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PRINT ISSN : 2319-7692
Online ISSN : 2319-7706 Issues : 12 per year Publisher : Excellent Publishers Email : editorijcmas@gmail.com / submit@ijcmas.com Editor-in-chief: Dr.M.Prakash Index Copernicus ICV 2018: 95.39 NAAS RATING 2020: 5.38 |
In conjunction with bioinformatics and comparable developments in tools, software, and visualisation modelling, current developments in plant sciences have propelled the scientific community into an active dispute over information. Despite the advent of Omics and numerous other remarkable bioinformatics tools, a considerable proportion of researchers still require further familiarisation with these instruments. The present evaluation centres on the potential implementations of diverse in silico tools and technologies in the analysis of plant sciences. Gaining knowledge of these many technologies will contribute to an enhanced comprehension of plant characteristics, including resistance to pathogens, tolerance to stress, and nutritional enhancement. Furthermore, we are collaborating on many challenges and limitations in the field of plant sciences that are associated with the bioinformatics methodology.
Alfaro, J.A., Bohländer, P., Dai, M., Filius, M., Howard, C.J., van Kooten, X.F., Ohayon, S., Pomorski, A., Schmid, S., Aksimentiev, A., Anslyn, E. V, Bedran, G., Cao, C., Chinappi, M., Coyaud, E., Dekker, C., Dittmar, G., Drachman, N., Eelkema, R., Goodlett, D., Hentz, S., Kalathiya, U., Kelleher, N.L., Kelly, R.T., Kelman, Z., Kim, S.H., Kuster, B., Rodriguez-Larrea, D., Lindsay, S., Maglia, G., Marcotte, E.M., Marino, J.P., Masselon, C., Mayer, M., Samaras, P., Sarthak, K., Sepiashvili, L., Stein, D., Wanunu, M., Wilhelm, M., Yin, P., Meller, A., Joo, C., 2021. The emerging landscape of single-molecule protein sequencing technologies. Nat. Methods 18, 604–617. https://doi.org/10.1038/s41592-021-01143-1
Angel, T.E., Aryal, U.K., Hengel, S.M., Baker, E.S., Kelly, R.T., Robinson, E.W., Smith, R.D., 2012. Mass spectrometry-based proteomics: existing capabilities and future directions. Chem. Soc. Rev. 41, 3912–3928. https://doi.org/10.1039/c2cs15331a
Antonov, A. V, Dietmann, S., Mewes, H.W., 2008. KEGG spider: interpretation of genomics data in the context of the global gene metabolic network. Genome Biol. 9, R179. https://doi.org/10.1186/gb-2008-9-12-r179
Babarinde, I.A., Li, Y., Hutchins, A.P., 2019. Computational Methods for Mapping, Assembly and Quantification for Coding and Non-coding Transcripts. Comput. Struct. Biotechnol. J. 17, 628–637. https://doi.org/https://doi.org/10.1016/j.csbj.2019.04.012
Barrera-Redondo, J., Piñero, D., Eguiarte, L.E., 2020. Genomic, Transcriptomic and Epigenomic Tools to Study the Domestication of Plants and Animals: A Field Guide for Beginners. Front. Genet. 11, 1–24. https://doi.org/10.3389/fgene.2020.00742
Beck, L., Geiger, T., 2022. MS-based technologies for untargeted single-cell proteomics. Curr. Opin. Biotechnol. 76, 102736. https://doi.org/https://doi.org/10.1016/j.copbio.2022.102736
Bhagwat, M., Young, L., Robison, R.R., 2012. Using BLAT to find sequence similarity in closely related genomes. Curr. Protoc. Bioinforma. Chapter 10, 10.8.1-10.8.24. https://doi.org/10.1002/0471250953.bi1008s37
Bumgarner, R., 2013. Overview of DNA microarrays: types, applications, and their future. Curr. Protoc. Mol. Biol. Chapter 22, Unit 22.1. https://doi.org/10.1002/0471142727.mb2201s101
Calderón-González, K.G., Hernández-Monge, J., Herrera-Aguirre, M.E., Luna-Arias, J.P., 2016. Bioinformatics Tools for Proteomics Data Interpretation BT - Modern Proteomics – Sample Preparation, Analysis and Practical Applications, in: Mirzaei, H., Carrasco, M. (Eds.),. Springer International Publishing, Cham, pp. 281–341. https://doi.org/10.1007/978-3-319-41448-5_16
Callahan, N., Tullman, J., Kelman, Z., Marino, J., 2020. Strategies for Development of a Next-Generation Protein Sequencing Platform. Trends Biochem. Sci. 45, 76–89. https://doi.org/10.1016/j.tibs.2019.09.005
Cánovas, F.M., Dumas-Gaudot, E., Recorbet, G., Jorrin, J., Mock, H.-P., Rossignol, M., 2004. Plant proteome analysis. Proteomics 4, 285–298. https://doi.org/10.1002/pmic.200300602
Chen, C., Hou, J., Tanner, J.J., Cheng, J., 2020. Bioinformatics methods for mass spectrometry-based proteomics data analysis. Int. J. Mol. Sci. 21. https://doi.org/10.3390/ijms21082873
Chen, H., Yin, X., Guo, L., Yao, J., Ding, Y., Xu, X., Liu, L., Zhu, Q.-H., Chu, Q., Fan, L., 2021. PlantscRNAdb: A database for plant single-cell RNA analysis. Mol. Plant 14, 855–857. https://doi.org/https://doi.org/10.1016/j.molp.2021.05.002
Chen, W., Yin, X., Mu, J., Yin, Y., 2007. Subfemtomole level protein sequencing by Edman degradation carried out in a microfluidic chip. Chem. Commun. 2488–2490. https://doi.org/10.1039/B700200A
Chu, Q., Zhang, X., Zhu, X., Liu, C., Mao, L., Ye, C., Zhu, Q.-H., Fan, L., 2017. PlantcircBase: A Database for Plant Circular RNAs. Mol. Plant 10, 1126–1128. https://doi.org/https://doi.org/10.1016/j.molp.2017.03.003
Conesa, A., Madrigal, P., Tarazona, S., Gomez-Cabrero, D., Cervera, A., McPherson, A., Szcze?niak, M.W., Gaffney, D.J., Elo, L.L., Zhang, X., Mortazavi, A., 2016. A survey of best practices for RNA-seq data analysis. Genome Biol. 17, 13. https://doi.org/10.1186/s13059-016-0881-8
de Graaf, S.C., Hoek, M., Tamara, S., Heck, A.J.R., 2022. A perspective toward mass spectrometry-based de novo sequencing of endogenous antibodies. MAbs 14, 2079449. https://doi.org/10.1080/19420862.2022.2079449
de Sena Brandine, G., Smith, A.D., 2019. Falco: high-speed FastQC emulation for quality control of sequencing data. F1000Research. https://doi.org/10.12688/f1000research.21142.2
DeLuca, D.S., Levin, J.Z., Sivachenko, A., Fennell, T., Nazaire, M.-D., Williams, C., Reich, M., Winckler, W., Getz, G., 2012. RNA-SeQC: RNA-seq metrics for quality control and process optimization. Bioinformatics 28, 1530–1532. https://doi.org/10.1093/bioinformatics/bts196
Dennis, G., Sherman, B.T., Hosack, D.A., Yang, J., Gao, W., Lane, H.C., Lempicki, R.A., 2003a. DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol. 4, P3. https://doi.org/10.1186/gb-2003-4-5-p3
Dennis, G., Sherman, B.T., Hosack, D.A., Yang, J., Gao, W., Lane, H.C., Lempicki, R.A., 2003b. DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol. 4, R60. https://doi.org/10.1186/gb-2003-4-9-r60
Deswal, R., Gupta, R., Dogra, V., Singh, R., Abat, J.K., Sarkar, A., Mishra, Y., Rai, V., Sreenivasulu, Y., Amalraj, R.S., Raorane, M., Chaudhary, R.P., Kohli, A., Giri, A.P., Chakraborty, N., Zargar, S.M., Agrawal, V.P., Agrawal, G.K., Job, D., Renaut, J., Rakwal, R., 2013. Plant proteomics in India and Nepal: current status and challenges ahead. Physiol. Mol. Biol. Plants 19, 461–477. https://doi.org/10.1007/s12298-013-0198-y
Dong, H., Zhang, A., Sun, H., Wang, H., Lu, X., Wang, M., Ni, B., Wang, X., 2012. Ingenuity pathways analysis of urine metabolomics phenotypes toxicity of Chuanwu in Wistar rats by UPLC-Q-TOF-HDMS coupled with pattern recognition methods. Mol. Biosyst. 8, 1206–1221. https://doi.org/10.1039/C1MB05366C
Dupree, E.J., Jayathirtha, M., Yorkey, H., Mihasan, M., Petre, B.A., Darie, C.C., 2020. A critical review of bottom-up proteomics: The good, the bad, and the future of this field. Proteomes 8, 1–26. https://doi.org/10.3390/proteomes8030014
Feng, J., Meyer, C.A., Wang, Q., Liu, J.S., Shirley Liu, X., Zhang, Y., 2012. GFOLD: a generalized fold change for ranking differentially expressed genes from RNA-seq data. Bioinformatics 28, 2782–2788. https://doi.org/10.1093/bioinformatics/bts515
Geniza, M., Jaiswal, P., 2017. Tools for building de novo transcriptome assembly. Curr. Plant Biol. 11–12, 41–45. https://doi.org/https://doi.org/10.1016/j.cpb.2017.12.004
Ghosh, S., Chan, C.-K.K., 2016. Analysis of RNA-Seq Data Using TopHat and Cufflinks. Methods Mol. Biol. 1374, 339–361. https://doi.org/10.1007/978-1-4939-3167-5_18
Giacomello, S., 2021. A new era for plant science: spatial single-cell transcriptomics. Curr. Opin. Plant Biol. 60, 102041. https://doi.org/https://doi.org/10.1016/j.pbi.2021.102041
Guo, J., Huang, Z., Sun, J., Cui, X., Liu, Y., 2021. Research Progress and Future Development Trends in Medicinal Plant Transcriptomics. Front. Plant Sci. 12, 691838. https://doi.org/10.3389/fpls.2021.691838
Hale, J.E., 2013. Advantageous Uses of Mass Spectrometry for the Quantification of Proteins. Int. J. Proteomics 2013, 219452. https://doi.org/10.1155/2013/219452
Han, X., Aslanian, A., Yates, J.R. 3rd, 2008. Mass spectrometry for proteomics. Curr. Opin. Chem. Biol. 12, 483–490. https://doi.org/10.1016/j.cbpa.2008.07.024
Hina, F., Yisilam, G., Wang, S., Li, P., Fu, C., 2020. De novo Transcriptome Assembly, Gene Annotation and SSR Marker Development in the Moon Seed Genus Menispermum (Menispermaceae). Front. Genet. 11, 1–13. https://doi.org/10.3389/fgene.2020.00380
Hou, Y.P., Diao, T.T., Xu, Z.H., Mao, X.Y., Wang, C., Li, B., 2022. Bioinformatic Analysis Combined With Experimental Validation Reveals Novel Hub Genes and Pathways Associated With Focal Segmental Glomerulosclerosis. Front. Mol. Biosci. 8, 1–9. https://doi.org/10.3389/fmolb.2021.691966
Hu, J., Rampitsch, C., Bykova, N. V., 2015. Advances in plant proteomics toward improvement of crop productivity and stress resistance. Front. Plant Sci. 6, 1–15. https://doi.org/10.3389/fpls.2015.00209
Hunt, D.F., Yates, J.R. 3rd, Shabanowitz, J., Winston, S., Hauer, C.R., 1986. Protein sequencing by tandem mass spectrometry. Proc. Natl. Acad. Sci. U. S. A. 83, 6233–6237. https://doi.org/10.1073/pnas.83.17.6233
Jammali, S., Aguilar, J.-D., Kuitche, E., Ouangraoua, A., 2019. SplicedFamAlign: CDS-to-gene spliced alignment and identification of transcript orthology groups. BMC Bioinformatics 20, 133. https://doi.org/10.1186/s12859-019-2647-2
Kanehisa, M., Goto, S., 2000. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30. https://doi.org/10.1093/nar/28.1.27
Kapustin, Y., Souvorov, A., Tatusova, T., Lipman, D., 2008. Splign: algorithms for computing spliced alignments with identification of paralogs. Biol. Direct 3, 20. https://doi.org/10.1186/1745-6150-3-20
Karagiannis, K., Simonyan, V., Mazumder, R., 2013. SNVDis: A Proteome-wide Analysis Service for Evaluating nsSNVs in Protein Functional Sites and Pathways. Genomics. Proteomics Bioinformatics 11, 122–126. https://doi.org/https://doi.org/10.1016/j.gpb.2012.10.003
Kim, D., Pertea, G., Trapnell, C., Pimentel, H., Kelley, R., Salzberg, S.L., 2013. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36. https://doi.org/10.1186/gb-2013-14-4-r36
Kosová, K., Vítámvás, P., Urban, M.O., Prášil, I.T., Renaut, J., 2018. Plant abiotic stress proteomics: The major factors determining alterations in cellular proteome. Front. Plant Sci. 9, 1–22. https://doi.org/10.3389/fpls.2018.00122
Kumar, G., Ertel, A., Feldman, G., Kupper, J., Fortina, P., 2020. iSeqQC: a tool for expression-based quality control in RNA sequencing. BMC Bioinformatics 21, 56. https://doi.org/10.1186/s12859-020-3399-8
Kumari, S., Verma, L.K., Weller, J.W., 2007. AffyMAPSDetector: a software tool to characterize Affymetrix GeneChipTM expression arrays with respect to SNPs. BMC Bioinformatics 8, 276. https://doi.org/10.1186/1471-2105-8-276
Leggett, R.M., Ramirez-Gonzalez, R.H., Clavijo, B.J., Waite, D., Davey, R.P., 2013. Sequencing quality assessment tools to enable data-driven informatics for high throughput genomics. Front. Genet. 4, 288. https://doi.org/10.3389/fgene.2013.00288
Li, Y., Ge, X., Peng, F., Li, W., Li, J.J., 2022. Exaggerated false positives by popular differential expression methods when analyzing human population samples. Genome Biol. 23, 79. https://doi.org/10.1186/s13059-022-02648-4
Libault, M., Pingault, L., Zogli, P., Schiefelbein, J., 2017. Plant Systems Biology at the Single-Cell Level. Trends Plant Sci. 22, 949–960. https://doi.org/https://doi.org/10.1016/j.tplants.2017.08.006
Liu, Y., Lu, S., Liu, K., Wang, S., Huang, L., Guo, L., 2019. Proteomics: a powerful tool to study plant responses to biotic stress. Plant Methods 15, 135. https://doi.org/10.1186/s13007-019-0515-8
Lowe, R., Shirley, N., Bleackley, M., Dolan, S., Shafee, T., 2017. Transcriptomics technologies. PLOS Comput. Biol. 13, e1005457. https://doi.org/10.1371/journal.pcbi.1005457
Macklin, A., Khan, S., Kislinger, T., 2020. Recent advances in mass spectrometry based clinical proteomics: applications to cancer research. Clin. Proteomics 17, 17. https://doi.org/10.1186/s12014-020-09283-w
Mahmood, K., Orabi, J., Kristensen, P.S., Sarup, P., Jørgensen, L.N., Jahoor, A., 2020. De novo transcriptome assembly, functional annotation, and expression profiling of rye (Secale cereale L.) hybrids inoculated with ergot (Claviceps purpurea). Sci. Rep. 10, 13475. https://doi.org/10.1038/s41598-020-70406-2
Malone, J.H., Oliver, B., 2011. Microarrays, deep sequencing and the true measure of the transcriptome. BMC Biol. 9, 34. https://doi.org/10.1186/1741-7007-9-34
Martin, L.B.B., Fei, Z., Giovannoni, J.J., Rose, J.K.C., 2013. Catalyzing plant science research with RNA-seq. Front. Plant Sci. 4, 1–10. https://doi.org/10.3389/fpls.2013.00066
Mergner, J., Kuster, B., 2022. Plant Proteome Dynamics. Annu. Rev. Plant Biol. 73, 67–92. https://doi.org/10.1146/annurev-arplant-102620-031308
Mi, H., Muruganujan, A., Casagrande, J.T., Thomas, P.D., 2013. Large-scale gene function analysis with the PANTHER classification system. Nat. Protoc. 8, 1551–1566. https://doi.org/10.1038/nprot.2013.092
Miyashita, M., Presley, J.M., Buchholz, B.A., Lam, K.S., Lee, Y.M., Vogel, J.S., Hammock, B.D., 2001. Attomole level protein sequencing by Edman degradation coupled with accelerator mass spectrometry. Proc. Natl. Acad. Sci. U. S. A. 98, 4403–4408. https://doi.org/10.1073/pnas.071047998
Moreno-Santillán, D.D., Machain-Williams, C., Hernández-Montes, G., Ortega, J., 2019. De Novo Transcriptome Assembly and Functional Annotation in Five Species of Bats. Sci. Rep. 9, 6222. https://doi.org/10.1038/s41598-019-42560-9
Pandeswari, P.B., Sabareesh, V., 2019. Middle-down approach: a choice to sequence and characterize proteins/proteomes by mass spectrometry. RSC Adv. 9, 313–344. https://doi.org/10.1039/C8RA07200K
Patole, C., Bindschedler, L. V, 2019. Chapter 4 - Plant proteomics: A guide to improve the proteome coverage, in: Meena, S.N., Naik, M.M.B.T.-A. in B.S.R. (Eds.),. Academic Press, pp. 45–67. https://doi.org/https://doi.org/10.1016/B978-0-12-817497-5.00004-5
Pierlé, S.A., Dark, M.J., Dahmen, D., Palmer, G.H., Brayton, K.A., 2012. Comparative genomics and transcriptomics of trait-gene association. BMC Genomics 13, 669. https://doi.org/10.1186/1471-2164-13-669
Raghavan, V., Kraft, L., Mesny, F., Rigerte, L., 2022. A simple guide to de novo transcriptome assembly and annotation. Brief. Bioinform. 23. https://doi.org/10.1093/bib/bbab563
Ragoussis, J., Elvidge, G., 2006. Affymetrix GeneChip® system: moving from research to the clinic. Expert Rev. Mol. Diagn. 6, 145–152. https://doi.org/10.1586/14737159.6.2.145
Rajczewski, A.T., Jagtap, P.D., Griffin, T.J., 2022. An overview of technologies for MS-based proteomics-centric multi-omics. Expert Rev. Proteomics 19, 165–181. https://doi.org/10.1080/14789450.2022.2070476
Rao, M.S., Van Vleet, T.R., Ciurlionis, R., Buck, W.R., Mittelstadt, S.W., Blomme, E.A.G., Liguori, M.J., 2019. Comparison of RNA-Seq and microarray gene expression platforms for the toxicogenomic evaluation of liver from short-term rat toxicity studies. Front. Genet. 10, 1–16. https://doi.org/10.3389/fgene.2018.00636
Robinson, M.D., McCarthy, D.J., Smyth, G.K., 2010. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140. https://doi.org/10.1093/bioinformatics/btp616
Rodriques, S.G., Marblestone, A.H., Boyden, E.S., 2019. A theoretical analysis of single molecule protein sequencing via weak binding spectra. PLoS One 14, e0212868. https://doi.org/10.1371/journal.pone.0212868
Sessegolo, C., Cruaud, C., Da Silva, C., Cologne, A., Dubarry, M., Derrien, T., Lacroix, V., Aury, J.-M., 2019. Transcriptome profiling of mouse samples using nanopore sequencing of cDNA and RNA molecules. Sci. Rep. 9, 14908. https://doi.org/10.1038/s41598-019-51470-9
Simpson, J.T., Wong, K., Jackman, S.D., Schein, J.E., Jones, S.J.M., Birol, I., 2009. ABySS: a parallel assembler for short read sequence data. Genome Res. 19, 1117–1123. https://doi.org/10.1101/gr.089532.108
Singhal, N., Kumar, M., Kanaujia, P.K., Virdi, J.S., 2015. MALDI-TOF mass spectrometry: An emerging technology for microbial identification and diagnosis. Front. Microbiol. 6, 1–16. https://doi.org/10.3389/fmicb.2015.00791
Smythers, A.L., Hicks, L.M., 2021. Mapping the plant proteome: tools for surveying coordinating pathways. Emerg. Top. Life Sci. 5, 203–220. https://doi.org/10.1042/ETLS20200270
Spies, D., Ciaudo, C., 2015. Dynamics in Transcriptomics: Advancements in RNA-seq Time Course and Downstream Analysis. Comput. Struct. Biotechnol. J. 13, 469–477. https://doi.org/https://doi.org/10.1016/j.csbj.2015.08.004
Squair, J.W., Gautier, M., Kathe, C., Anderson, M.A., James, N.D., Hutson, T.H., Hudelle, R., Qaiser, T., Matson, K.J.E., Barraud, Q., Levine, A.J., La Manno, G., Skinnider, M.A., Courtine, G., 2021. Confronting false discoveries in single-cell differential expression. Nat. Commun. 12, 5692. https://doi.org/10.1038/s41467-021-25960-2
Standing, K.G., 2003. Peptide and protein de novo sequencing by mass spectrometry. Curr. Opin. Struct. Biol. 13, 595–601. https://doi.org/https://doi.org/10.1016/j.sbi.2003.09.005
Suriyakala, G., Sathiyaraj, S., Gandhi, A.D., Vadakkan, K., Mahadeva Rao, U.S., Babujanarthanam, R., 2021. Plumeria pudica Jacq. flower extract - mediated silver nanoparticles: Characterization and evaluation of biomedical applications. Inorg. Chem. Commun. 126, 108470. https://doi.org/10.1016/j.inoche.2021.108470
Tamara, S., den Boer, M.A., Heck, A.J.R., 2022. High-Resolution Native Mass Spectrometry. Chem. Rev. 122, 7269–7326. https://doi.org/10.1021/acs.chemrev.1c00212
Trapnell, C., Pachter, L., Salzberg, S.L., 2009. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25, 1105–1111. https://doi.org/10.1093/bioinformatics/btp120
Trapnell, C., Williams, B.A., Pertea, G., Mortazavi, A., Kwan, G., van Baren, M.J., Salzberg, S.L., Wold, B.J., Pachter, L., 2010. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28, 511–515. https://doi.org/10.1038/nbt.1621
Ungaro, A., Pech, N., Martin, J.-F., McCairns, R.J.S., Mévy, J.-P., Chappaz, R., Gilles, A., 2017. Challenges and advances for transcriptome assembly in non-model species. PLoS One 12, e0185020. https://doi.org/10.1371/journal.pone.0185020
Vadakkan, K., 2020. Molecular Mechanism of Bacterial Quorum Sensing and Its Inhibition by Target Specific Approaches. ACS Symp. Ser. 1374, 21–234. https://doi.org/10.1021/bk-2020-1374.ch012
Vadakkan, K., 2019. Acute and sub-acute toxicity study of bacterial signaling inhibitor Solanum torvum root extract in Wister rats. Clin. Phytoscience 5. https://doi.org/10.1186/s40816-019-0113-3
Vadakkan, K., Cheruvathur, M.K., Chulliparambil, A.S., Francis, F., Abimannue, A.P., 2021. Proteolytic enzyme arbitrated antagonization of helminthiasis by Cinnamomum cappara leaf extract in Pheretima posthuma. Clin. Phytoscience 7. https://doi.org/10.1186/s40816-021-00261-9
Vadakkan, Kayeen, Choudhury, A.A., Gunasekaran, R., Hemapriya, J., Vijayanand, S., 2018a. Quorum sensing intervened bacterial signaling: Pursuit of its cognizance and repression. J. Genet. Eng. Biotechnol. 16, 239–252. https://doi.org/10.1016/j.jgeb.2018.07.001
Vadakkan, Kayeen, Choudhury, A.A., Gunasekaran, R., Hemapriya, J., Vijayanand, S., 2018b. Quorum sensing intervened bacterial signaling: Pursuit of its cognizance and repression. J. Genet. Eng. Biotechnol. 16, 239–252. https://doi.org/10.1016/j.jgeb.2018.07.001
Vadakkan, K., Gunasekaran, R., Choudhury, A.A., Ravi, A., Arumugham, S., Hemapriya, J., Vijayanand, S., 2018. Response Surface Modelling through Box-Behnken approach to optimize bacterial quorum sensing inhibitory action of Tribulus terrestris root extract. Rhizosphere 6, 134–140. https://doi.org/10.1016/j.rhisph.2018.06.005
Vadakkan, K., Hemapriya, J., Anbarasu, A., Ramaiah, S., Vijayanand, S., 2020. Quorum quenching by 2-Hydroxyanisole extracted from Solanum torvum on Pseudomonas aeruginosa and its inhibitory action upon LasR protein. Gene Reports 21, 100802. https://doi.org/10.1016/j.genrep.2020.100802
Vadakkan, K., Hemapriya, J., Selvaraj, V., 2019a. Quorum quenching intervened in vivo attenuation and immunological clearance enhancement by Solanum torvum root extract against Pseudomonas aeruginosa instigated pneumonia in Sprague Dawley rats. Clin. Phytoscience 5, 24. https://doi.org/10.1186/s40816-019-0120-4
Vadakkan, K., Hemapriya, J., Selvaraj, V., 2019b. Quorum quenching intervened in vivo attenuation and immunological clearance enhancement by Solanum torvum root extract against Pseudomonas aeruginosa instigated pneumonia in Sprague Dawley rats.
Vadakkan, Kayeen, Vijayanand, S., Choudhury, A.A., Gunasekaran, R., Hemapriya, J., 2018c. Optimization of quorum quenching mediated bacterial attenuation of Solanum torvum root extract by response surface modelling through Box-Behnken approach. J. Genet. Eng. Biotechnol. https://doi.org/10.1016/j.jgeb.2018.02.001
Vadakkan, Kayeen, Vijayanand, S., Choudhury, A.A., Gunasekaran, R., Hemapriya, J., 2018d. Optimization of quorum quenching mediated bacterial attenuation of Solanum torvum root extract by response surface modelling through Box-Behnken approach. J. Genet. Eng. Biotechnol. 16, 381–386. https://doi.org/10.1016/j.jgeb.2018.02.001
Vadakkan, K., Vijayanand, S., Hemapriya, J., Gunasekaran, R., 2019c. Quorum sensing inimical activity of Tribulus terrestris against gram negative bacterial pathogens by signalling interference. 3 Biotech 9, 163. https://doi.org/10.1007/s13205-019-1695-7
Vecchi, M.M., Xiao, Y., Wen, D., 2019. Identification and Sequencing of N-Terminal Peptides in Proteins by LC-Fluorescence-MS/MS: An Approach to Replacement of the Edman Degradation. Anal. Chem. 91, 13591–13600. https://doi.org/10.1021/acs.analchem.9b02754
Vitorino, R., Guedes, S., Trindade, F., Correia, I., Moura, G., Carvalho, P., Santos, M.A.S., Amado, F., 2020. De novo sequencing of proteins by mass spectrometry. Expert Rev. Proteomics 17, 595–607. https://doi.org/10.1080/14789450.2020.1831387
Walker, J.M., 1997. The Dansyl-Edman Method for Manual Peptide Sequencing BT - Protein Sequencing Protocols, in: Smith, B.J. (Ed.),. Humana Press, Totowa, NJ, pp. 183–187. https://doi.org/10.1385/0-89603-353-8:183
Wang, B., Kumar, V., Olson, A., Ware, D., 2019. Reviving the transcriptome studies: An insight into the emergence of single-molecule transcriptome sequencing. Front. Genet. 10, 1–11. https://doi.org/10.3389/fgene.2019.00384
Wang, Z., Gerstein, M., Snyder, M., 2009. RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10, 57–63. https://doi.org/10.1038/nrg2484
Winck, F.V., dos Santos, A.L.W., Calderan-Rodrigues, M.J., 2021. Plant Proteomics and Systems Biology BT - Advances in Plant Omics and Systems Biology Approaches, in: Vischi Winck, F. (Ed.),. Springer International Publishing, Cham, pp. 51–66. https://doi.org/10.1007/978-3-030-80352-0_3
Wu, T.D., Watanabe, C.K., 2005. GMAP: a genomic mapping and alignment program for mRNA and EST sequences. Bioinformatics 21, 1859–1875. https://doi.org/10.1093/bioinformatics/bti310
Xie, H., Wang, W., Sun, F., Deng, K., Lu, X., Liu, H., Zhao, W., Zhang, Y., Zhou, X., Li, K., Hou, Y., 2017. Proteomics analysis to reveal biological pathways and predictive proteins in the survival of high-grade serous ovarian cancer. Sci. Rep. 7, 9896. https://doi.org/10.1038/s41598-017-10559-9
Yang, I.S., Kim, S., 2015. Analysis of Whole Transcriptome Sequencing Data: Workflow and Software. Genomics Inform. 13, 119–125. https://doi.org/10.5808/GI.2015.13.4.119
Yang, M., Wang, Q., Wang, S., Wang, Y., Zeng, Q., Qin, Q., 2019. Transcriptomics analysis reveals candidate genes and pathways for susceptibility or resistance to Singapore grouper iridovirus in orange-spotted grouper (Epinephelus coioides). Dev. Comp. Immunol. 90, 70–79. https://doi.org/10.1016/j.dci.2018.09.003
Yang, Y., Smith, S.A., 2013. Optimizing de novo assembly of short-read RNA-seq data for phylogenomics. BMC Genomics 14, 328. https://doi.org/10.1186/1471-2164-14-328
Yu, J., Gu, X., Yi, S., 2016. Ingenuity pathway analysis of gene expression profiles in distal nerve stump following nerve injury: Insights into wallerian degeneration. Front. Cell. Neurosci. 10, 1–12. https://doi.org/10.3389/fncel.2016.00274
Zhou, Q., Su, X., Jing, G., Chen, S., Ning, K., 2018. RNA-QC-chain: comprehensive and fast quality control for RNA-Seq data. BMC Genomics 19, 144. https://doi.org/10.1186/s12864-018-4503-6