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Bioinformatics. 2010 Sep 1;26(17):2101-8. doi: 10.1093/bioinformatics/btq343. Epub 2010 Jul 8.

Global sequence characterization of rice centromeric satellite based on oligomer frequency analysis in large-scale sequencing data.

Author information

1
Institute of Plant Molecular Biology, Biology Centre ASCR, Branisovska 31, CZ-37005, Ceske Budejovice, Czech Republic. macas@umbr.cas.cz

Abstract

MOTIVATION:

Satellite DNA makes up significant portion of many eukaryotic genomes, yet it is relatively poorly characterized even in extensively sequenced species. This is, in part, due to methodological limitations of traditional methods of satellite repeat analysis, which are based on multiple alignments of monomer sequences. Therefore, we employed an alternative, alignment-free, approach utilizing k-mer frequency statistics, which is in principle more suitable for analyzing large sets of satellite repeat data, including sequence reads from next generation sequencing technologies.

RESULTS:

k-mer frequency spectra were determined for two sets of rice centromeric satellite CentO sequences, including 454 reads from ChIP-sequencing of CENH3-bound DNA (7.6 Mb) and the whole genome Sanger sequencing reads (5.8 Mb). k-mer frequencies were used to identify the most conserved sequence regions and to reconstruct consensus sequences of complete monomers. Reconstructed consensus sequences as well as the assessment of overall divergence of k-mer spectra revealed high similarity of the two datasets, suggesting that CentO sequences associated with functional centromeres (CENH3-bound) do not significantly differ from the total population of CentO, which includes both centromeric and pericentromeric repeat arrays. On the other hand, considerable differences were revealed when these methods were used for comparison of CentO populations between individual chromosomes of the rice genome assembly, demonstrating preferential sequence homogenization of the clusters within the same chromosome. k-mer frequencies were also successfully used to identify and characterize smRNAs derived from CentO repeats.

PMID:
20616383
DOI:
10.1093/bioinformatics/btq343
[Indexed for MEDLINE]

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