Evidence map›Paper›PMID 18976482›Full record

ArticleBMC genomics2008

A new method to compute K-mer frequencies and its application to annotate large repetitive plant genomes.

Stefan Kurtz, Apurva Narechania, Joshua C Stein, Doreen Ware

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Article in BMC genomics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 124 papers.

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124citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

124 citing papers in PubMed.

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  9. Genome research · 2025
    Review
  10. A survey of k-mer methods and applications in bioinformatics.Computational and structural biotechnology journal · 2024
    Review
  11. Review
  12. Review
  13. Article
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64 more citing papers are in PubMed but not listed here.

4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Stefan KurtzCenter for Bioinformatics, University of Hamburg, Bundesstrasse 43, 20146 Hamburg, Germany. kurtz@zbh.uni-hamburg.de
Apurva Narechania
Joshua C Stein
Doreen Ware

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe challenges of accurate gene prediction and enumeration are further aggravated in large genomes that contain highly repetitive transposable elements (TEs). Yet TEs play a substantial role in genome evolution and are themselves an important subject of study. Repeat annotation, based on counting occurrences of k-mers, has been previously used to distinguish TEs from low-copy genic regions; but currently available software solutions are impractical due to high memory requirements or specialization for specific user-tasks.

resultsHere we introduce the Tallymer software, a flexible and memory-efficient collection of programs for k-mer counting and indexing of large sequence sets. Unlike previous methods, Tallymer is based on enhanced suffix arrays. This gives a much larger flexibility concerning the choice of the k-mer size. Tallymer can process large data sizes of several billion bases. We used it in a variety of applications to study the genomes of maize and other plant species. In particular, Tallymer was used to index a set of whole genome shotgun sequences from maize (B73) (total size 109 bp.). We analyzed k-mer frequencies for a wide range of k. At this low genome coverage ( approximately 0.45x) highly repetitive 20-mers constituted 44% of the genome but represented only 1% of all possible k-mers. Similar low-complexity was seen in the repeat fractions of sorghum and rice. When applying our method to other maize data sets, High-C0t derived sequences showed the greatest enrichment for low-copy sequences. Among annotated TEs, the most highly repetitive were of the Ty3/gypsy class of retrotransposons, followed by the Ty1/copia class, and DNA transposons. Among expressed sequence tags (EST), a notable fraction contained high-copy k-mers, suggesting that transposons are still active in maize. Retrotransposons in Mo17 and McC cultivars were readily detected using the B73 20-mer frequency index, indicating their conservation despite extensive rearrangement across cultivars. Among one hundred annotated bacterial artificial chromosomes (BACs), k-mer frequency could be used to detect transposon-encoded genes with 92% sensitivity, compared to 96% using alignment-based repeat masking, while both methods showed 92% specificity.

conclusionThe Tallymer software was effective in a variety of applications to aid genome annotation in maize, despite limitations imposed by the relatively low coverage of sequence available. For more information on the software, see http://www.zbh.uni-hamburg.de/Tallymer.

Indexed as

DNA Transposable ElementsGenome, PlantComputational BiologyGenomicsMethodsOryzaSoftwareSorghumZea maysDNA Transposable Elements

Identifiers

PMID18976482
PMCPMC2613927

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.