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SABmark---a benchmark for sequence alignment that covers the entire known fold space

Authors:

Ivo Van Walle,

Ignace Lasters,

Lode WynsAuthors Info & Claims

Bioinformatics, Volume 21, Issue 7

Pages 1267 - 1268

https://doi.org/10.1093/bioinformatics/bth493

Published: 01 April 2005 Publication History

Abstract

Summary: The Sequence Alignment Benchmark (SABmark) provides sets of multiple alignment problems derived from the SCOP classification. These sets, Twilight Zone and Superfamilies, both cover the entire known fold space using sequences with very low to low, and low to intermediate similarity, respectively. In addition, each set has an alternate version in which unalignable but apparently similar sequences are added to each problem.

Availability: SABmark is available from http://bioinformatics.vub.ac.be

Contact: [email protected]

Cited By

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Kuang MLiu YGao L(2020)DLPAlign: A Deep Learning based Progressive Alignment Method for Multiple Protein SequencesCSBio '20: Proceedings of the Eleventh International Conference on Computational Systems-Biology and Bioinformatics10.1145/3429210.3429221(83-92)Online publication date: 19-Nov-2020
https://dl.acm.org/doi/10.1145/3429210.3429221
Chowdhury BGarai G(2020)A bi-objective function optimization approach for multiple sequence alignment using genetic algorithmSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-020-04917-524:20(15871-15888)Online publication date: 1-Oct-2020
https://dl.acm.org/doi/10.1007/s00500-020-04917-5
Fotoohifiroozabadi SMohamad MDeris S(2018)NAHAL-FlexIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2017.270508015:3(934-943)Online publication date: 1-May-2018
https://dl.acm.org/doi/10.1109/TCBB.2017.2705080
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SABmark---a benchmark for sequence alignment that covers the entire known fold space

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Published In

Bioinformatics Volume 21, Issue 7

April 2005

460 pages

ISSN:1367-4803

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Oxford University Press, Inc.

United States

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Published: 01 April 2005

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Kuang MLiu YGao L(2020)DLPAlign: A Deep Learning based Progressive Alignment Method for Multiple Protein SequencesCSBio '20: Proceedings of the Eleventh International Conference on Computational Systems-Biology and Bioinformatics10.1145/3429210.3429221(83-92)Online publication date: 19-Nov-2020
https://dl.acm.org/doi/10.1145/3429210.3429221
Chowdhury BGarai G(2020)A bi-objective function optimization approach for multiple sequence alignment using genetic algorithmSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-020-04917-524:20(15871-15888)Online publication date: 1-Oct-2020
https://dl.acm.org/doi/10.1007/s00500-020-04917-5
Fotoohifiroozabadi SMohamad MDeris S(2018)NAHAL-FlexIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2017.270508015:3(934-943)Online publication date: 1-May-2018
https://dl.acm.org/doi/10.1109/TCBB.2017.2705080
DeBlasio DKececioglu J(2017)Learning Parameter-Advising Sets for Multiple Sequence AlignmentIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2015.243032314:5(1028-1041)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1109/TCBB.2015.2430323
Rubio-Largo lVanneschi LCastelli MVega-Rodrguez M(2017)Using biological knowledge for multiple sequence aligner decision makingInformation Sciences: an International Journal10.1016/j.ins.2017.08.069420:C(278-298)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1016/j.ins.2017.08.069
Deblasio DKececioglu J(2017)Boosting Alignment Accuracy by Adaptive Local Realignment21st Annual International Conference on Research in Computational Molecular Biology - Volume 1022910.1007/978-3-319-56970-3_1(1-17)Online publication date: 3-May-2017
https://dl.acm.org/doi/10.1007/978-3-319-56970-3_1
Rubio-Largo ÁVega-Rodríguez MGonzález-Álvarez D(2016)A Hybrid Multiobjective Memetic Metaheuristic for Multiple Sequence AlignmentIEEE Transactions on Evolutionary Computation10.1109/TEVC.2015.246954620:4(499-514)Online publication date: 1-Aug-2016
https://dl.acm.org/doi/10.1109/TEVC.2015.2469546
DeBlasio DKececioglu J(2015)Ensemble multiple sequence alignment via advisingProceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics10.1145/2808719.2808766(452-461)Online publication date: 9-Sep-2015
https://dl.acm.org/doi/10.1145/2808719.2808766
Ortuño FValenzuela OPrieto BSaez-Lara MTorres CPomares HRojas I(2015)Comparing different machine learning and mathematical regression models to evaluate multiple sequence alignmentsNeurocomputing10.1016/j.neucom.2015.01.080164:C(123-136)Online publication date: 21-Sep-2015
https://dl.acm.org/doi/10.1016/j.neucom.2015.01.080
DeBlasio DKececioglu JBaldi PWang W(2014)Learning parameter sets for alignment advisingProceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics10.1145/2649387.2649448(230-239)Online publication date: 20-Sep-2014
https://dl.acm.org/doi/10.1145/2649387.2649448
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Multiple sequence alignment based on set covers

Space Efficient Diagonal Linear Space Sequence Alignment

On multiple sequence alignment

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