Advertisement

Bioinformatic Tools to Study the Soil Microorganisms: An In Silico Approach for Sustainable Agriculture

  • Pankaj Bhatt
  • Anupam Barh
Chapter

Abstract

The twenty-first century is the era of omics technologies which is mainly focused on generation and analysis of molecular data present within the organisms. In the last two decades, enormous data were generated by researchers in laboratories, due to the rapid developments of high-throughput next-generation sequencing (NGS) technologies. These data generated by these technologies can directly be applied to the agricultural developments. The agriculture system which is directly connected to soil can act as plant growth promoters in free-living state or either associated with the rhizospheric region. Whole-genome sequences of the microorganisms are available in the database which is useful for genome-wide identification of specific genes, proteins, ESTs, ORFs, etc. Identification through DNA barcoding in soil microorganism is also a new avenue where various bioinformatic tool assisted the process like MUSCLE, BRONX, ecoPrimers, etc. Microbial system biology is another way to explore the data from different metabolic pathways, genes, and proteins for the valid conclusion of the microbial activity. In totality, the in silico tools comprised of databases and softwares that can assist to reduce the “sequence-function gap” and help in the broad-spectrum study of soil microorganisms and their application toward sustainable agriculture.

Notes

Acknowledgment

Authors P.B and A.B are thankful for all the researchers for their contribution. Their contribution cited in reference section.

References

  1. Brendel V, Xing L, Zhu W (2004) Gene structure prediction from consensus spliced alignment of multiple ESTs matching the same genomic locus. Bioinformatics 20:1157–1169.  https://doi.org/10.1093/bioinformatics/bth058 CrossRefPubMedGoogle Scholar
  2. Brown SDJ, Collins RA, Boyer S et al (2012) Spider: an R package for the analysis of species identity and evolution, with particular reference to DNA barcoding. Mol Ecol Resour 12:562–565.  https://doi.org/10.1111/j.1755-0998.2011.03108.x CrossRefPubMedGoogle Scholar
  3. Coleman AW, Vacquier VD (2002) Exploring the phylogenetic utility of ITS sequences for animals: a test case for abalone (Haliotis). J Mol Evol 54:246–257.  https://doi.org/10.1007/s00239-001-0006-0 CrossRefPubMedGoogle Scholar
  4. Edgar RC (2004) MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res 32:1792–1797.  https://doi.org/10.1093/nar/gkh340 CrossRefPubMedPubMedCentralGoogle Scholar
  5. Faiza M (2017) Ab-initio prediction of protein structure: an introduction. Bioinforma Rev 2:4Google Scholar
  6. Hiraoka S, Yang C-C, Iwasaki W (2016) Metagenomics and bioinformatics in microbial ecology: current status and beyond. Microbes Environ 31:204–212.  https://doi.org/10.1264/jsme2.ME16024 CrossRefPubMedPubMedCentralGoogle Scholar
  7. Jain A, Bacolla A, Chakraborty P et al (2010) Human DHX9 helicase unwinds triple-helical DNA structures. Biochemistry 49:6992–6999.  https://doi.org/10.1021/bi100795m CrossRefPubMedPubMedCentralGoogle Scholar
  8. Kress WJ, Erickson DL (2012) DNA barcodes: methods and protocols. In: Methods in molecular biology. Clifton, pp 3–8CrossRefGoogle Scholar
  9. Krieger E, Nabuurs SB, Vriend G (2003) Homology modeling. In: Bourne P, Weissig H (eds) Structural bioinformatics. Wiley-Liss, Hoboken, pp 507–521Google Scholar
  10. Kumar S, Carlsen T, Mevik BH et al (2011) CLOTU: an online pipeline for processing and clustering of 454 amplicon reads into OTUs followed by taxonomic annotation. BMC Bioinforma 12:182.  https://doi.org/10.1186/1471-2105-12-182 CrossRefGoogle Scholar
  11. Kustrin AS, Beresford R (2000) Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. J Pharm Biomed Anal 22:717–727CrossRefGoogle Scholar
  12. Little DP (2011) DNA barcode sequence identification incorporating taxonomic hierarchy and within taxon variability. PLoS One 6:e20552.  https://doi.org/10.1371/journal.pone.0020552 CrossRefPubMedPubMedCentralGoogle Scholar
  13. Mishra J, Prakash J, Arora NK (2014) Role of beneficial soil microbes in sustainable agriculture and environmental management. Clim Chang Environ Sustain 4:137–149CrossRefGoogle Scholar
  14. Negi G, Pankaj, Srivastava A, Sharma A (2014) In situ biodegradation of endosulfan, imidacloprid, and carbendazim using indigenous bacterial cultures of agriculture fields of Uttarakhand, India. Int J Bioeng Life Sci 8:973–981Google Scholar
  15. Negi G, Srivastava A, Sharma A (2016) Optimization of endosulfan biodegradation using indigenous bacterial isolate bacillus Aryabhatti through response surface methodology. J Ind Pollut Control 32:638–644Google Scholar
  16. Nielsen UN, Wall DH (2013) The future of soil invertebrate communities in polar regions: different climate change responses in the Arctic and Antarctic? Ecol Lett 16:409–419.  https://doi.org/10.1111/ele.12058 CrossRefPubMedGoogle Scholar
  17. O’Donnell AG, Young IM, Rushton SP et al (2007) Visualization, modelling and prediction in soil microbiology. Nat Rev Microbiol 5:689–699.  https://doi.org/10.1038/nrmicro1714 CrossRefPubMedGoogle Scholar
  18. Ogram A (2000) Soil molecular microbial ecology at age 20: methodological challenges for the future. Soil Biol Biochem 32:1499–1504.  https://doi.org/10.1016/S0038-0717(00)00088-2 CrossRefGoogle Scholar
  19. Orgiazzi A, Dunbar MB, Panagos P et al (2015) Soil biodiversity and DNA barcodes: opportunities and challenges. Soil Biol Biochem 80:244–250.  https://doi.org/10.1016/J.SOILBIO.2014.10.014 CrossRefGoogle Scholar
  20. Pankaj, Nailwal TK, Singh L, Panwar A (2014) Isolation and characterization of rhizobial isolates from the rhizospheric soil of an endangered plant meizotropis pellita. Asian J Microbiol Biotechnol Environ Sci Pap 16:301–306Google Scholar
  21. Pankaj, Negi G, Gangola S et al (2016a) Differential expression and characterization of cypermethrin-degrading potential proteins in Bacillus thuringiensis strain, SG4. 3 Biotech 6:225.  https://doi.org/10.1007/s13205-016-0541-4 CrossRefPubMedPubMedCentralGoogle Scholar
  22. Pankaj, Sharma A, Gangola S et al (2016b) Novel pathway of cypermethrin biodegradation in a Bacillus sp. strain SG2 isolated from cypermethrin-contaminated agriculture field. 3 Biotech 6:45.  https://doi.org/10.1007/s13205-016-0372-3 CrossRefPubMedPubMedCentralGoogle Scholar
  23. Pathak RK, Baunthiyal M, Pandey N et al (2017) Modeling of the jasmonate signaling pathway in Arabidopsis thaliana with respect to pathophysiology of alternaria blight in brassica. Sci Rep 7:16790.  https://doi.org/10.1038/s41598-017-16884-3 CrossRefPubMedPubMedCentralGoogle Scholar
  24. Probandt D, Eickhorst T, Ellrott A et al (2017) Microbial life on a sand grain: from bulk sediment to single grains. ISME J.  https://doi.org/10.1038/ismej.2017.197
  25. Riaz T, Shehzad W, Viari A et al (2011) ecoPrimers: inference of new DNA barcode markers from whole genome sequence analysis. Nucleic Acids Res 39:e145.  https://doi.org/10.1093/nar/gkr732 CrossRefPubMedPubMedCentralGoogle Scholar
  26. Roy A, Kucukural A, Zhang Y (2010) I-TASSER: a unified platform for automated protein structure and function prediction. Nat Protoc 5:725–738.  https://doi.org/10.1038/nprot.2010.5 CrossRefPubMedPubMedCentralGoogle Scholar
  27. Santos EC, Armas ED, Crowley D, Lambais MR (2014) Artificial neural network modeling of microbial community structures in the Atlantic Forest of Brazil. Soil Biol Biochem 69:101–109.  https://doi.org/10.1016/J.SOILBIO.2013.10.049 CrossRefGoogle Scholar
  28. Seibel PN, Müller T, Dandekar T et al (2006) 4SALE – a tool for synchronous RNA sequence and secondary structure alignment and editing. BMC Bioinforma 7:498.  https://doi.org/10.1186/1471-2105-7-498 CrossRefGoogle Scholar
  29. Steinke D, Vences M, Salzburger W, Meyer A (2005) TaxI: a software tool for DNA barcoding using distance methods. Philos Trans R Soc Lond Ser B Biol Sci 360:1975–1980.  https://doi.org/10.1098/rstb.2005.1729 CrossRefGoogle Scholar
  30. Wang Z, Chen Y, Li Y (2004) A brief review of computational gene prediction methods. doi: https://doi.org/10.1016/S1672-0229(04)02028-5 CrossRefGoogle Scholar
  31. Whitman WB, Coleman DC, Wiebe WJ (1998) Prokaryotes: the unseen majority. Proc Natl Acad Sci 95:6578–6583CrossRefPubMedGoogle Scholar
  32. Wolf M, Friedrich J, Dandekar T, Müller T (2005) CBCAnalyzer: inferring phylogenies based on compensatory base changes in RNA secondary structures. In Silico Biol 5:291–294PubMedGoogle Scholar
  33. Zhang Y, Kolinski A, Skolnick J (2003) TOUCHSTONE II: a new approach to ab initio protein structure prediction. Biophys J 85:1145–1164.  https://doi.org/10.1016/S0006-3495(03)74551-2 CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Pankaj Bhatt
    • 1
    • 2
  • Anupam Barh
    • 1
    • 2
  1. 1.Department of MicrobiologyDolphin (P.G) Institute of Biomedical and Natural SciencesDehradunIndia
  2. 2.ICAR-Directorate of Mushroom ResearchSolanIndia

Personalised recommendations