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Insect science
Jun, 2021;28 (3): 590-601.

FAWMine: An integrated database and analysis platform for fall armyworm genomics.

Pengcheng Yang, Depin Wang, Wei Guo, Le Kang
Author Information
  1. Pengcheng Yang: Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, 100101, China.
  2. Depin Wang: Sino-Danish College, University of Chinese Academy of Sciences, Beijing, 101408, China.
  3. Wei Guo: State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
  4. Le Kang: Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, 100101, China. ORCID
PMID: 33511767 DOI: 10.1111/1744-7917.12903

Abstract

Fall Armyworm (Spodoptera frugiperda), a native insect species in the Americas, is rapidly becoming a major agricultural pest worldwide and is causing great damage to corn, rice, soybeans, and other crops. To control this pest, scientists have accumulated a great deal of high-throughput data of Fall Armyworm, and nine versions of its genomes and transcriptomes have been published. However, easily accessing and performing integrated analysis of these omics data sets is challenging. Here, we developed the Fall Armyworm Genome Database (FAWMine, http://159.226.67.243:8080/fawmine/) to maintain genome sequences, structural and functional annotations, transcriptomes, co-expression, protein interactions, homologs, pathways, and single-nucleotide variations. FAWMine provides a powerful framework that helps users to perform flexible and customized searching, present integrated data sets using diverse visualization methods, output results tables in a range of file formats, analyze candidate gene lists using multiple widgets, and query data available in other InterMine systems. Additionally, stand-alone JBrowse and BLAST services are also established, allowing the users to visualize RNA-Seq data and search genome and annotated gene sequences. Altogether, FAWMine is a useful tool for querying, visualizing, and analyzing compiled data sets rapidly and efficiently. FAWMine will be continually updated to function as a community resource for Fall Armyworm genomics and pest control research.

Keywords

InterMine data warehouse invasive species population resequencing transcriptome

References

  1. Anders, S., Pyl, P.T. and Huber, W. (2015) HTSeq-a Python framework to work with high-throughput sequencing data. Bioinformatics, 31, 166-169.
  2. Balakrishnan, R., Park, J., Karra, K., Hitz, B.C., Binkley, G., Hong, E.L. et al. (2012) YeastMine-an integrated data warehouse for Saccharomyces cerevisiae data as a multipurpose tool-kit. Database: the Journal of Biological Databases and Curation, 2012, bar062.
  3. Bolger, A.M., Lohse, M. and Usadel, B. (2014) Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics, 30, 2114-2120.
  4. Buels, R., Yao, E., Diesh, C.M., Hayes, R.D., Munoz-Torres, M., Helt, G. et al. (2016) JBrowse: a dynamic web platform for genome visualization and analysis. Genome Biology, 17, 66.
  5. Chang, Z.X., Ajayi, O.E., Guo, D.Y. and Wu, Q.F. (2020) Genome-wide characterization and developmental expression profiling of long non-coding RNAs in Sogatella furcifera. Insect Science, 27, 987-997.
  6. Cui, Y., Ren, Y.D., Lyu, M., Zheng, S.C., Feng, Q.L. and Xiang, H. (2020) Genomic divergences between the two polyphagous Spodoptera relatives provide cues for successful invasion of the fall armyworm. Insect Science, 27, 1257-1265.
  7. Deng, W., Nickle, D.C., Learn, G.H., Maust, B. and Mullins, J.I. (2007) ViroBLAST: a stand-alone BLAST web server for flexible queries of multiple databases and user's datasets. Bioinformatics, 23, 2334-2336.
  8. Dimase, M., Brown, S., Head, G.P., Price, P.A., Walker, W., Yu, W. et al. (2021) Performance of Bt-susceptible and -heterozygous dual-gene resistant genotypes of Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae) in seed blends of non-Bt and pyramided Bt maize. Insect Science, https://doi.org/10.1111/1744-7917.12850.
  9. Do Nascimento, A.R., Fresia, P., Consoli, F.L. and Omoto, C. (2015) Comparative transcriptome analysis of lufenuron-resistant and susceptible strains of Spodoptera frugiperda (Lepidoptera: Noctuidae). BMC Genomics, 16, 985.
  10. Donitz, J., Gerischer, L., Hahnke, S., Pfeiffer, S. and Bucher, G. (2018) Expanded and updated data and a query pipeline for iBeetle-Base. Nucleic Acids Research, 46, D831-D835.
  11. Elsik, C.G., Tayal, A., Diesh, C.M., Unni, D.R., Emery, M.L., Nguyen, H.N. and Hagen, D.E. (2016) Hymenoptera Genome Database: integrating genome annotations in HymenopteraMine. Nucleic Acids Research, 44, D793-800.
  12. Feldmann, F., Rieckmann, U. and Winter, S. (2019) The spread of the fall armyworm Spodoptera frugiperda in Africa-What should be done next? Journal of Plant Diseases and Protection, 126, 97-101.
  13. Giraldo-Calderon, G.I., Emrich, S.J., Maccallum, R.M., Maslen, G., Dialynas, E., Topalis, P., et al. (2015) VectorBase: an updated bioinformatics resource for invertebrate vectors and other organisms related with human diseases. Nucleic Acids Research, 43, D707-D713.
  14. Gouin, A., Bretaudeau, A., Nam, K., Gimenez, S., Aury, J.M., Duvic, B., et al. (2017) Two genomes of highly polyphagous lepidopteran pests (Spodoptera frugiperda, Noctuidae) with different host-plant ranges. Scientific Reports, 7, 11816.
  15. Guan, F., Zhang, J., Shen, H., Wang, X., Padovan, A., Walsh, T.K., et al. (2021) Whole-genome sequencing to detect mutations associated with resistance to insecticides and Bt proteins in Spodoptera frugiperda. Insect Science, 28, 627-638.
  16. Gui, F., Lan, T., Zhao, Y., Guo, W., Dong, Y., Fang, D., et al. (2020) Genomic and transcriptomic analysis unveils population evolution and development of pesticide resistance in fall armyworm Spodoptera frugiperda. Protein & Cell, https://doi.org/10.1007/s13238-020-00795-7.
  17. Huang, F. (2021) Resistance of the fall armyworm, Spodoptera frugiperda, to transgenic Bacillus thuringiensis Cry1F corn in the Americas: lessons and implications for Bt corn IRM in China. Insect Science, 28, 574-589.
  18. Jing, D.P., Guo, J.F., Jiang, Y.Y., Zhao, J.Z., Sethi, A., He, K.L. and Wang, Z.Y. (2020) Initial detections and spread of invasive Spodoptera frugiperda in China and comparisons with other noctuid larvae in cornfields using molecular techniques. Insect Science, 27, 780-790.
  19. Jones, P., Binns, D., Chang, H.Y., Fraser, M., Li, W., Mcanulla, C., et al. (2014) InterProScan 5: genome-scale protein function classification. Bioinformatics, 30, 1236-1240.
  20. Kakumani, P.K., Malhotra, P., Mukherjee, S.K. and Bhatnagar, R.K. (2014) A draft genome assembly of the army worm, Spodoptera frugiperda. Genomics, 104, 134-143.
  21. Kakumani, P.K., Shukla, R., Todur, V.N., Malhotra, P., Mukherjee, S.K. and Bhatnagar, R.K. (2015) De novo transcriptome assembly and analysis of Sf21 cells using illumina paired end sequencing. Biology Direct, 10, 44.
  22. Kalderimis, A., Lyne, R., Butano, D., Contrino, S., Lyne, M., Heimbach, J., et al. (2014) InterMine: extensive web services for modern biology. Nucleic Acids Research, 42, W468-W472.
  23. Kanehisa, M., Sato, Y. and Morishima, K. (2016) BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. Journal of Molecular Biology, 428, 726-731.
  24. Kim, D., Langmead, B. and Salzberg, S.L. (2015) HISAT: a fast spliced aligner with low memory requirements. Nature Methods, 12, 357-360.
  25. Kyritsis, K.A., Wang, B., Sullivan, J., Lyne, R. and Micklem, G. (2019) InterMineR: an R package for InterMine databases. Bioinformatics, 35, 3206-3207.
  26. Landais, I., Ogliastro, M., Mita, K., Nohata, J., Lopez-Ferber, M., Duonor-Cerutti, M., et al. (2003) Annotation pattern of ESTs from Spodoptera frugiperda Sf9 cells and analysis of the ribosomal protein genes reveal insect-specific features and unexpectedly low codon usage bias. Bioinformatics, 19, 2343-2350.
  27. Langfelder, P. and Horvath, S. (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics, 9, 559.
  28. Legeai, F., Gimenez, S., Duvic, B., Escoubas, J.M., Gosselin Grenet, A.S., Blanc, F., et al. (2014) Establishment and analysis of a reference transcriptome for Spodoptera frugiperda. BMC Genomics, 15, 704.
  29. Li, F., Zhao, X., Li, M., He, K., Huang, C., Zhou, Y., Li, Z. and Walters, J.R. (2019) Insect genomes: progress and challenges. Insect Molecular Biology, 28, 739-758.
  30. Lv, S.L., Shi, Y., Zhang, J.C., Liang, P., Zhang, L. and Gao, X.W. (2021) Detection of ryanodine receptor target-site mutations in diamide insecticide-resistant Spodoptera frugiperda in China. Insect Science, 28, 639-648.
  31. Lyne, R., Smith, R., Rutherford, K., Wakeling, M., Varley, A., Guillier, F., et al. (2007) FlyMine: an integrated database for Drosophila and Anopheles genomics. Genome Biology, 8, R129.
  32. Lyne, R., Sullivan, J., Butano, D., Contrino, S., Heimbach, J., Hu, F., et al. (2015) Cross-organism analysis using InterMine. Genesis, 53, 547-60.
  33. Matthews, L.R., Vaglio, P., Reboul, J., Ge, H., Davis, B.P., Garrels, J., et al. (2001) Identification of potential interaction networks using sequence-based searches for conserved protein-protein interactions or “interologs”. Genome Research, 11, 2120-2126.
  34. Montezano, D.G., Specht, A., Sosa-Gómez, D.R., Roque-Specht, V.F., Sousa-Silva, J.C., Paula-Moraes, S.V., et al. (2018) Host plants of Spodoptera frugiperda (Lepidoptera: Noctuidae) in the Americas. African Entomology, 26, 286-300, 15.
  35. Motenko, H., Neuhauser, S.B., O'Keefe, M. and Richardson, J.E. (2015) MouseMine: a new data warehouse for MGI. Mammalian Genome, 26, 325-330.
  36. Nagoshi, R.N., Goergen, G., Tounou, K.A., Agboka, K., Koffi, D. and Meagher, R.L. (2018) Analysis of strain distribution, migratory potential, and invasion history of fall armyworm populations in northern Sub-Saharan Africa. Scientific Reports, 8, 3710.
  37. Nam, K., Gimenez, S., Hilliou, F., Blanco, C.A., Hänniger, S., Bretaudeau, A., et al. (2019) Adaptation by copy number variation increases insecticide resistance in fall armyworms. bioRxiv, https://doi.org/10.1101/812958.
  38. Nam, K., Nhim, S., Robin, S., Bretaudeau, A., Nègre, N. and d'Alençon, E. (2018) Divergent selection causes whole genome differentiation without physical linkage among the targets in Spodoptera frugiperda (Noctuidae). bioRxiv, https://doi.org/10.1101/452870.
  39. Nandakumar, S., Ma, H. and Khan, A.S. (2017) Whole-genome sequence of the Spodoptera frugiperda Sf9 insect cell line. Genome Announcments, 5(34), e00829-17.
  40. Negre, V., Hotelier, T., Volkoff, A.N., Gimenez, S., Cousserans, F., Mita, K., et al. (2006) SPODOBASE: an EST database for the lepidopteran crop pest Spodoptera. BMC Bioinformatics, 7, 322.
  41. Orsucci, M., Moné, Y., Audiot, P., Gimenez, S., Nhim, S., Naït-Saïdi, R., et al. (2018) Transcriptional plasticity evolution in two strains of Spodoptera frugiperda (Lepidoptera: Noctuidae) feeding on alternative host-plants. bioRxiv, https://doi.org/10.1101/263186.
  42. Ostlund, G., Schmitt, T., Forslund, K., Kostler, T., Messina, D.N., Roopra, S., et al. (2010) InParanoid 7: new algorithms and tools for eukaryotic orthology analysis. Nucleic Acids Research, 38, D196-D203.
  43. Pfreundt, U., James, D.P., Tweedie, S., Wilson, D., Teichmann, S.A. and Adryan, B. (2010) FlyTF: improved annotation and enhanced functionality of the Drosophila transcription factor database. Nucleic Acids Research, 38, D443-D447.
  44. Poelchau, M., Childers, C., Moore, G., Tsavatapalli, V., Evans, J., Lee, C.Y., et al. (2015) The i5k Workspace@NAL-enabling genomic data access, visualization and curation of arthropod genomes. Nucleic Acids Research, 43, D714-D719.
  45. Razick, S., Magklaras, G. and Donaldson, I.M. (2008) iRefIndex: a consolidated protein interaction database with provenance. BMC Bioinformatics, 9, 405.
  46. Robinson, M.D., Mccarthy, D.J. and Smyth, G.K. (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26, 139-140.
  47. Ruzicka, L., Bradford, Y.M., Frazer, K., Howe, D.G., Paddock, H., Ramachandran, S., et al. (2015) ZFIN, The zebrafish model organism database: Updates and new directions. Genesis, 53, 498-509.
  48. Silva-Brandao, K.L., Horikoshi, R.J., Bernardi, D., Omoto, C., Figueira, A. and Brandao, M.M. (2017) Transcript expression plasticity as a response to alternative larval host plants in the speciation process of corn and rice strains of Spodoptera frugiperda. BMC Genomics, 18, 792.
  49. Sullivan, J., Karra, K., Moxon, S.A., Vallejos, A., Motenko, H., Wong, J.D., et al. (2013) InterMOD: integrated data and tools for the unification of model organism research. Scientific Reports, 3, 1802.
  50. Unbehend, M., Hanniger, S., Vasquez, G.M., Juarez, M.L., Reisig, D., Mcneil, J.N., et al. (2014) Geographic variation in sexual attraction of Spodoptera frugiperda corn- and rice-strain males to pheromone lures. PLoS ONE, 9, e89255.
  51. Wang, K., Li, M. and Hakonarson, H. (2010) ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Research, 38, e164.
  52. Wu, Q.L., Jiang, Y.Y., Liu, J., Hu, G. and Wu, K.M. (2020) Trajectory modeling revealed a southwest-northeast migration corridor for fall armyworm Spodoptera frugiperda (Lepidoptera: Noctuidae) emerging from the North China Plain. Insect Science, https://doi.org/10.1111/1744-7917.12852.
  53. Xiao, H., Ye, X., Xu, H., Mei, Y., Yang, Y., Chen, X., et al. (2020) The genetic adaptations of fall armyworm Spodoptera frugiperda facilitated its rapid global dispersal and invasion. Molecular Ecology Resources, 20, 1050-1068.
  54. Yang, P., Hou, L., Wang, X. and Kang, L. (2019) Core transcriptional signatures of phase change in the migratory locust. Protein & Cell, 10, 883-901.
  55. Yu, S.J., Nguyen, S.N. and Abo-Elghar, G.E. (2003) Biochemical characteristics of insecticide resistance in the fall armyworm, Spodoptera frugiperda (J.E. Smith). Pesticide Biochemistry and Physiology, 77, 1-11.
  56. Zhang, L., Liu, B., Zheng, W., Liu, C., Zhang, D., Zhao, S., et al. (2019) High-depth resequencing reveals hybrid population and insecticide resistance characteristics of fall armyworm (Spodoptera frugiperda) invading China. bioRxiv. https://doi.org/10.1101/813154.

Grants

  1. KJZD-SW-L07/National Natural Science Foundation of China
  2. IPM2008/The State Key Laboratory of Integrated Management of Pest Insects and Rodents

MeSH Term

Animals
Databases, Genetic
Gene Expression Profiling
Genome, Insect
Genomics
Insect Control
Molecular Sequence Annotation
Pest Control
Spodoptera
Transcriptome
Journal Article

Links to CNCB-NGDC Resources

Database Commons: DBC007398 (FAWMine)
Article has an altmetric score of 1

Word Cloud

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