[1]

Harun S, Rohani ER, Ohme-Takagi M, Goh HH, Mohamed-Hussein ZA. 2021. ADAP is a possible negative regulator of glucosinolate biosynthesis in Arabidopsis thaliana based on clustering and gene expression analyses. Journal of Plant Research 134:327−39

doi: 10.1007/s10265-021-01257-9
[2]

Harun S, Abdullah-Zawawi MR, A-Rahman MRA, Muhammad NAN, Mohamed-Hussein ZA. 2019. SuCComBase: a manually curated repository of plant sulfur-containing compounds. Database 2019:baz021

doi: 10.1093/database/baz021
[3]

Nowicki D, Rodzik O, Herman-Antosiewicz A, Szalewska-Pałasz A. 2016. Isothiocyanates as effective agents against enterohemorrhagic Escherichia coli: insight to the mode of action. Scientific Reports 6:22263

doi: 10.1038/srep22263
[4]

Rungapamestry V, Duncan AJ, Fuller Z, Ratcliffe B. 2007. Effect of cooking brassica vegetables on the subsequent hydrolysis and metabolic fate of glucosinolates. The Proceedings of the Nutrition Society 66:69−81

doi: 10.1017/S0029665107005319
[5]

Prieto MA, López CJ, Simal-Gandara J. 2019. Glucosinolates: molecular structure, breakdown, genetic, bioavailability, properties and healthy and adverse effects. Advances in Food and Nutrition Research 90:305−50

doi: 10.1016/bs.afnr.2019.02.008
[6]

Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, et al. 2009. BLAST+: architecture and applications. BMC Bioinformatics 10:421

doi: 10.1186/1471-2105-10-421
[7]

Potter SC, Luciani A, Eddy SR, Park Y, Lopez R, et al. 2018. HMMER web server: 2018 update. Nucleic Acids Research 46:W200−W204

doi: 10.1093/nar/gky448
[8]

Huang S, Cai N, Pacheco PP, Narrandes S, Wang Y, et al. 2018. Applications of support vector machine (SVM) learning in cancer genomics. Cancer Genomics & Proteomics 15:41−51

doi: 10.21873/cgp.20063
[9]

Li Z, Tang W, You X, Hou X. 2022. LSAP: a machine learning method for leaf-senescence-associated genes prediction. Life 12:1095

doi: 10.3390/life12071095
[10]

Meher PK, Mohapatra A, Satpathy S, Sharma A, Saini I, et al. 2021. PredCRG: a computational method for recognition of plant circadian genes by employing support vector machine with Laplace kernel. Plant Methods 17:46

doi: 10.1186/s13007-021-00744-3
[11]

N'Diaye A, Byrns B, Cory AT, Nilsen KT, Walkowiak S, et al. 2020. Machine learning analyses of methylation profiles uncovers tissue-specific gene expression patterns in wheat. The Plant Genome 13:e20027

doi: 10.1002/tpg2.20027
[12]

Lamesch P, Berardini TZ, Li D, Swarbreck D, Wilks C, et al. 2012. The Arabidopsis Information Resource (TAIR): improved gene annotation and new tools. Nucleic Acids Research 40:D1202−D1210

doi: 10.1093/nar/gkr1090
[13]

Liu B, Liu F, Wang X, Chen J, Fang L, et al. 2015. Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences. Nucleic Acids Research 43:W65−W71

doi: 10.1093/nar/gkv458
[14]

Goodstein DM, Shu S, Howson R, Neupane R, Hayes RD, et al. 2012. Phytozome: a comparative platform for green plant genomics. Nucleic Acids Research 40:D1178−D1186

doi: 10.1093/nar/gkr944
[15]

Sayers EW, Bolton EE, Brister JR, Canese K, Chan J, et al. 2022. Database resources of the national center for biotechnology information. Nucleic Acids Research 50:D20−D26

doi: 10.1093/nar/gkab1112
[16]

Gupta P, Naithani S, Tello-Ruiz MK, Chougule K, D'Eustachio P, et al. 2016. Gramene database: navigating plant comparative genomics resources. Current Plant Biology 7−8:10−15

doi: 10.1016/j.cpb.2016.12.005
[17]

Yu J, Zhao M, Wang X, Tong C, Huang S, et al. 2013. Bolbase: a comprehensive genomics database for Brassica oleracea. BMC Genomics 14:664

doi: 10.1186/1471-2164-14-664
[18]

Li Z, Li Y, Liu T, Zhang C, Xiao D, et al. 2022. Non-heading Chinese cabbage database: an open-access platform for the genomics of Brassica campestris (syn. Brassica rapa) ssp. chinensis. Plants 11:1005

doi: 10.3390/plants11081005
[19]

Zheng Y, Wu S, Bai Y, Sun H, Jiao C, et al. 2019. Cucurbit Genomics Database (CuGenDB): a central portal for comparative and functional genomics of cucurbit crops. Nucleic Acids Research 47:D1128−D1136

doi: 10.1093/nar/gky944
[20]

Brown AV, Conners SI, Huang W, Wilkey AP, Grant D, et al. 2021. A new decade and new data at SoyBase, the USDA-ARS soybean genetics and genomics database. Nucleic Acids Research 49:D1496−D1501

doi: 10.1093/nar/gkaa1107
[21]

Jayakodi M, Choi BS, Lee SC, Kim NH, Park JY, et al. 2018. Ginseng Genome Database: an open-access platform for genomics of Panax ginseng. BMC Plant Biology 18:62

doi: 10.1186/s12870-018-1282-9
[22]

Sakai H, Naito K, Takahashi Y, Sato T, Yamamoto T, et al. 2016. The Vigna genome server, 'VigGS': a genomic knowledge base of the genus Vigna based on high-quality, annotated genome sequence of the azuki bean, Vigna angularis (Willd.) Ohwi & Ohashi. Plant & Cell Physiology 57:e2

doi: 10.1093/pcp/pcv189
[23]

Yu HJ, Baek S, Lee YJ, Cho A, Mun JH. 2019. The radish genome database (RadishGD): an integrated information resource for radish genomics. Database 2019:baz009

doi: 10.1093/database/baz009
[24]

Plomion C, Aury JM, Amselem J, Leroy T, Murat F, et al. 2018. Oak genome reveals facets of long lifespan. Nature Plants 4:440−52

doi: 10.1038/s41477-018-0172-3
[25]

Wei T, van Treuren R, Liu X, Zhang Z, Chen J, et al. 2021. Whole-genome resequencing of 445 Lactuca accessions reveals the domestication history of cultivated lettuce. Nature Genetics 53:752−60

doi: 10.1038/s41588-021-00831-0
[26]

Wang X, Wu J, Liang J, Cheng F, Wang X. 2015. Brassica database (BRAD) version 2.0: integrating and mining Brassicaceae species genomic resources. Database 2015:bav093

doi: 10.1093/database/bav093
[27]

Chalhoub B, Denoeud F, Liu S, Parkin IAP, Tang H, et al. 2014. Early allopolyploid evolution in the post-Neolithic Brassica napus oilseed genome. Science 345:950−53

doi: 10.1126/science.1253435
[28]

Byrne SL, Erthmann PØ, Agerbirk N, Bak S, Hauser TP, et al. 2017. The genome sequence of Barbarea vulgaris facilitates the study of ecological biochemistry. Scientific Reports 7:40728

doi: 10.1038/srep40728
[29]

Droc G, Martin G, Guignon V, Summo M, Sempéré G, et al. 2022. The banana genome hub: a community database for genomics in the Musaceae. Horticulture Research 9:uhac221

doi: 10.1093/hr/uhac221
[30]

Zhou Y, Qiao Y, Ni Z, Du J, Xiong J, et al. 2021. GDS: a genomic database for strawberries (Fragaria spp.). Horticulturae 8:41

doi: 10.3390/horticulturae8010041
[31]

Tang Y, Zhang G, Jiang X, Shen S, Guan M, et al. 2023. Genome-wide association study of glucosinolate metabolites (mGWAS) in Brassica napus L. Plants 12:639

doi: 10.3390/plants12030639
[32]

Feng X, Ma J, Liu Z, Li X, Wu Y, et al. 2022. Analysis of glucosinolate content and metabolism related genes in different parts of Chinese flowering cabbage. Frontiers in Plant Science 12:767898

doi: 10.3389/fpls.2021.767898
[33]

Gamet-Payrastre L. 2006. Signaling pathways and intracellular targets of sulforaphane mediating cell cycle arrest and apoptosis. Current Cancer Drug Targets 6:135−45

doi: 10.2174/156800906776056509
[34]

Zhang XF, Liu PY, Zhang SJ, Zhao KL, Zhao WX. 2022. Principle and progress of radical treatment for locally advanced esophageal squamous cell carcinoma. World Journal of Clinical Cases 10:12804−11

doi: 10.12998/wjcc.v10.i35.12804
[35]

Miękus N, Marszałek K, Podlacha M, Iqbal A, Puchalski C, et al. 2020. Health benefits of plant-derived sulfur compounds, glucosinolates, and organosulfur compounds. Molecules 25:3804

doi: 10.3390/molecules25173804
[36]

Fuentes F, Paredes-Gonzalez X, Kong AN T. 2015. Dietary glucosinolates sulforaphane, phenethyl isothiocyanate, indole-3-carbinol/3,3'-diindolylmethane: antioxidative stress/inflammation, Nrf2, epigenetics/epigenomics and in vivo cancer chemopreventive efficacy. Current Pharmacology Reports 1:179−96

doi: 10.1007/s40495-015-0017-y
[37]

Tanabe TS, Dahl C. 2022. HMS-S-S: a tool for the identification of Sulphur metabolism-related genes and analysis of operon structures in genome and metagenome assemblies. Molecular Ecology Resources 22:2758−74

doi: 10.1111/1755-0998.13642
[38]

Tanabe TS, Dahl C. 2023. HMSS2: an advanced tool for the analysis of sulphur metabolism, including organosulphur compound transformation, in genome and metagenome assemblies. Molecular Ecology Resources 23:1930−45

doi: 10.1111/1755-0998.13848
[39]

Bell L, Chadwick M, Puranik M, Tudor R, Methven L, et al. 2020. The Eruca sativa genome and transcriptome: a targeted analysis of sulfur metabolism and glucosinolate biosynthesis pre and postharvest. Frontiers in Plant Science 11:525102

doi: 10.3389/fpls.2020.525102
[40]

Liao N, Hu Z, Miao J, Hu X, Lyu X, et al. 2022. Chromosome-level genome assembly of bunching onion illuminates genome evolution and flavor formation in Allium crops. Nature Communications 13:6690

doi: 10.1038/s41467-022-34491-3
[41]

Yan X, Chen S. 2007. Regulation of plant glucosinolate metabolism. Planta 226(6):1343−52

doi: 10.1007/s00425-007-0627-7