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

Roman Prytuliak (1) , Friedhelm Pfeiffer (1) , Bianca Habermann (2)

1 MPIB - Max-Planck-Institut für Biochemie = Max Planck Institute of Biochemistry
2 IBDM - Institut de Biologie du Développement de Marseille

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.

Domains: Computer Science [cs]; Bioinformatics [q-bio.QM]; Mathematics [math]; Statistics [math.ST]

Roman Prytuliak, Friedhelm Pfeiffer, Bianca Habermann. SLALOM, a flexible method for the identification and statistical analysis of overlapping continuous sequence elements in sequence-and time-series data. BMC Bioinformatics, 2018, 19 (1), ⟨10.1186/s12859-018-2020-x⟩. ⟨hal-02393094⟩