Abstract
Many practical algorithms for sequence alignment, genome assembly and other tasks represent a sequence as a set of k-mers. Here, we address the problems of estimating genome size and sequencing coverage from sequencing reads, without the need for sequence assembly. Our estimates are based on a histogram of k-mer abundance in the input set of sequencing reads and on probabilistic modeling of distribution of k-mer abundance based on parameters related to the coverage, error rate and repeat structure of the genome. Our method provides reliable estimates even at coverage as low as 0.5 or at error rates as high as 10%.
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
Preview
Unable to display preview. Download preview PDF.
References
Bloom, B.H.: Space/time trade-offs in hash coding with allowable errors. Communications of the ACM 13(7), 422–426 (1970)
Ferragina, P., Manzini, G.: Opportunistic data structures with applications. In: Foundations of Computer Science (FOCS), pp. 390–398 (2000)
Illumina (2015). E.coli MG1655 Illumina sequencing dataset. ftp://webdata:webdata@ussd-ftp.illumina.com/Data/SequencingRuns/MG1655/MiSeq_Ecoli_MG1655_110721_PF.bam (accessed: March 03, 2015)
Kelley, D.R., Schatz, M.C., Salzberg, S.L., et al.: Quake: Quality-aware detection and correction of sequencing errors. Genome Biology 11(11), R116 (2010)
Kurtz, S., Narechania, A., Stein, J.C., Ware, D.: A new method to compute \(k\)-mer frequencies and its application to annotate large repetitive plant genomes. BMC Genomics 9(1), 517 (2008)
Li, R., Fan, W., Tian, G., et al.: The sequence and de novo assembly of the giant panda genome. Nature 463(7279), 311–317 (2010)
Li, X., Waterman, M.S.: Estimating the repeat structure and length of DNA sequences using \(\ell \)-tuples. Genome Research 13(8), 1916–1922 (2003)
Marçais, G., Kingsford, C.: A fast, lock-free approach for efficient parallel counting of occurrences of \(k\)-mers. Bioinformatics 27(6), 764–770 (2011)
Melsted, P., Pritchard, J.K.: Efficient counting of \(k\)-mers in DNA sequences using a Bloom filter. BMC Bioinformatics 12(1), 333 (2011)
Pevzner, P.A., Tang, H., Waterman, M.S.: An Eulerian path approach to DNA fragment assembly. Proceedings of the National Academy of Sciences 98(17), 9748–9753 (2001)
Sveinsson, S., Gill, N., Kane, N.C., Cronk, Q.: Transposon fingerprinting using low coverage whole genome shotgun sequencing in Cacao (Theobroma cacao L.) and related species. BMC Genomics 14(1), 502 (2013)
Wang, Y., Leung, H.C., Yiu, S.-M., Chin, F.Y.: MetaCluster 5.0: A two-round binning approach for metagenomic data for low-abundance species in a noisy sample. Bioinformatics 28(18), i356–i362 (2012)
Williams, D., Trimble, W.L., Shilts, M., Meyer, F., Ochman, H.: Rapid quantification of sequence repeats to resolve the size, structure and contents of bacterial genomes. BMC Genomics 14(1), 537 (2013)
Wu, Y.-W., Ye, Y.: A novel abundance-based algorithm for binning metagenomic sequences using l-tuples. Journal of Computational Biology 18(3), 523–534 (2011)
Zhang, Q., Pell, J., Canino-Koning, R., Howe, A.C., Brown, C.T.: These are not the \(k\)-mers you are looking for: Efficient online \(k\)-mer counting using a probabilistic data structure. PloS One 9(7), e101271 (2014)
Zhu, C., Byrd, R.H., Lu, P., Nocedal, J.: Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization. ACM Transactions on Mathematical Software 23(4), 550–560 (1997)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Hozza, M., Vinař, T., Brejová, B. (2015). How Big is that Genome? Estimating Genome Size and Coverage from k-mer Abundance Spectra. In: Iliopoulos, C., Puglisi, S., Yilmaz, E. (eds) String Processing and Information Retrieval. SPIRE 2015. Lecture Notes in Computer Science(), vol 9309. Springer, Cham. https://doi.org/10.1007/978-3-319-23826-5_20
Download citation
DOI: https://doi.org/10.1007/978-3-319-23826-5_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23825-8
Online ISBN: 978-3-319-23826-5
eBook Packages: Computer ScienceComputer Science (R0)