Abstract
The classification of reads from a metagenomic sample using a reference taxonomy is usually based on first mapping the reads to the reference sequences and then, classifying each read at a node under the lowest common ancestor of the candidate sequences in the reference taxonomy with the least classification error. However, this taxonomic annotation can be biased by an imbalanced taxonomy and also by the presence of multiple nodes in the taxonomy with the least classification error for a given read. In this paper, we show that the Rand index is a better indicator of classification error than the often used area under the ROC curve and F-measure for both balanced and imbalanced reference taxonomies, and we also address the second source of bias by reducing the taxonomic annotation problem for a whole metagenomic sample to a set cover problem, for which a logarithmic approximation can be obtained in linear time.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Alonso, D., Barré, A., Beretta, S., Bonizzoni, P., Nikolski, M., Valiente, G.: Further steps in TANGO: improved taxonomic assignment in metagenomics. Bioinformatics 30(1), 17–23 (2013)
Bar-Yehuda, R., Even, S.: A linear-time approximation algorithm for the weighted vertex cover problem. J. Algorithms 2(2), 198–203 (1981)
Clemente, J.C., Jansson, J., Valiente, G.: Flexible taxonomic assignment of ambiguous sequencing reads. BMC Bioinform. 12(1), 8 (2011)
Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms, 3rd edn. MIT Press, Cambridge (2009)
Federhen, S.: The NCBI taxonomy database. Nucleic Acids Res. 40(D1), D136–D143 (2012)
Federhen, S.: Type material in the NCBI taxonomy database. Nucleic Acids Res. 43(D1), D1086–D1098 (2015)
Fischer, J., Huson, D.H.: New common ancestor problems in trees and directed acyclic graphs. Inform. Process. Lett. 110(8–9), 331–335 (2010)
Fosso, B., Santamaria, M., D’Antonio, M., Lovero, D., Corrado, G., Vizza, E., Passero, N., Garbuglia, A.R., Capobianchi, M.R., Crescenzi, M., Valiente, G., Pesole, G.: MetaShot: An accurate workflow for taxon classification of host-associated microbiome from shotgun metagenomic data. Bioinformatics (2017, in press)
Fosso, B., Santamaria, M., Marzano, M., Alonso, D., Valiente, G., Donvito, G., Monaco, A., Notarangelo, P., Pesole, G.: BioMaS: a modular pipeline for bioinformatic analysis of metagenomic amplicons. BMC Bioinform. 16(1), 203 (2015)
Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to NP-Completeness. Freeman, Dallas (1979)
Huerta-Cepas, J., Serra, F., Bork, P.: ETE 3: reconstruction, analysis and visualization of phylogenomic data. Mol. Biol. Evol. 33(6), 1635–1638 (2016)
Huson, D.H., Auch, A., Qi, J., Schuster, S.C.: MEGAN analysis of metagenomic data. Genome Res. 17(3), 377–386 (2007)
Jaccard, P.: Étude comparative de la distribution florale dans une portion des Alpes et du Jura. Bull. Soc. Vaud. Sc. Nat. 37(142), 547–579 (1901)
Johnson, D.S.: Approximation algorithms for combinatorial problems. J. Comput. Syst. Sci. 9(3), 256–278 (1974)
Kunin, V., Copeland, A., Lapidus, A., Mavromatis, K., Hugenholtz, P.: A bioinformatician’s guide to metagenomics. Microbiol. Mol. Biol. Rev. 72(4), 557–578 (2008)
López, V., Fernández, A., García, S., Palade, V., Herrera, F.: An insight into classification with imbalanced data: empirical results and current trends on using data intrinsic characteristics. Inform. Sci. 250(1), 113–141 (2013)
Matthews, B.W.: Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim. Biophys. Acta 405(2), 442–451 (1975)
Powers, D.M.W.: Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. J. Mach. Learn. Tech. 2(1), 37–63 (2011)
Rand, W.M.: Objective criteria for the evaluation of clustering methods. J. Am. Stat. Assoc. 66(336), 846–850 (1971)
Thomas, T., Gilbert, J., Meyer, F.: Metagenomics: a guide from sampling to data analysis. Microb. Inform. Exp. 2(1), 3 (2012)
Wooley, J.C., Godzik, A., Friedberg, I.: A primer on metagenomics. PLoS Comput. Biol. 6(2), e1000667 (2010)
Youden, W.J.: Index for rating diagnostic tests. Cancer 3(1), 32–35 (1950)
Yule, G.U.: On the methods of measuring association between two attributes. J. R. Statist. Soc. 75(6), 579–642 (1912)
Acknowledgements
Partially supported by Spanish Ministry of Economy and Competitiveness and European Regional Development Fund project DPI2015-67082-P (MINECO/FEDER).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Fosso, B., Pesole, G., Rosselló, F., Valiente, G. (2017). Unbiased Taxonomic Annotation of Metagenomic Samples. In: Cai, Z., Daescu, O., Li, M. (eds) Bioinformatics Research and Applications. ISBRA 2017. Lecture Notes in Computer Science(), vol 10330. Springer, Cham. https://doi.org/10.1007/978-3-319-59575-7_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-59575-7_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59574-0
Online ISBN: 978-3-319-59575-7
eBook Packages: Computer ScienceComputer Science (R0)