Approaches to in silico analysis of micobiome biodiversity metrics of radionuclide contaminated soils

Authors

  • O. Yu. Parenyuk National University of Life and Environmental Sciences of Ukraine image/svg+xml
  • I. O. Simutin Taras Shevchenko National University of Kyiv image/svg+xml
  • D. O. Samofalova Institute of Food Biotechnology and Genomics NAS of Ukraine , ДУ «Інститут харчової біотехнології та геноміки НАН України»
  • Yu. V. Ruban National University of Life and Environmental Sciences of Ukraine image/svg+xml
  • V. V. Illienko National University of Life and Environmental Sciences of Ukraine image/svg+xml
  • N. H. Nesterova National University of Life and Environmental Sciences of Ukraine image/svg+xml
  • I. M. Gudkov National University of Life and Environmental Sciences of Ukraine image/svg+xml

DOI:

https://doi.org/10.31548/bio2017.05.002

Abstract

The bioinformatic approaches to the processing of the results of total soil microorganisms DNA sequencing have been analyzed. The methodology that provides the highest quality and reliability of the source data was designed.

Keywords: microbiology of soil, radioactive contamination, bioinformatic methods

References

Asnicar, F., Weingart, G., Tickle, T. L., Huttenhower, C., & Segata, N. (2015). Compact graphical representation of phylogenetic data and metadata with GraPhlAn. PeerJ, 3, e1029. http://doi.org/10.7717/peerj.1029

Blaxter, M., Mann, J., Chapman, T., Thomas, F., Whitton, C., Floyd, R., & Abebe, E. (2005). Defining operational taxonomic units using DNA barcode data. Philosophical Transactions of the Royal Society B: Biological Sciences, 360(1462), 1935–1943. http://doi.org/10.1098/rstb.2005.1725

Ewing, B., & Green, P. (1998). Base-calling of automated sequencer traces using phred. II. Error probabilities. Genome Research, 8(3), 186–94. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/9521922

Gołębiewski, M., Deja-Sikora, E., Cichosz, M., Tretyn, A., & Wróbel, B. (2014). 16S rDNA pyrosequencing analysis of bacterial community in heavy metals polluted soils. Microbial Ecology, 67(3), 635–47. http://doi.org/10.1007/s00248-013-0344-7

Kuczynski, J., Stombaugh, J., Walters, W. A., González, A., Caporaso, J. G., & Knight, R. (2011). Using QIIME to analyze 16S rRNA gene sequences from microbial communities. Current Protocols in Bioinformatics / Editoral Board, Andreas D. Baxevanis ... [et Al.], Chapter 10, Unit 1E.5. http://doi.org/10.1002/9780471729259.mc01e05s27

Langille, M. G. I., Zaneveld, J., Caporaso, J. G., McDonald, D., Knights, D., Reyes, J. A., … Huttenhower, C. (2013). Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nature Biotechnology, 31(9), 814–821. http://doi.org/10.1038/nbt.2676

Luo, C., Rodriguez-R, L. M., Johnston, E. R., Wu, L., Cheng, L., Xue, K., … Konstantinidis, K. T. (2014). Soil microbial community responses to a decade of warming as revealed by comparative metagenomics.1. Luo C. Soil microbial community responses to a decade of warming as revealed by comparative metagenomics. / C. Luo, L. M. Rodriguez-R, E. R. Johnston[et a. Applied and Environmental Microbiology, 80(5), 1777–86. http://doi.org/10.1128/AEM.03712-13

McMurdie, P. J., Holmes, S., Kindt, R., Legendre, P., & O’Hara, R. (2013). phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLoS ONE, 8(4), e61217. http://doi.org/10.1371/journal.pone.0061217

Pareniuk, O., Shavanova, K., Laceby, J. P., Illienko, V., Tytova, L., Levchuk, S., … Nanba, K. (2015). Modification of 137Cs transfer to rape (Brassica napus L.) phytomass under the influence of soil microorganisms. Journal of Environmental Radioactivity, 149, 73–80. http://doi.org/10.1016/j.jenvrad.2015.07.003

Puente-Sánchez, F., Aguirre, J., & Parro, V. (2016). A novel conceptual approach to read-filtering in high-throughput amplicon sequencing studies. Nucleic Acids Research, 44(4), e40. http://doi.org/10.1093/nar/gkv1113

Schmieder, R., & Edwards, R. (2011). Quality control and preprocessing of metagenomic datasets. Bioinformatics, 27(6), 863–864. http://doi.org/10.1093/bioinformatics/btr026

Schneider, T., Keiblinger, K. M., Schmid, E., Sterflinger-Gleixner, K., Ellersdorfer, G., Roschitzki, B., … Riedel, K. (2012). Who is who in litter decomposition? Metaproteomics reveals major microbial players and their biogeochemical functions. The ISME Journal, 6(9), 1749–62. http://doi.org/10.1038/ismej.2012.11

Thomas, T., Gilbert, J., & Meyer, F. (2012). Metagenomics - a guide from sampling to data analysis. Microbial Informatics and Experimentation, 2(1), 3. http://doi.org/10.1186/2042-5783-2-3

Downloads

Published

2018-02-22

Issue

Vol. 9 No. 5-6 (2017)

Section

Biology

License

Relationship between right holders and users shall be governed by the terms of the license Creative Commons  Attribution – non-commercial – Distribution On Same Conditions 4.0 international (CC BY-NC-SA 4.0):https://creativecommons.org/licenses/by-nc-sa/4.0/deed.uk

Authors who publish with this journal agree to the following terms:

  • Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
  • Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  • Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).