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Assessment of Molecular Diversity in Biofuel Crops

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Biofuels and Biodiesel

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2290))

Abstract

Sustainable biofuel sources require the new sources of biofuel crops that can be developed into scalable plantation to meet the growing energy demands. Diverse supply sources of bioenergy plantations (edible, nonedible, and perennial grasses) will enable de-risking impact on geography and climate change that humans are likely to face in future. Use of phenotypic descriptors alone does not provide a deep insight into plantation population dynamics and molecular diversity of a biofuel crop. We provide protocols and methods to rapidly assess population parameters for emerging biofuel crops using genomics. This article has an application focus on next-generation sequencing to assess biofuel crop diversity. Use of these methods can accelerate germplasm assessment to accelerate population development and creation of sustainable biofuel plantations.

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References

  1. Das D, Varanasi JL (2019) Fundamentals of biofuel production processes. CRC Press, Boca Raton

    Book  Google Scholar 

  2. Pandey A (ed) (2009) Handbook of plant-based biofuels. CRC Press, Boca Raton

    Google Scholar 

  3. Pandey A, Larroche C, Gnansounou E et al (eds) (2019) Biofuels: alternative feedstocks and conversion processes for the production of liquid and gaseous biofuels. Academic Press, Cambridge

    Google Scholar 

  4. Solomon BD (2010) Biofuels and sustainability. Ann N Y Acad Sci 1185:119–134

    Article  Google Scholar 

  5. Schubert C (2006) Can biofuels finally take center stage? Nat Biotech 24(7):777–784

    Article  CAS  Google Scholar 

  6. Miladinović D, Vollmann J, Molinero-Ruiz L, Torres M (2019) Advances in oil crops research classical and new approaches to achieve sustainable productivity. Front Plant Sci 10:791. https://doi.org/10.3389/fpls.2019.00791

    Article  PubMed  PubMed Central  Google Scholar 

  7. Madan NS, Arockiasamy S, Narasimham JV, Patil M et al (2019) Anther culture for the production of haploid and doubled haploids in Jatropha curcas L. and its hybrids. Plant Cell Tissue Org Cult 138(1):181–192

    Article  CAS  Google Scholar 

  8. Rosado TB, Laviola BG, Faria DA, Pappas MR, Bhering LL, Quirino B, Grattapaglia B (2010) Molecular markers reveal limited genetic diversity in a large germplasm collection of the biofuel crop L. in Brazil. Crop Sci 50:2372–2382

    Article  CAS  Google Scholar 

  9. Fu Q, Tao YB, Xu ZF (2019) Genetic transformation and Transgenics of Jatropha curcas, a biofuel plant. In: Mulpuri S, Carels N, Bahadur B (eds) Jatropha, challenges for a new energy crop. Springer, Singapore, pp 79–93

    Chapter  Google Scholar 

  10. Di Tomaso JM, Barney JN, Fox AM (2007) Biofuel feedstocks: the risk of future invasions. All U.S. Government Documents (Utah Regional Depository). Paper 79

    Google Scholar 

  11. Taheripoura F, Hertela TW, Ramankutty N (2019) Market-mediated responses confound policies to limit deforestation from oil palm expansion in Malaysia and Indonesia. PNAS 116(38):19193–19199

    Article  Google Scholar 

  12. Rajora OP, Mosseler M (2001) Challenges and opportunities for conservation of forest genetic resources. Euphytica 118:197–212

    Article  Google Scholar 

  13. Abdelgadir HA, Johnson SD, Staden J (2009) Pollinator effectiveness, breeding system, and tests for inbreeding depression in the biofuel seed crop, Jatropha curcas. J Horticultural Sci Biotech 84:319–324

    Article  Google Scholar 

  14. Muranty H, Jorge V, Bastien C, Lepoittevin C et al (2014) Potential for marker-assisted selection for forest tree breeding: lessons from 20 years of MAS in crops. Tree Genet Genomes 10(6):1491–1510

    Article  Google Scholar 

  15. Laviola BG, Rodrigues EV, Ribeiro LP, Silva LA, de Azevedo Peixoto L, Bhering LL (2019) Strategies in the genetic breeding of Jatropha curcas for biofuel production in Brazil. In: Mulpuri S, Carels N, Bahadur B (eds) Jatropha, challenges for a new energy crop. Springer, Singapore, pp 45–62

    Chapter  Google Scholar 

  16. Ru S, Main D, Evans K, Peace C (2015) Current applications, challenges, and perspectives of marker-assisted seedling selection in Rosaceae tree fruit breeding. Tree Genet Genomes 11:8

    Article  Google Scholar 

  17. Xia W, Luo T, Dou Y, Zhang W et al (2019) Identification and validation of candidate genes involved in fatty acid content in oil palm by genome-wide association analysis. Front Plant Sci 10:1263

    Article  Google Scholar 

  18. Allwright MR, Taylor G (2015) Molecular breeding for improved second generation bioenergy crops. Trends Plant Sci 21(1):43–54

    Article  Google Scholar 

  19. Vandepitte K, Valdés-Rodríquez OA, Sánchez-Sánchez O, De Kort H, Martinez-Herrera J et al (2019) High SNP diversity in the non-toxic indigenous Jatropha curcas germplasm widens the potential of this upcoming major biofuel crop species. Ann Bot 124(4):645–652

    Article  CAS  Google Scholar 

  20. Xu J (ed) (2014) Next-generation sequencing: current technologies and applications. Caister Academic Press, Norfolk

    Google Scholar 

  21. Mitra R, Gill R, Datta S, Datta S (2014) Statistical analyses of next generation sequencing data: an overview. In: Datta S, Nettleton D (eds) Statistical analysis of next generation sequencing data. Frontiers in probability and the statistical sciences. Springer, Cham, pp 1–24

    Google Scholar 

  22. Quail MA, Smith M, Coupland P et al (2012) A tale of three next generation sequencing platforms: comparison of Ion Torrent, Pacific Biosciences and Illumina MiSeq sequencers. BMC Genomics 13:341

    Article  CAS  Google Scholar 

  23. Toit A (2019) Core leaf taxa of biofuel crops. Nat Rev Microbiol 17:647. https://doi.org/10.1038/s41579-019-0277-3

    Article  CAS  PubMed  Google Scholar 

  24. Mielczarek M, Szyda JJ (2016) Appl Gene 57:71. https://doi.org/10.1007/s13353-015-0292-7

    Article  CAS  Google Scholar 

  25. Mamanova L, Coffey AJ, Scott CE, Kozarewa I et al (2010) Target-enrichment strategies for next generation sequencing. Nat Methods 7(2):111–118

    Article  CAS  Google Scholar 

  26. Healey A, Furtado A, Cooper T et al (2014) Protocol: a simple method for extracting next-generation sequencing quality genomic DNA from recalcitrant plant species. Plant Methods 10:21. https://doi.org/10.1186/1746-4811-10-21

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Wan CY, Wilkins TA (1994) A modified hot borate method significantly enhances the yield of high-quality RNA from cotton (Gossypium hirsutum L.). Anal Biochem 223(1):7–12

    Article  CAS  Google Scholar 

  28. MacDonald RJ, Swift GH, Przybyla AE, Chirgwin JM (1987) Isolation of RNA using guanidinium salts. Meth Enzym 152:219–226

    Article  CAS  Google Scholar 

  29. Untergasser A, Cutcutache I, Koressaar T, Ye J, Faircloth BC, Remm M, Rozen SG (2012) Primer3-new capabilities and interfaces. Nucleic Acids Res 40(15):115

    Article  Google Scholar 

  30. Kumar A, Chordia N (2015) In silico PCR primer designing and validation. In: Basu C (ed) PCR primer design. Methods in molecular biology, vol 1275. Humana Press, New York, NY

    Google Scholar 

  31. Bragg L, Tyson GW (2014) Metagenomics using next-generation sequencing. Methods Mol Biol 1096:183–201. https://doi.org/10.1007/978-1-62703-712-9_15

    Article  CAS  PubMed  Google Scholar 

  32. De Filippis F, Laiola M, Blaiotta G, Ercolinia D (2017) Different amplicon targets for sequencing based studies of fungal diversity. Appl Environ Microbiol 83(17):00905-17

    Article  Google Scholar 

  33. Paulsen IT, Holmes AJ (eds) (2014) Environmental microbiology: methods and protocols, Methods in molecular biology, vol 1096, 2nd edn. Humana Press, New York. https://doi.org/10.1007/987-1-62703-712-9

    Book  Google Scholar 

  34. Patel RK, Jain M (2012) NGS QC toolkit: a toolkit for quality control of next generation sequencing data. PLoS One 7(2):e30619. https://doi.org/10.1371/journal.pone.0030619

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Geraldine A, Van der Auwera Mauricio O, Christopher C et al (2013) From FastQ data to high-confidence variant calls: the genome analysis toolkit best practices pipeline. Curr Protoc Bioinform 11(1110):11.10.1–11.10.33

    Google Scholar 

  36. Masella AP, Bartram AK, Truszkowski JM et al (2012) PANDAseq: paired-end assembler for illumina sequences. BMC Bioinformatics 13:31. https://doi.org/10.1186/1471-2105-13-31

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Kuczynski J, Stombaugh J, Walters WA et al (2011) Using QIIME to analyze 16S rRNA gene sequences from Microbial Communities. Curr Protoc Bioinform 36:10.7.1–10.7.20

    Article  Google Scholar 

  38. Wickham H (2016) ggplot2: elegant graphics for data analysis. Springer International Publishing, Cham

    Book  Google Scholar 

  39. Bray N, Pimentel H, Melsted P et al (2016) Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol 34:525–527. https://doi.org/10.1038/nbt.3519

    Article  CAS  PubMed  Google Scholar 

  40. Liu P, Wang CM, Li L et al (2011) Mapping QTLs for oil traits and eQTLs for oleosin genes in Jatropha. BMC Plant Biol 11:132. https://doi.org/10.1186/1471-2229-11-132

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Ye J, Hong Y, Qu J, Wang C (2013) Improvement of J. curcas oil by genetic transformation. In: Bahadur B, Sujatha M, Carels N (eds) Jatropha, challenges for a new energy crop. Springer, New York, pp 547–562

    Chapter  Google Scholar 

  42. Excoffier L, Lischer HEL (2010) Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Mol Ecol Resour 10:564–567

    Article  Google Scholar 

  43. Hubisz MA, Falush D, Stephens M, Pritchard JK (2009) Inferring weak population structure with the assistance of sample group information. Mol Ecol Resour 9:1322–1332

    Article  Google Scholar 

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Parameswaran, S., Eswaran, N., Johnson, T.S. (2021). Assessment of Molecular Diversity in Biofuel Crops. In: Basu, C. (eds) Biofuels and Biodiesel. Methods in Molecular Biology, vol 2290. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1323-8_11

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  • DOI: https://doi.org/10.1007/978-1-0716-1323-8_11

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1322-1

  • Online ISBN: 978-1-0716-1323-8

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