The Power of Word-Frequency Based Alignment-Free Functions: a Comprehensive Large-Scale Experimental Analysis
| Autor: | Giuseppe Cattaneo, Francesco Palini, Chiara Romualdi, Umberto Ferraro Petrillo, Raffaele Giancarlo |
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| Přispěvatelé: | Cattaneo G., Petrillo U.F., Giancarlo R., Palini F., Romualdi C. |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Statistics and Probability
Sequence Similarity (geometry) Settore INF/01 - Informatica sequence analysis Computer science power statistics Alignment-Free Genomic Analysis Big Data Software Platforms Bioinformatics Algorithms Scale (descriptive set theory) Function (mathematics) computer.software_genre Biochemistry Computer Science Applications Set (abstract data type) Computational Mathematics Range (mathematics) Computational Theory and Mathematics alignment-free functions Data mining Completeness (statistics) Molecular Biology computer Type I and type II errors |
| Popis: | Motivation Alignment-free (AF) distance/similarity functions are a key tool for sequence analysis. Experimental studies on real datasets abound and, to some extent, there are also studies regarding their control of false positive rate (Type I error). However, assessment of their power, i.e. their ability to identify true similarity, has been limited to some members of the D2 family. The corresponding experimental studies have concentrated on short sequences, a scenario no longer adequate for current applications, where sequence lengths may vary considerably. Such a State of the Art is methodologically problematic, since information regarding a key feature such as power is either missing or limited. Results By concentrating on a representative set of word-frequency-based AF functions, we perform the first coherent and uniform evaluation of the power, involving also Type I error for completeness. Two alternative models of important genomic features (CIS Regulatory Modules and Horizontal Gene Transfer), a wide range of sequence lengths from a few thousand to millions, and different values of k have been used. As a result, we provide a characterization of those AF functions that is novel and informative. Indeed, we identify weak and strong points of each function considered, which may be used as a guide to choose one for analysis tasks. Remarkably, of the 15 functions that we have considered, only four stand out, with small differences between small and short sequence length scenarios. Finally, to encourage the use of our methodology for validation of future AF functions, the Big Data platform supporting it is public. Availability and implementation The software is available at: https://github.com/pipp8/power_statistics. Supplementary information Supplementary data are available at Bioinformatics online. |
| Databáze: | OpenAIRE |
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