Abstract
Current technologies allow the sequencing of microbial communities directly from the environment without prior culturing. One of the major problems when analyzing a microbial sample is to taxonomically annotate its reads to identify the species it contains. Most of the methods currently available focus on the classification of reads using a set of reference genomes and their k-mers. While in terms of precision these methods have reached percentages of correctness close to perfection, in terms of sensitivity (the actual number of classified reads) the performances are often poor. One of the reasons is the fact that the reads in a sample can be very different from the corresponding reference genomes, e.g. viral genomes are usually highly mutated.
To address this issue, in this paper we propose ClassGraph a new taxonomic classification method that makes use of the reads overlap graph and applies a label propagation algorithm to refine the result of existing tools. We evaluated the performance on simulated and real datasets against several taxonomic classification tools and the results showed an improved sensitivity and F-measure, while preserving high precision. ClassGraph is able to improve the classification accuracy especially on difficult cases like Virus and real datasets, where traditional tools are not able to classify many reads.
Availability: https://github.com/CominLab/ClassGraph
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Altschul, S.F., Gish, W., Miller, W., Myers, E.W., Lipman, D.J.: Basic local alignment search tool. J. Mol. Biol. 215(3), 403–410 (1990)
Andreace, F., Pizzi, C., Comin, M.: MetaProb 2: improving unsupervised metagenomic binning with efficient reads assembly using minimizers. In: Jha, S.K., Măndoiu, I., Rajasekaran, S., Skums, P., Zelikovsky, A. (eds.) ICCABS 2020. LNCS, vol. 12686, pp. 15–25. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-79290-9_2
Andreace, F., Pizzi, C., Comin, M.: Metaprob 2: metagenomic reads binning based on assembly using minimizers and k-mers statistics. J. Comput. Biol. https://doi.org/10.1089/cmb.2021.0270, pMID: 34448593
Bankevich, A., et al.: Spades: a new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19(5), 455–477 (2012). https://doi.org/10.1089/cmb.2012.0021, pMID: 22506599
Břinda, K., Sykulski, M., Kucherov, G.: Spaced seeds improve k-mer-based metagenomic classification. Bioinformatics 31(22), 3584 (2015). https://doi.org/10.1093/bioinformatics/btv419
Comin, M., Di Camillo, B., Pizzi, C., Vandin, F.: Comparison of microbiome samples: methods and computational challenges. Briefings Bioinf. (June 2020). https://doi.org/10.1093/bib/bbaa121, bbaa121
Girotto, S., Comin, M., Pizzi, C.: Higher recall in metagenomic sequence classification exploiting overlapping reads. BMC Genomics 18(10), 917 (2017)
Girotto, S., Pizzi, C., Comin, M.: Metaprob: accurate metagenomic reads binning based on probabilistic sequence signatures. Bioinformatics 32(17), i567–i575 (2016). https://doi.org/10.1093/bioinformatics/btw466
Holtgrewe, M.: Mason: a read simulator for second generation sequencing data (2010)
Huson, D.H., Auch, A.F., Qi, J., Schuster, S.C.: Megan analysis of metagenomic data. Genome Res. 17, 377–386 (2007)
Jang, H.B., et al.: Taxonomic assignment of uncultivated prokaryotic virus genomes is enabled by gene-sharing networks. Nat. Biotechnol. (June 2019). https://doi.org/10.1038/s41587-019-0100-8
Kim, D., Song, L., Breitwieser, F., Salzberg, S.: Centrifuge: rapid and sensitive classification of metagenomic sequences. Genome Res. 26, gr.210641.116 (2016). https://doi.org/10.1101/gr.210641.116
Lindgreen, S., Adair, K., Gardner, P.: An Evaluation of the Accuracy and Speed of Metagenome Analysis Tools. Cold Spring Harbor Laboratory Press, New York (2015)
Mallawaarachchi, V., Wickramarachchi, A., Lin, Y.: GraphBin: refined binning of metagenomic contigs using assembly graphs. Bioinformatics 36(11), 3307–3313 (2020)
Mallawaarachchi, V.G., Wickramarachchi, A.S., Lin, Y.: GraphBin2: refined and Overlapped binning of metagenomic contigs using assembly graphs. In: Kingsford, C., Pisanti, N. (eds.) 20th International Workshop on Algorithms in Bioinformatics (WABI 2020). Leibniz International Proceedings in Informatics (LIPIcs), vol. 172, pp. 8:1–8:21. Schloss Dagstuhl-Leibniz-Zentrum für Informatik, Dagstuhl, Germany (2020). https://doi.org/10.4230/LIPIcs.WABI.2020.8, https://drops.dagstuhl.de/opus/volltexte/2020/12797
Mande, S.S., Mohammed, M.H., Ghosh, T.S.: Classification of metagenomic sequences: methods and challenges. Briefings Bioinf. 13(6), 669–681 (2012). https://doi.org/10.1093/bib/bbs054
Marchiori, D., Comin, M.: Skraken: fast and sensitive classification of short metagenomic reads based on filtering uninformative k-mers. In: BIOINFORMATICS 2017–8th International Conference on Bioinformatics Models, Methods and Algorithms, Proceedings; Part of 10th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2017, vol. 3, pp. 59–67 (2017)
Ounit, R., Wanamaker, S., Close, T.J., Lonardi, S.: Clark: fast and accurate classification of metagenomic and genomic sequences using discriminative k-mers. BMC Genomics 16(1), 1–13 (2015)
Qian, J., Comin, M.: Metacon: unsupervised clustering of metagenomic contigs with probabilistic k-mers statistics and coverage. BMC Bioinf. 20(367), (2019). https://doi.org/10.1186/s12859-019-2904-4
Qian, J., Marchiori, D., Comin, M.: Fast and sensitive classification of short metagenomic reads with SKraken. In: Peixoto, N., Silveira, M., Ali, H.H., Maciel, C., van den Broek, E.L. (eds.) BIOSTEC 2017. CCIS, vol. 881, pp. 212–226. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-94806-5_12
Sczyrba, A., Hofmann, P., McHardy, A.C.: Critical assessment of metagenome interpretation-a benchmark of metagenomics software. Nat. Methods 14, 1063–1071 (2017)
Simpson, J., Durbin, R.: Efficient de novo assembly of large genomes using compressed data structures. Genome Res. 22(3), 549–56 (2012)
Wood, D., Salzberg, S.: Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biol. 15, 1–12 (2014)
Wood, D.E., Lu, J., Langmead, B.: Improved metagenomic analysis with kraken 2. Genome Biol. 20(1), 257 (2019)
Zhang, Z., Schwartz, S., Wagner, L., Miller, W.: A greedy algorithm for aligning DNA sequences. J. Comput. Biol. 7(1–2), 203–214 (2004)
Zhang, Z., Zhang, L.: Metamvgl: a multi-view graph-based metagenomic contig binning algorithm by integrating assembly and paired-end graphs. BMC Bioinf. 22 (July 2021). https://doi.org/10.1186/s12859-021-04284-4
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Cavattoni, M., Comin, M. (2021). Boosting Metagenomic Classification with Reads Overlap Graphs. In: Wei, Y., Li, M., Skums, P., Cai, Z. (eds) Bioinformatics Research and Applications. ISBRA 2021. Lecture Notes in Computer Science(), vol 13064. Springer, Cham. https://doi.org/10.1007/978-3-030-91415-8_44
Download citation
DOI: https://doi.org/10.1007/978-3-030-91415-8_44
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-91414-1
Online ISBN: 978-3-030-91415-8
eBook Packages: Computer ScienceComputer Science (R0)