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SLALOM, a flexible method for the identification and statistical analysis of overlapping continuous sequence elements in sequence- and time-series data

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Prytuliak,  Roman
Habermann, Bianca / Computational Biology, Max Planck Institute of Biochemistry, Max Planck Society;

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Pfeiffer,  Friedhelm
Habermann, Bianca / Computational Biology, Max Planck Institute of Biochemistry, Max Planck Society;

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Habermann,  Bianca Hermine
Habermann, Bianca / Computational Biology, Max Planck Institute of Biochemistry, Max Planck Society;

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Fulltext (public)

Prytuliak.pdf
(Publisher version), 825KB

Supplementary Material (public)

3988656.zip
(Supplementary material), 5MB

Citation

Prytuliak, R., Pfeiffer, F., & Habermann, B. H. (2018). SLALOM, a flexible method for the identification and statistical analysis of overlapping continuous sequence elements in sequence- and time-series data. BMC Bioinformatics, 19: 24. doi:10.1186/s12859-018-2020-x.


Cite as: http://hdl.handle.net/21.11116/0000-0000-B124-F
Abstract
Background: Protein or nucleic acid sequences contain a multitude of associated annotations representing continuous sequence elements (CSEs). Comparing these CSEs is needed, whenever we want to match identical annotations or integrate distinctive ones. Currently, there is no ready-to-use software available that provides comprehensive statistical readout for comparing two annotations of the same type with each other, which can be adapted to the application logic of the scientific question. Results: We have developed a method, SLALOM (for StatisticaL Analysis of Locus Overlap Method), to perform comparative analysis of sequence annotations in a highly flexible way. SLALOM implements six major operation modes and a number of additional options that can answer a variety of statistical questions about a pair of input annotations of a given sequence collection. We demonstrate the results of SLALOM on three different examples from biology and economics and compare our method to already existing software. We discuss the importance of carefully choosing the application logic to address specific scientific questions. Conclusion: SLALOM is a highly versatile, command-line based method for comparing annotations in a collection of sequences, with a statistical read-out for performance evaluation and benchmarking of predictors and gene annotation pipelines. Abstraction from sequence content even allows SLALOM to compare other kinds of positional data including, for example, data coming from time series.