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Systematic prediction of functionally linked genes in bacterial and archaeal genomes

Nature Protocols volume 14, pages 3013–3031 (2019)Cite this article

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Abstract

Functionally linked genes in bacterial and archaeal genomes are often organized into operons. However, the composition and architecture of operons are highly variable and frequently differ even among closely related genomes. Therefore, to efficiently extract reliable functional predictions for uncharacterized genes from comparative analyses of the rapidly growing genomic databases, dedicated computational approaches are required. We developed a protocol to systematically and automatically identify genes that are likely to be functionally associated with a ‘bait’ gene or locus by using relevance metrics. Given a set of bait loci and a genomic database defined by the user, this protocol compares the genomic neighborhoods of the baits to identify genes that are likely to be functionally linked to the baits by calculating the abundance of a given gene within and outside the bait neighborhoods and the distance to the bait. We exemplify the performance of the protocol with three test cases, namely, genes linked to CRISPR–Cas systems using the ‘CRISPRicity’ metric, genes associated with archaeal proviruses and genes linked to Argonaute genes in halobacteria. The protocol can be run by users with basic computational skills. The computational cost depends on the sizes of the genomic dataset and the list of reference loci and can vary from one CPU-hour to hundreds of hours on a supercomputer.

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Fig. 1: The pipeline for the identification of gene families associated with a set of baits.
Fig. 2: A detailed, step by step schematic of the protocol.
Fig. 3: The space of relevance metrics.
Fig. 4: Dissection of the space of relevance metrics.

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Data and code availability

The source code of the Icity pipeline is freely available under open-source NCBI license (https://github.com/ncbi/ICITY/blob/master/LICENSE.txt) at the NCBI GitHub page (https://github.com/ncbi/ICITY). Questions and comments can be addressed to authors through the GitHub portal or by email. All example datasets and the results of their analysis presented in the paper are available at the NCBI FTP site (ftp://ftp.ncbi.nih.gov/pub/wolf/_suppl/icityNatProt/).

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Acknowledgements

This research was funded through the Intramural Research Program of the National Institutes of Health of the USA, the RFBR (for research project 18-34-00012, S.A.S.), a systems biology fellowship funded by Philip Morris Sales and Marketing (to S.A.S.), the Ministry of Education and Science of the Russian Federation (subsidy agreement 14.606.21.0006; project identifier RFMEFI60617X0006; to S.A.S. and K.V.S.) and an NIH grant (R01 GM10407 to K.V.S.).

Author information

Authors and Affiliations

  1. National Center for Biotechnology Information, National Library of Medicine, Bethesda, MD, USA

    Sergey A. Shmakov, Guilhem Faure, Kira S. Makarova, Yuri I. Wolf & Eugene V. Koonin

  2. Skolkovo Institute of Science and Technology, Skolkovo, Russia

    Sergey A. Shmakov & Konstantin V. Severinov

  3. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Guilhem Faure

  4. Waksman Institute of Microbiology, Rutgers, The State University of New Jersey, Piscataway, NJ, USA

    Konstantin V. Severinov

  5. Institute of Molecular Genetics, Russian Academy of Sciences, Moscow, Russia

    Konstantin V. Severinov

Authors
  1. Sergey A. Shmakov
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  5. Konstantin V. Severinov
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  6. Eugene V. Koonin
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Contributions

S.A.S., Y.I.W. and E.V.K. designed the protocol; S.A.S. implemented the protocol with assistance from G.F.; S.A.S., G.F., K.S.M., Y.I.W. and K.V.S. analyzed the results; S.A.S., Y.I.W. and E.V.K. wrote the manuscript, which was read, edited and approved by all authors.

Corresponding author

Correspondence to Eugene V. Koonin.

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The authors declare no competing interests.

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Peer review information Nature Protocols thanks Christine Pourcel and other anonymous reviewer(s) for their contribution to the peer review of this work.

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Key references using this protocol

Shmakov, S. A., Makarova, K. S., Wolf, Y. I., Severinov, K. V. & Koonin, E. V. Proc. Natl Acad. Sci. USA 115, E5307–E5316 (2018): https://doi.org/10.1073/pnas.1803440115

Shmakov, S. et al. Nat. Rev. Microbiol. 15, 169–182 (2017): https://doi.org/10.1038/nrmicro.2016.184

Shmakov, S. et al. Mol. Cell 60, 385–397 (2015): https://doi.org/10.1016/j.molcel.2015.10.008

Supplementary information

Supplementary Data

Step-by-step explanation of the RunClust.sh script used for protein clustering, and an alternative iterative clustering procedure.

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Shmakov, S.A., Faure, G., Makarova, K.S. et al. Systematic prediction of functionally linked genes in bacterial and archaeal genomes. Nat Protoc 14, 3013–3031 (2019). https://doi.org/10.1038/s41596-019-0211-1

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