constraint alignment). EMMA builds on MAFFT--add, which is also designed to add sequences into a given constraint alignment. EMMA improves on MAFFT--add methods by using a divide-and-conquer framework to scale its most accurate version, MAFFT-linsi--add, to constraint alignments with many sequences. We show that EMMA has an accuracy advantage over other techniques for adding sequences into alignments under many realistic conditions and can scale to large datasets with high accuracy (hundreds of thousands of sequences). EMMA is available at https://github.com/c5shen/EMMA . Conclusions EMMA is a new tool that provides high accuracy and scalability for adding sequences into an existing alignment. |
| first_indexed | 2024-03-09T01:21:18Z |
| format | Article |
| id | doaj.art-3ff599092538409eb4e3b3b0db256f89 |
| institution | Directory Open Access Journal |
| issn | 1748-7188 |
| language | English |
| last_indexed | 2024-03-09T01:21:18Z |
| publishDate | 2023-12-01 |
| publisher | BMC |
| record_format | Article |
| series | Algorithms for Molecular Biology |
| spelling | doaj.art-3ff599092538409eb4e3b3b0db256f892023-12-10T12:07:38ZengBMCAlgorithms for Molecular Biology1748-71882023-12-0118111410.1186/s13015-023-00247-xEMMA: a new method for computing multiple sequence alignments given a constraint subset alignmentChengze Shen0Baqiao Liu1Kelly P. Williams2Tandy Warnow3Computer Science, University of Illinois, Urbana-ChampaignComputer Science, University of Illinois, Urbana-ChampaignSandia National LaboratoriesComputer Science, University of Illinois, Urbana-ChampaignAbstract Background Adding sequences into an existing (possibly user-provided) alignment has multiple applications, including updating a large alignment with new data, adding sequences into a constraint alignment constructed using biological knowledge, or computing alignments in the presence of sequence length heterogeneity. Although this is a natural problem, only a few tools have been developed to use this information with high fidelity. Results We present EMMA (Extending Multiple alignments using MAFFT--add) for the problem of adding a set of unaligned sequences into a multiple sequence alignment (i.e., a constraint alignment). EMMA builds on MAFFT--add, which is also designed to add sequences into a given constraint alignment. EMMA improves on MAFFT--add methods by using a divide-and-conquer framework to scale its most accurate version, MAFFT-linsi--add, to constraint alignments with many sequences. We show that EMMA has an accuracy advantage over other techniques for adding sequences into alignments under many realistic conditions and can scale to large datasets with high accuracy (hundreds of thousands of sequences). EMMA is available at https://github.com/c5shen/EMMA . Conclusions EMMA is a new tool that provides high accuracy and scalability for adding sequences into an existing alignment.https://doi.org/10.1186/s13015-023-00247-xMultiple sequence alignmentConstraint alignmentMAFFT |
| spellingShingle | Chengze Shen Baqiao Liu Kelly P. Williams Tandy Warnow EMMA: a new method for computing multiple sequence alignments given a constraint subset alignment Algorithms for Molecular Biology Multiple sequence alignment Constraint alignment MAFFT |
| title | EMMA: a new method for computing multiple sequence alignments given a constraint subset alignment |
| title_full | EMMA: a new method for computing multiple sequence alignments given a constraint subset alignment |
| title_fullStr | EMMA: a new method for computing multiple sequence alignments given a constraint subset alignment |
| title_full_unstemmed | EMMA: a new method for computing multiple sequence alignments given a constraint subset alignment |
| title_short | EMMA: a new method for computing multiple sequence alignments given a constraint subset alignment |
| title_sort | emma a new method for computing multiple sequence alignments given a constraint subset alignment |
| topic | Multiple sequence alignment Constraint alignment MAFFT |
| url | https://doi.org/10.1186/s13015-023-00247-x |
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