IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0245058.html
   My bibliography  Save this article

Efficient association mapping from k-mers—An application in finding sex-specific sequences

Author

Listed:
  • Zakaria Mehrab
  • Jaiaid Mobin
  • Ibrahim Asadullah Tahmid
  • Atif Rahman

Abstract

Genome wide association studies (GWAS) attempt to map genotypes to phenotypes in organisms. This is typically performed by genotyping individuals using microarray or by aligning whole genome sequencing reads to a reference genome. Both approaches require knowledge of a reference genome which hinders their application to organisms with no or incomplete reference genomes. This caveat can be removed by using alignment-free association mapping methods based on k-mers from sequencing reads. Here we present an improved implementation of an alignment free association mapping method. The new implementation is faster and includes additional features to make it more flexible than the original implementation. We have tested our implementation on an E. Coli ampicillin resistance dataset and observe improvement in execution time over the original implementation while maintaining accuracy in results. We also demonstrate that the method can be applied to find sex specific sequences.

Suggested Citation

  • Zakaria Mehrab & Jaiaid Mobin & Ibrahim Asadullah Tahmid & Atif Rahman, 2021. "Efficient association mapping from k-mers—An application in finding sex-specific sequences," PLOS ONE, Public Library of Science, vol. 16(1), pages 1-12, January.
    • Handle: RePEc:plo:pone00:0245058
    DOI: 10.1371/journal.pone.0245058
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0245058
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0245058&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0245058?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. John A. Lees & Minna Vehkala & Niko Välimäki & Simon R. Harris & Claire Chewapreecha & Nicholas J. Croucher & Pekka Marttinen & Mark R. Davies & Andrew C. Steer & Steven Y. C. Tong & Antti Honkela & J, 2016. "Sequence element enrichment analysis to determine the genetic basis of bacterial phenotypes," Nature Communications, Nature, vol. 7(1), pages 1-8, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Erki Aun & Age Brauer & Veljo Kisand & Tanel Tenson & Maido Remm, 2018. "A k-mer-based method for the identification of phenotype-associated genomic biomarkers and predicting phenotypes of sequenced bacteria," PLOS Computational Biology, Public Library of Science, vol. 14(10), pages 1-17, October.
    2. Michelle Baker & Xibin Zhang & Alexandre Maciel-Guerra & Kubra Babaarslan & Yinping Dong & Wei Wang & Yujie Hu & David Renney & Longhai Liu & Hui Li & Maqsud Hossain & Stephan Heeb & Zhiqin Tong & Nic, 2024. "Convergence of resistance and evolutionary responses in Escherichia coli and Salmonella enterica co-inhabiting chicken farms in China," Nature Communications, Nature, vol. 15(1), pages 1-21, December.
    3. Danesh Moradigaravand & Martin Palm & Anne Farewell & Ville Mustonen & Jonas Warringer & Leopold Parts, 2018. "Prediction of antibiotic resistance in Escherichia coli from large-scale pan-genome data," PLOS Computational Biology, Public Library of Science, vol. 14(12), pages 1-17, December.
    4. Allison L Hicks & Nicole Wheeler & Leonor Sánchez-Busó & Jennifer L Rakeman & Simon R Harris & Yonatan H Grad, 2019. "Evaluation of parameters affecting performance and reliability of machine learning-based antibiotic susceptibility testing from whole genome sequencing data," PLOS Computational Biology, Public Library of Science, vol. 15(9), pages 1-21, September.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0245058. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.