
LXVIII SIGA Annual Congress
“Leveraging genetic innovation for future-proofing crops – From conventional breeding to multi-omics and AI in sustainable agriculture”
09-12 September 2025

Session 1 – Pan-genomics and multi-omics: applications in plant breeding
Session 2 – Advances in industrial crops genetics and breeding
Session 3 – Crop resilience to climate changes for food security
Session 5 – New avenues for new cultivars
Session 6 – Genetic innovations in fruit trees
TUESDAY, SEPTEMBER 09
| 12:00 – 14:00 | Registration |
| 14:00 - 14:30 | Opening ceremony Chairpersons: Silvio Salvi, Stefania Masci, Giovanna Frugis Welcome addresses by Institutional and Local Authorities |
| 14:30 - 16:45 | Session 1 - Pan-genomics and multi-omics: applications in plant breeding Chairpersons: Giorgia Batelli, Riccardo Aiese Cigliano |
| 14:30 – 15:00 | Invited Lecture Martin Mascher - IPK Gatersleben (DE) Barley crop evolution through the lens of pangenomics |
| 15:00 – 15:15 | Popoli A., Colozza D., d'Atti D., Faino L., Pajoro A. A long-read genome assembly and multi-omics analysis to accelerate breeding in wild rocket (Diplotaxis tenuifolia) |
| 15:15 – 15:30 | Negussu M., Ventimiglia M., Vieri W., Buti M., Giosa D., Trifilò P., Nocentini M., Soares C., Fidalgo F., Karalija E., Meoni G., Turano P., Pollastri S., Loreto F., Martinelli F. Balancing growth and resilience: a multi-omics study of chickpea's intergenerational adaptive strategies under drought stress |
| 15:30 – 15:45 | Farinon B., Olivieri F., Picarella M.E., Aprea G., Ferrante P., Giuliano G., Mazzucato A. Integrated BSA-seq, RNA-seq, and GWAS analyses to identify candidate genes associated with the stigma position trait in cultivated tomato |
| 15:45 – 16:00 | Chiatti V., De Carolis C., De Luca V., Fabriani M., Gentile D., Iannelli M.A., Iori V., Di Milia A., Marano V., Mosconi P., Dimitrakopoulou S., Desplanches C., Fain V.V., Guilloteau Fonteny E., Mulder S., Suidgeest F., De Kraker J.-W., De Boer G.-J., Sestili F., Frugis G. A multi-omic approach to enhance abiotic stress resilience in Cichorium endivia |
| 16:00 – 16:15 | Mazzucotelli E., Llaca V., Fengler K., Charlotte H., Zastrow-Hayes G., Forestan C., Lev-Mirom Y., Swarbreck D., Gundlach H., Navratilova P., Šimková H., Faccioli P., Giorgioni M., Desiderio F., Valladares A.P., Bozzoli M., Gemy K., Lux T., Chawla H., Pirona R., Lauria M., Ceriotti A., Giuliano G., Gadaleta A., Krattinger S., Cavalet-Giorsa E., Pecchioni N., Mastrangelo A.M., Marone D., Eilam T., Oren L., Faris J., Xu S., Sanchez J.I., Stella A., Milanesi L., Lazzari B., Cozzi P., Tavakol E., Bassi F., Baum M., Distelfeld A., Chantret N., Girodolle J., Ranwez V., Provart N., Sen T., Eric Y., Masci S., Sestili F., Curci L., Sonnante G., Morgante M., Spannagl M., Hall A., Pozniak C., Maccaferri M., Tuberosa R., Cattivelli L. Svevo reference genome 2.0: a chromosome-level de novo assembly to support the durum wheat community |
| 16:15 – 16:30 | Carballo J., Gallo C., Selva J.P., Zappacosta D., Echenique V., Albertini E., Caccamo M. Eragrostis curvula genome assembly unveils a single region involved in apomixis |
| 16:30 – 16:45 | General discussion |
| 16:45 – 17:15 | Coffee break and poster viewing |
| 17:15 – 19:00 | Session 2 - Advances in industrial crops genetics and breeding Chairpersons: Roberta Paris, Pasquale Tripodi |
| 17:15 – 17:45 | Invited Lecture Christian Jung - University of Kiel (DE) Genome-wide association studies lead to identifying genes important for sugar beet domestication and breeding |
| 17:45 – 18:00 | Bassolino L., Fulvio F., Righetti L., Terracciano I., Paris R. Unveiling metabolomic and biosynthetic profile of polyphenols in Cannabis sativa L. |
| 18:00 – 18:15 | Biselli C., Fricano A., Carra A., Vietto L., Picco F., Rosso L., Carletti G., Jorge V., Teani A., Valentini F.M., Cattivelli L., Nervo G. New breeding tools for improving poplar resistance to Woolly Poplar Aphid |
| 18:15 – 18:30 | Rossini L., Tavakol E., Shariati V., Hause B., Muehlbauer G., Ferrario C., Gregis V., Shaaf S., Biswas A., Janiak A., Yoneyama K., Brewer P. Barley tillering genes and hormonal pathways |
| 18:30 – 18:45 | Testone G., Dougué Kentsop R.A., Giannino D., da Silva Linge C., Galasso I., Genga A., Delledonne A., Biffani S., Roda G., Frugis G., Mattana M. Effects of methyl jasmonate and chitosan nanoparticle treatments on field-grown hemp inflorescences: integrating transcriptomic and secondary metabolite insights |
| 18:45 – 19:00 | General discussion |
| 19:15 - 20:30 | Welcome cocktail - Municipal Garden, Palazzo dei Priori - Piazza del Plebiscito 14, Viterbo |
WEDNESDAY, SEPTEMBER 10
| 09:00 – 11:00 | Session 3 - Crop resilience to climate changes for food security Chairpersons: Giovanna Frugis, Andrea Mazzucato |
| 09:00 – 09:30 | Invited Lecture Sotiros Fragkostefanakis - Goethe University Frankfurt (DE) The molecular toolkit for tomato heat resilience: Key players shaping thermotolerance |
| 09:30 – 09:45 | Gervasoni D., Baldoni E., Pirona R., Colanero S., Landoni B., Martignago D., Castorina G., Gallo G., Conti L., Galbiati M. Beyond guard cells: SlMYB60 editing reveals a broader role in epidermal regulation and cuticle permeability in tomato |
| 09:45 – 10:00 | Giannelli G., Luche S., Righetti L., Galaverna G., Bonini P., Gazza L., Visioli G. At the root of perennialism: how the perennial line OK72 shows a shift of specialized metabolome toward wheatgrass species |
| 10:00 – 10:15 | Lezzi A., Stagnati L., Caproni L., Dell'Acqua M., Busconi M., Lanubile A., Marocco A. Unlocking local adaptation: harnessing the genetic diversity of Italian traditional maize landraces through landscape genomics |
| 10:15 – 10:30 | Xi X., Colombo M., Carletti G., Puglisi D., Casas A., Igartua E., Contreras-Moreira E., Cattivelli L., Fricano A. Single primer enrichment technology and whole genome resequencing elucidate the molecular basis of regional adaptation of barley in the Mediterranean |
| 10:30 – 10:45 | Bellon O., Previtali P., Green E., Fasoli M., Shackel K., Cousins P., Zenoni S., Dokoozlian N. Hormone metabolism regulates fruit maturation in a slow ripening grape genotype |
| 10:45 – 11:00 | Mukamanasasira G., Okando T., Macharia M., Runo S., Maina F., Lasky J., Fadda C., Gebrehawaryat Y., Caproni L., Pè M.E., Dell'Acqua M. Resilient roots: harnessing landrace diversity for climate-smart sorghum in East Africa |
| 11:00 – 11:30 | Coffee Break and Poster Viewing |
| 11:30 – 13:00 | Session 3 - cont. |
| 11:30 – 11:45 | Goretti D., Bocchio M., Givonetti A., Bazzano C., Vitiello G., Bona E., Cavaletto M., Pagliano C., Mica E., Valè G. Toward climate-resilient rice: identifying genotypes tolerant to drought and salinity |
| 11:45 – 12:00 | Sangiorgi G., Forestan C., Camerlengo F., Sciara G., Bozzoli M., Fricano A., Tondelli A., Fusi R., Bhosale R., Tuberosa R., Salvi S. Genetic dissection of root traits in barley (Hordeum vulgare L.) identifies major QTLs and domestication signature |
| 12:00 – 12:15 | Branchi A., Crosatti C., Mica E., Colombo M., Gazzetti K., Guerra D., Faccioli P., Lo Piero A.R., Radchuk V., Gallavotti A., Galli M., Dockter C., Cattivelli L., Battaglia R. Fine tuning of the HvGRF4 expression improves seed size in barley |
| 12:15 – 12:30 | Bertagnon G., Mineri L., Mirone F., Bono G.A., Fornara F., Brambilla V. EMS mutagenesis and MutMap to identify new alleles for resilient crops breeding |
| 12:30 – 12:45 | Olivieri F., Farinon B., Mancini L., Mazzucato A. Heat tolerance in seeds: a multi-omic approach revealed putative regions controlling germination under high temperature conditions in tomato |
| 12:45 – 13:00 | General discussion |
| 13:00 – 14:30 | Lunch Break |
| 14:00 – 14:15 | Affixing of the philatelic cancellation on the Congress postcard featuring the Strampelli commemorative stamp |
| 14:30 – 16:30 | Session 4 - Next Generation SIGA Session Innovative tools in plant genetics: Exploring the future of phenotyping and data analysis in the AI era Chairpersons: Maria Dellino, Damiano Puglisi |
| 14:30 – 15:00 | Invited Lecture Tom Theeuwen - Jan IngenHousz Institute (NL) From photosynthesis to biomass: using natural genetic variation to identify photosynthetic limitations in field grown crops |
| 15:00 – 15:15 | Graci S., Moshelion M., Francesca S., Rigano M.M., Barone A. Precision phenotyping and genomic insight for drought-tolerance screening in tomato |
| 15:15 – 15:30 | Bertoncini A., Pasquariello M., Palma D., Cattivelli L., Tondelli A., Guerra D. From pixel to stomata: leveraging AI for high resolution analysis of stomatal traits in a worldwide barley collection |
| 15:30 – 15:45 | Vieri W., F. Grinberg N., I. Orhobor O., Belocchi A., Buti M., Paffetti D. Uncovering genetic drivers of yield and protein content in durum wheat (Triticum durum Desf.) using machine learning and GWAS approaches |
| 15:45 – 16:00 | Sechi M., Kiros A.Y., Caffi A., Marziali R., Cominelli E., Consonni G., Galbiati M., Porcedda R., Vandin A., Caproni L., Castorina G., Dell'Acqua M. Uncovering the genetic determinants of drought tolerance in the MAGIC maize population through high-throughput phenotyping of cuticular permeability and AI-driven analysis of stomatal traits |
| 16:00 – 16:15 | Gipli V., Stich B., Rossini L. Tailoring a genomic selection model to predict culm morphology traits in two-row and six-row barley under different marker densities |
| 16:15 – 16:30 | General discussion |
| 16:30 – 17:00 | Coffee Break and Poster Viewing |
| 17:00 – 19:30 | SIGA General Assembly |
THURSDAY, SEPTEMBER 11
| 09:00 – 11:00 | Session 5 - New avenues for new cultivars Chairpersons: Vittoria Brambilla, Silvia Giuliani |
| 09:00 – 09:30 | Invited Lecture Sebastian Soyk - University of Lausanne (CH) Dissecting genetic interactions with impact on flower formation in tomato |
| 09:30 – 09:45 | Guerriero M., Arcieri F., Delvento C., Giudice G., Cannarella M.S., Mimiola G., Cavallo G., Ricciardi L., Lotti C., Pavan S. Whole-genome sequencing and phenotyping of neglected and underutilized vegetable melons from the Salento diversity centre (Southern Italy) |
| 09:45 – 10:00 | Ferrero M., Paladini F., Martina M., Valentino D., Milani A.M., Lanteri S., Portis E., Comino C., Acquadro A., Moglia A. Downy mildew resistance 6 (DMR6) knockout and multi-stress resilience in eggplant |
| 10:00 – 10:15 | Rossi R., Da Silva Linge C., Gipli V., Friel J., Shaaf S., Maurer A., Pillen K., Tamm Ü., Daszkowska-Golec A., Gajecka M., Tondelli A., Cattivelli L., Rossini L. Dissection of culm morphology traits in two barley double haploid populations grown in different locations |
| 10:15 – 10:30 | Mirone F., Rossoni A., Mineri L., Vicentini G., Kunova A., Martinotti M., Huang Y.-S., Langner T., Fornara F., Brambilla V.F. Marker-assisted selection and CRISPR–Cas9 approaches for durable blast resistance in Italian rice |
| 10:30 – 10:45 | Viola P., Faccini N., Tondelli A., Cattivelli L., Invernizzi C. APSOV-CREA-GB: an example of public-private partnership to support small grain cereal sector |
| 10:45 – 11:00 | General discussion |
| 11:00 – 11:30 | Coffee Break and Poster Viewing |
| 11:30 – 13:15 | Session 6 - Genetic innovations in fruit trees Chairpersons: Salvatore Esposito, Francesca Taranto |
| 11:30 – 12:00 | Invited Lecture Dario Cantu - University of California (USA) Bridging the gap between grape genomics and viticulture innovation |
| 12:00 - 12:15 | Salvagnin U., Pupi E., Di Gaspero G., Vezzulli S., Dalla Costa L., Giacomelli L. Transgene-free CRISPR-Cas and cisgenesis approaches for resistance to powdery and downy mildew in grapevine |
| 12:15 - 12:30 | Garosi C., Paffetti D., Arcidiaco L., Ottanelli A., Vettori C. Identification of best local olive tree genotype resilient to drought |
| 12:30 - 12:45 | Catalano C., Di Guardo M., Gusella G., Inzirillo I., Cannizzaro G., La Malfa S., Polizzi G., Gentile A., Distefano G. Genetic insights into almond tolerance to constriction canker through GWAS |
| 12:45 - 13:00 | Fanelli V., Miazzi M., Procino S., Fruggiero C., Cafferati Beltrame L., Volpicella M., Mascio I., Susca L., Montilon V., Taranto F., D'Agostino N., Montemurro C. Investigating the olive response to Xylella fastidiosa infection through a transcriptomic approach |
| 13:00 - 13:15 | Ordoñez Trejo E., Consiglio F., Termolino P., Palomba E., Iovene M., Aiese Cigliano R., Di Marsico M., Varotto S., Bonghi C. De novo genome comparison of a slow-ripening mutant and a commercial cultivar of Prunus persica uncovers a structural variant underlying fruit development and ripening |
| 13:15 – 14:15 | Lunch Break |
| 14:15 – 16:30 | Poster Session |
| 16:30 – 17:00 | Coffee Break and Poster Viewing |
| 17:00 – 19:00 | Workshop – Start-up in the fields: People bridging academia and industry Discussant: Luca Polizzano Participants: Ratan Chopra – Covercress Marco Di Marsico - Sequentia Biotech Carmen Laezza - Immunoveg Simona Masiero - Peptofarm Davide Scaglione - IGA Technology Services Davide Sosso - Heritable Sara Zenoni - Edivite |
| 20:30 | Social Event - Medieval Cloister, Monumental Complex of Santa Maria in Gradi - Via Santa Maria in Gradi 4, Viterbo |
FRIDAY, SEPTEMBER 12
| 09:30 – 10:15 | SIGA Young Researcher Award 2025 Dedicated to Carlo Jucci Chairpersons: Silvio Salvi, Daniele Rosellini Pieri A. Transcriptomic response to nitrogen availability reveals signatures of adaptive plasticity during tetraploid wheat domestication Bozzoli M. Dissecting the genetic basis of resistance to Soil‑borne cereal mosaic virus (SBCMV) in durum wheat by bi‑parental mapping and GWAS Moffa L., Mannino G., Bevilacqua I. CRISPR/Cas9-driven double modification of grapevine MLO6-7 imparts powdery mildew resistance, while editing of NPR3 augments powdery and downy mildew tolerance Award ceremony |
| 10:15 – 12:45 | Session 7 - Genome editing beyond single gene knockout Chairpersons: Fabio D’Orso, Domenica Nigro |
| 10:15 - 10:45 | Invited Lecture Caixia Gao - Chinese Academy of Science (CN) Advancing crop improvement with Next-Generation CRISPR Technologies |
| 10:45 - 11:00 | Ottaviani L., Lefeuvre R., Montes E., Widiez T., Giorni P., Mithöfer A., Marocco A., Lanubile A. The inactivation by CRISPR/Cas9 system of ZmWRKY125 gene improves resistance to Fusarium verticillioides in maize |
| 11:00 - 11:15 | Rogo U., Merwaiss F., Aragonés V., Fambrini M., Pugliesi C., Michelmore R., Daròs J.-A., Giordani T. Virus-Induced Gene Editing (VIGE): from model systems to crop applications (Lactuca sativa L.) |
| 11:15 - 11:30 | Farinati S., Soria Garcia A.F., Draga S., Palumbo F., Vannozzi A., Barcaccia G. Targeting MYB80 in tomato: a DNA-free strategy for engineering male sterility |
| 11:30 - 11:45 | D'Attilia C., Buffagni V., Hatyta S., Smedley M.A., Masci S., Palombieri S., Sestili F. Enhancing climate resilience in durum wheat via CRISPR/Cas9-mediated knockout of the brassinosteroid regulator IBH1 |
| 11:45 - 12:00 | Rossoni A., Mineri L., Mirone F., Bono G.A., Vicentini G., Fornara F., Brambilla V.F. Prime editing, editing of multiple cis-regulatory sequences and trait pyramiding to support rice breeding |
| 12:00 - 12:15 | Zattoni S., Bertini E., D'Inca E., Lissandrini S., De Luca R., Perlati I., Cattaneo L., Fasoli M., Polverari A., Pezzotti M., Sena G., Zenoni S. Multiple DNA-free genome editing in grapevine |
| 12:15 - 12:30 | Fabene E., Nava M., Sandri C., Ricci D., Cuccurullo A., Lobato-Gomez M., Wang J.Y., Nicolia A., Granell A., Al-Babili S., Santi L., Diretto G., Demurtas O. Pathway discovery in apocarotenoid metabolism: focus on putative zaxinone synthase enzymes in tomato |
| 12:30 - 12:45 | General discussion |
| 12:45 – 13:00 | Closing Ceremony Chairpersons: Silvio Salvi, Stefania Masci |
Keywords index
| A | |
| ABA | 6.34 |
| ABA signalling | 3.27 |
| Abc1k | 8.18 |
| Abiotic Stress | 3.39 |
| abiotic stress | 3.26, 3.28, 3.54, 8.18 |
| Abiotic stress | 3.12, 3.25, 3.31, 4.02, 6.03, 7.05, 8.27 |
| abiotic stresses | 8.24 |
| abscisic acid signaling | 3.02 |
| acclimation | 3.58 |
| adaptation | 3.05 |
| Adverse reactions to wheat | 2.15 |
| AFLP | 3.36 |
| Agrobiodiversity | 8.20 |
| agroecological sustainability | 4.10 |
| agronomic traits | 8.43 |
| AI-supported Early Warning System | 8.07 |
| AImodel | 4.03 |
| Albania | 5.10 |
| Alfalfa | 1.12 |
| alien gene transfer | 1.30 |
| alien species | 3.54 |
| Allium cepa | 8.20 |
| Almond | 6.21 |
| Almond genome | 6.14 |
| alternative splicing | 3.63 |
| Ancient Olive trees | 8.06 |
| Anthesis | 2.07 |
| anthocyanidins | 6.35 |
| anthocyanins | 6.36, 8.36 |
| Apocarotenoids | 1.19, 7.08 |
| APOMIXIS | 1.07 |
| apomixis | 2.08 |
| App | 4.12 |
| Apple germplasm | 8.04 |
| Arabidopsis thaliana | 1.27, 8.18 |
| Artichoke characterization | 4.09 |
| Artificial Intelligence | 4.09 |
| artificial intelligence | 4.08 |
| Ascorbic acid | 1.31 |
| asexual reproduction | 2.08 |
| Assembly | 1.23, 6.21 |
| association studies | 3.53 |
| Asteraceae | 1.17 |
| ATI | 2.15 |
| AxiomTM60K almond SNP array | 6.04 |
| B | |
| barley | 1.01, 3.05, 3.47, 4.01 |
| Barley | 3.55, 3.56, 4.03 |
| BARLEY | 3.10 |
| Benchmark | 4.09 |
| berry softening | 3.06 |
| bioactive compounds | 1.13, 3.62 |
| bioactives | 2.20 |
| Bioactivity | 1.19 |
| Biochemical Markers | 8.39 |
| Bioclimatic indicators association | 8.27 |
| biocontrol | 4.10 |
| biodiversity | 2.10, 3.41, 5.10, 8.43 |
| Biodiversity | 8.02, 8.14 |
| Biofertilizers | 8.31 |
| biofortification | 3.17 |
| Bioinformatics | 1.11, 1.23, 8.41 |
| biological processes | 3.23 |
| Biomarkers | 3.57 |
| biomass partitioning | 2.17 |
| biostimulant | 1.22 |
| Biostimulant | 3.34 |
| biotic stress | 3.52, 6.04, 6.11 |
| Biotic stress | 3.59 |
| Blast disease | 2.19 |
| Bleaching | 8.42 |
| Blockchain technology | 8.07 |
| Blumeria graminis f.sp. tritici | 3.59 |
| Borlotti bean | 3.20 |
| Brassica rapa subsp. sylvestris | 2.07 |
| Brassinosteroid | 7.05 |
| Bread Wheat | 1.11 |
| Breeding | 1.18, 2.06, 2.19, 4.02, 5.05 |
| breeding | 1.16, 5.14 |
| brewing | 8.09 |
| BSA-Seq | 6.07 |
| Budbreak | 6.15, 6.26 |
| bulk segregant analysis | 6.08 |
| BVOC | 3.40 |
| C | |
| calla lily | 8.16 |
| callus induction | 6.20 |
| cannabinoid yield | 2.17 |
| Cannabis sativa | 2.05, 2.17 |
| Cannabis sativa L. | 8.05 |
| Capsicum annuum | 5.11 |
| carotenoids | 8.32 |
| Carotenoids | 8.42 |
| carotenoids content | 3.19 |
| Castanea sativa Mill. | 8.15 |
| Castanea spp. | 6.07 |
| Castor bean | 2.06 |
| Cell culture | 3.39 |
| Cell Penetrating Peptides (CPPs) | 7.10 |
| cell wall integrity | 6.33 |
| cereals | 4.08 |
| Characterization and Valorization | 8.02 |
| chemovars | 2.02 |
| chestnut | 6.27 |
| Chestnut | 6.07, 8.15 |
| Chickpea | 1.03 |
| Chicory | 1.17, 2.09 |
| chromosome engineering | 1.30, 3.54 |
| Cichorium endivia | 1.05 |
| Cisgenesis | 6.02 |
| Citrus | 3.32 |
| citrus disease | 8.30 |
| Citrus sinensis | 1.34, 8.39 |
| climate adaptation | 3.04 |
| Climate change | 2.03, 4.04, 6.15 |
| climate change | 3.05, 3.48 |
| climate changes | 6.08 |
| climate resilience | 3.19 |
| Climate resilience | 1.05, 2.16 |
| Climate-Resilient Agriculture | 6.32 |
| Climatic changes | 6.03 |
| Coexpression | 6.30 |
| coffee breeding | 2.18 |
| Cold hardiness | 6.26 |
| Common bean | 3.29, 5.13 |
| Common bean landraces | 3.37 |
| Compacted soil | 3.47 |
| Comparative genomics | 1.15 |
| complex trait | 4.06 |
| Conservation | 8.20 |
| Conservation strategies | 8.04 |
| conservative agriculture | 8.11 |
| core collection | 8.28 |
| Corylus avellana L. | 1.29 |
| CpG island methylation | 8.05 |
| CREs | 7.11 |
| CRISPR-based tools | 7.10 |
| CRISPR-Cas9 | 7.11, 7.13, 7.14 |
| CRISPR-cas9 | 6.34 |
| CRISPR-Cas9 gene editing | 3.02, 8.17 |
| CRISPR/Cas | 7.03 |
| CRISPR/Cas9 | 5.05, 6.22, 7.04, 7.06, 7.07, 7.09, 7.15, 7.17 |
| Croatian germplasm | 8.02 |
| Crop resilience | 3.03 |
| Crop Water Stress | 8.39 |
| cucumber melon | 5.02 |
| Cucurbita maxima | 3.19 |
| Cucurbita spp. | 5.15 |
| cuticle | 4.05 |
| Cytokinin Response Factors | 3.22 |
| Cytokinins | 3.22 |
| D | |
| Data Analysis | 8.41 |
| ddPCR | 8.33 |
| ddRAD | 8.14 |
| ddRAD-seq | 1.13 |
| ddRADseq | 1.18 |
| De-novo | 1.32 |
| DEGs | 6.05, 8.35 |
| Detached Leaf Assays | 3.59 |
| development | 3.22 |
| Differentially expressed genes | 1.09 |
| Differentially Methylated Genes (DMGs) | 2.13 |
| differentially methylated regions | 6.13 |
| digestive enzymes | 6.12 |
| Digital Food Product Passport | 8.07 |
| Digital PCR-Based Gene Expression | 8.32 |
| Digitalization | 4.12 |
| Diplotaxis tenuifolia | 1.02 |
| Ditaxis heterantha | 1.19 |
| divergent loci | 6.16 |
| diversity | 5.02 |
| Diversity hotspots | 8.01 |
| DNA fingerprinting | 8.07 |
| DNA methylation | 2.13 |
| DNA profiles | 8.23 |
| DNA-free | 7.19 |
| DNA-free genome editing | 7.12 |
| Domestication | 3.09, 6.21 |
| Dormancy | 6.15, 6.26 |
| drought | 3.08, 3.21, 4.10 |
| Drought | 3.32, 3.39, 4.03 |
| Drought resilience | 3.31 |
| drought resistance | 4.05 |
| drought stress | 1.26, 2.19, 3.18, 3.46, 3.48, 3.63, 4.07, 5.03 |
| Drought stress | 1.03, 3.30, 3.38, 3.57, 3.60 |
| Drought Stress | 8.39 |
| drought tolerance | 3.02, 3.18 |
| Drought tolerance | 3.37, 3.42, 3.44, 3.65 |
| duckweed | 8.21 |
| Durum wheat | 2.15, 3.64, 4.04, 7.05, 7.17, 8.42 |
| durum wheat | 1.33, 3.46, 8.11, 8.43 |
| Durum Wheat | 1.06, 2.11, 2.16, 4.11 |
| DURUM WHEAT | 8.35 |
| Durum Wheat Protoplasts | 7.13 |
| E | |
| East Africa | 3.07 |
| editing | 5.14 |
| eggplant | 3.50, 8.36 |
| Eggplant | 3.26, 5.03 |
| Einkorn wheat | 3.59 |
| elderberry | 6.10 |
| Electric field | 7.07 |
| elicitation | 2.05, 8.12 |
| EMS mutagenesis | 3.11 |
| Endive | 1.17 |
| endophytes | 8.09 |
| Environmental stress | 3.45 |
| Epigenetic regulation | 3.57 |
| epigenetics | 1.27 |
| Epigenomics | 6.06 |
| Eragrostis curvula | 1.09, 1.15 |
| ERFs | 3.45 |
| EST-SSR | 8.22 |
| ETE | 1.11 |
| EVOO | 8.13 |
| EVOO Traceability | 8.07 |
| Exaptation | 1.11 |
| Expressivity | 8.40 |
| Extracellular vesicles | 3.52 |
| F | |
| faba bean | 3.14 |
| female gametophyte | 2.09 |
| Fennel | 1.14 |
| Fertilization strategies | 5.09 |
| field phenomics | 2.18 |
| fig tree | 6.13 |
| Filippo Cea | 6.14 |
| fine roots | 3.47 |
| Firmness | 8.26 |
| flavonoid biosynthesis | 6.36 |
| flavonoids | 2.02, 8.32 |
| Flower morphology | 1.04 |
| Flowering time | 1.35, 3.11 |
| Flowering TIme | 7.18 |
| food innovation | 8.25 |
| Forest conservation management strategies | 8.15 |
| Friariello | 3.36 |
| fruit crops | 6.20, 8.24 |
| Fruit development | 6.24 |
| Fruit ripening | 6.06 |
| Fruit tree breeding | 6.25 |
| FruitTree | 4.12 |
| fumonisins | 7.02 |
| functional annotation | 1.29 |
| functional classification | 6.10 |
| Functional genomics | 8.19 |
| Functional physiological phenotyping | 4.02 |
| fungi strains | 8.30 |
| Fusarium | 8.29 |
| Fusarium ear rot | 7.02 |
| G | |
| Gas exchange | 6.34 |
| Gaspè Flint 1.1.1 | 7.18 |
| GBLUP | 4.06 |
| GBS (Genotyping by sequencing) | 3.20 |
| gene editing | 3.17, 6.27 |
| Gene editing | 7.17 |
| Gene Editing | 6.02 |
| gene editing and overexpression | 6.33 |
| Gene expression | 3.30, 6.24, 6.26 |
| gene expression | 3.35, 3.48, 8.03, 8.12 |
| Gene expression atlas | 3.31 |
| gene isolation | 6.10 |
| Gene Knockout | 7.14 |
| gene ontology | 6.05 |
| Gene Regulation | 7.11 |
| Gene Regulatory Network | 3.60 |
| gene sequence | 6.10 |
| Gene silencing | 8.38 |
| Genetic characterization | 6.21 |
| genetic distance | 1.25 |
| genetic diversity | 2.10, 3.20, 4.07, 5.08 |
| Genetic diversity | 1.17, 3.65, 8.02, 8.04, 8.06, 8.10, 8.15 |
| Genetic Diversity | 8.22 |
| genetic improvement | 2.17, 3.58, 6.20 |
| Genetic Map | 6.07 |
| genetic relationship | 8.16, 8.23 |
| Genetic Resistance | 7.14 |
| genetic resources | 8.25 |
| Genetic Resources | 5.12 |
| genetic transformation | 6.09, 6.12 |
| genetic variations | 1.34 |
| Genetics | 5.05 |
| genetics | 5.14 |
| Genome annotation | 1.35 |
| Genome assembly | 1.14 |
| genome assembly | 1.34 |
| GENOME ASSEMBLY | 1.07 |
| genome editing | 5.03 |
| Genome editing | 1.31, 2.15, 7.03, 7.05, 7.06, 7.08, 7.10, 7.13, 7.15, 7.18 |
| genome sequencig | 6.08 |
| genome sequencing | 3.53, 5.08 |
| Genome sequencing | 1.06 |
| Genome wide association mapping (GWAS) | 3.65 |
| genome-wide association mapping | 8.11 |
| Genome-Wide Association Study | 1.04 |
| Genome-wide association study (GWAS) | 6.23 |
| genome-wide expression | 3.51 |
| genomic | 1.13 |
| Genomic Environment Association Analysis | 8.27 |
| genomic estimated breeding values | 4.14 |
| genomic model | 1.16 |
| Genomic Prediction | 2.03 |
| Genomic prediction | 4.04 |
| Genomic variability | 8.37 |
| genomics | 1.01 |
| Genomics | 1.12, 6.06 |
| genotype by environment interaction | 8.11 |
| Genotype-Environment Association | 3.07 |
| genotypes | 3.46 |
| Genotyping | 1.18 |
| genotyping | 2.12, 3.21, 3.53, 6.29 |
| genotyping by sequencing | 6.16 |
| Germanium exposure | 5.09 |
| germplasm bank | 3.20 |
| Germplasm characterization | 2.06 |
| Germplasm Collection | 8.22 |
| Germplasm repository | 8.21 |
| GFP | 6.09 |
| Gibberellins (GA) | 2.07 |
| Glutahione S-transferase | 3.43 |
| Glutathione | 3.45 |
| GMO quantification | 8.33 |
| Grafting | 2.13 |
| grain size | 3.10 |
| Grain size/Shape | 4.11 |
| Grain Width and Weight 2 (GW2) | 2.11 |
| grain yield | 3.49, 4.14 |
| Granny Smith | 6.35 |
| Grapevine | 1.10, 3.30, 6.02, 6.15, 6.24, 6.26, 6.30, 7.07, 7.12, 7.15 |
| grapevine | 3.48, 5.10, 6.18, 6.31 |
| Grapevine genomics | 3.42 |
| Grapevine germplasm | 6.23 |
| Gravitropism | 3.55 |
| GRF | 3.10 |
| group testing | 8.33 |
| GSTs | 3.45 |
| GWAS | 1.14, 1.20, 1.36, 3.08, 3.09, 3.21, 3.47, 3.64, 4.03, 4.11, 4.13, 5.04, 5.11, 6.25, 8.26, 8.28 |
| H | |
| Haplotype assembly | 6.14 |
| Haplotype GWAS | 2.16 |
| Haplotype-assisted Breeding | 1.36 |
| haplotype-resolved methylome | 6.13 |
| heat stress | 3.48, 3.51 |
| Heat Stress | 2.16 |
| heavy metals | 1.08 |
| Helianthus annuus | 3.57 |
| hemp | 2.20 |
| heterosis | 1.25 |
| High Amylose | 2.11 |
| High-resolution LC-MS | 8.32 |
| high-throughput phenotyping | 4.01, 4.07, 4.14 |
| High-throughput phenotyping | 4.10, 4.13 |
| histone acetylation | 3.35 |
| histone modification | 3.62 |
| histone modifications | 1.26 |
| hologenome | 3.50 |
| hops | 8.09 |
| Hordeum vulgare | 2.04, 3.51, 4.06, 5.04, 8.17 |
| hormonal quantification | 1.28 |
| hormone regulation | 3.06 |
| hormones | 1.27, 6.18 |
| Hub genes | 3.60 |
| Hybridization | 8.21 |
| I | |
| in vitro culture | 6.22 |
| Induced polyploidy | 8.34 |
| Induced resistance | 1.10 |
| industrial hemp | 2.12 |
| interkingdom communication | 3.52 |
| intra-varietal diversity | 6.16 |
| Intra-varietal genomic variation | 6.14 |
| Italian cultivation | 8.09 |
| Italian maize inbred lines | 3.65 |
| Italian varieties | 2.12 |
| J | |
| jasmonic acid | 3.15 |
| K | |
| KASP® | 1.36 |
| Keracyanin | 2.02 |
| kernel | 3.41 |
| key genes | 3.23 |
| L | |
| Lactuca sativa | 1.31 |
| landraces | 3.04, 3.20, 3.24 |
| landscape genomics | 3.04 |
| large genomes | 3.53 |
| LATERAL ORGAN BOUNDARIES DOMAIN | 6.24 |
| leaf architecture | 3.56 |
| leaf curl | 8.28 |
| Leaf erectness | 8.17 |
| Leaf transcriptome | 1.09 |
| Learning-based predictions | 3.60 |
| Lectins | 8.08 |
| legume crops | 4.07 |
| lemon Protected Geographical Indication | 8.30 |
| Lettuce | 3.22, 7.03 |
| Leucocarpa | 6.36 |
| light | 3.63 |
| lignans | 2.14 |
| linkage map | 6.17 |
| Linkage mapping | 2.03 |
| lipidome | 3.15 |
| lipofectamine/PEG-transfection | 6.22 |
| lipoxygenase | 3.15 |
| lncRNAs | 8.36 |
| Local adaptation | 8.27 |
| Local traditional products | 3.36 |
| Local varieties | 8.04 |
| local varieties | 8.16 |
| lodging | 4.06, 5.04 |
| long-read sequencing | 3.51 |
| long-reads sequencing | 1.29 |
| Low-toxicity accessions | 2.06 |
| Lycopene | 6.29 |
| M | |
| MABC | 2.19 |
| Machine learning | 4.04 |
| MAGIC | 4.05 |
| MAGIC population | 1.24, 2.10, 8.43 |
| Magnaporthe oryzae pathotype Triticum | 3.59 |
| maize | 3.24 |
| male sterility | 7.04 |
| Malus × domestica | 6.32 |
| mapping-by sequencing | 3.56 |
| marker free | 7.16 |
| Marker-Assisted Selection | 6.04 |
| markers | 5.14 |
| MCSeEd | 5.07 |
| Medicago sativa | 3.25, 8.33 |
| meristem development | 3.40 |
| meristem differentiation | 3.40 |
| meta-analysis | 3.23 |
| metabarcoding | 8.09 |
| metabolic profiling | 8.25 |
| metabolite profiling | 1.13 |
| metabolomics | 1.08, 1.27, 6.35, 6.36 |
| Metabolomics | 1.19, 8.19, 8.34 |
| Metagenomics | 8.31 |
| metagenomics | 1.08 |
| methyl jasmonate | 2.05 |
| MHB | 4.10 |
| microsatellite | 5.10 |
| microsatellite markers | 6.32 |
| MicroTom | 3.33 |
| mid-parent heterosis | 1.25 |
| milk thistle | 2.14 |
| Minimum Spanning Network | 6.29 |
| miRNA | 3.39 |
| model training | 4.06 |
| Molecular Biology | 7.11 |
| molecular breeding | 3.26, 3.28 |
| molecular marker | 3.43 |
| Molecular markers | 3.36 |
| molecular markers | 8.13 |
| Molecular Markers | 5.12 |
| molecular traceability | 8.13 |
| Monodehydroascorbate reductase (MDHAR) | 1.31 |
| Morpho-agronomical analysis | 3.29 |
| Morpho-physiological traits | 3.38 |
| mountain | 3.24 |
| MTAs | 1.20 |
| MULTI-OMICS | 1.07 |
| Multimodal llm | 4.09 |
| Multiomics | 1.03, 8.41 |
| multiomics | 3.52 |
| Mushrooms | 8.29 |
| mutagenesis | 5.13 |
| Mutagenesis | 8.19 |
| Mutagenic agents | 8.34 |
| mutants | 2.04 |
| MutMap | 3.11 |
| MYB60 | 3.18 |
| MYB80 | 7.04 |
| N | |
| Nanotechnology | 7.10 |
| natural variations | 6.29 |
| NB-LRR proteins | 5.15 |
| Neotetraploid | 1.12 |
| Nested Association Mapping | 1.21 |
| network | 8.36 |
| Network visualization | 6.30 |
| new crop | 8.21 |
| New Genomics Techniques (NGTs) | 6.22 |
| New natural sites | 8.01 |
| NGS | 1.17, 6.03 |
| NGT plants | 7.12 |
| NGTs | 6.11 |
| nitrogen availability | 3.50 |
| non-dormant mutants | 6.08 |
| NUE | 3.50 |
| Nutritional profiling | 3.37 |
| nutritional quality | 3.41, 3.49 |
| Nutritional Quality | 2.11 |
| O | |
| Olea europaea | 1.20, 6.12, 6.22, 8.03, 8.12 |
| Olea europaea L. | 3.21, 3.23, 6.09, 8.02 |
| Olea europea | 6.17 |
| Oleander | 8.34 |
| olive | 6.36, 8.13 |
| Olive | 3.61 |
| olive domestication | 6.19 |
| Olive tree | 6.03 |
| Omics | 1.23 |
| omics | 1.22 |
| ONT | 1.32 |
| OQDS | 6.05 |
| Organic farming | 3.34 |
| Oryza rufipogon | 5.08 |
| osmotic stress | 3.62 |
| Oxford Nanopore Technology | 1.21, 1.24, 8.30 |
| oxidative DNA damage | 8.05 |
| P | |
| Paclobutrazol (PAC/PBZ) | 2.07 |
| Pakistan | 6.19 |
| pan-genome | 1.33 |
| Pan-genome | 1.15, 1.21, 1.24 |
| pan-transcriptome | 1.33 |
| pan-transcriptomics | 1.25 |
| pangenome | 1.01, 1.34 |
| Parthenocarpy | 8.40 |
| Paspalum simplex | 2.08 |
| Pathogen resistance | 6.02 |
| PAV | 1.32, 6.29 |
| pea (Pisum sativum L.) | 1.28 |
| peach | 8.28 |
| pedigree method | 3.19 |
| Pepper | 3.28, 5.11, 5.12 |
| Perennialism trait | 3.03 |
| PFAS contamination | 8.05 |
| PGPR | 3.13 |
| Phaseolus vulgaris | 8.19 |
| Phaseolus vulgaris L. | 3.37 |
| Phaseolus vulgaris landtaces | 3.38 |
| phenolic compounds | 6.12 |
| phenolics | 2.14 |
| Phenology | 6.31 |
| phenomic estimated breeding values | 4.14 |
| Phenotypic and genetic characterization | 8.20 |
| phenotypic traits | 1.25 |
| phenotyping | 2.10, 3.21, 8.03 |
| Phenotyping | 3.12, 4.12, 6.23 |
| photoreceptors | 8.03 |
| Photosynthesis | 1.24 |
| photosynthesis | 4.01, 4.05 |
| Phylogeography | 8.01, 8.10 |
| Physiological response | 3.30 |
| phytic acid | 3.17 |
| phytoextraction | 8.05 |
| Phytoextracts | 3.34 |
| phytopathogenic fungi | 8.08 |
| Phytophthora | 5.03 |
| Phytophthora cinnamomi | 6.27 |
| Pistacia | 8.22 |
| Pisum sativum | 3.53 |
| plant architecture | 3.40 |
| plant biomass | 2.02 |
| plant breeding | 2.08 |
| Plant cells | 7.10 |
| plant defence | 8.12 |
| plant development | 3.35 |
| plant genetic resources | 1.01, 3.04 |
| Plant Genetics | 8.41 |
| plant growth | 3.27 |
| Plant Growth Promoting Bacteria (PGPB) | 8.31 |
| Plant growth-promoting bacteria (PGPB) | 3.31 |
| Plant height | 4.13 |
| plant immunity | 6.33 |
| plant regeneration | 6.20 |
| PLANT REPRODUCTION | 1.07 |
| Plant stress memory | 1.03 |
| Plant stress mitigation | 3.34 |
| plant-associated microbial communities | 3.50 |
| Plant-fungi interactions | 3.52 |
| plant-growth promoting bacteria | 3.41 |
| Plasmopara viticola | 1.10 |
| plastoglobules | 8.18 |
| Plenodomus trachephilus | 1.32 |
| Ploidy | 1.09 |
| PME | 6.33 |
| PNRSV | 6.28 |
| pollen germination | 2.09 |
| Polyploid | 1.12 |
| Polyploidy | 3.25 |
| POLYSTYRENE NANOPLASTIC | 8.35 |
| Poplar | 2.03 |
| Population genomics | 1.14 |
| Post-harvest | 8.26 |
| Potato | 3.39 |
| potato | 4.01 |
| Powdery mildew | 5.11 |
| Powdery mildew resistance | 6.23 |
| PPV | 6.28 |
| Pre-breeding | 5.12 |
| pre-breeding | 5.04 |
| prediction accuracy | 1.16 |
| Prediction accuracy | 4.13 |
| Primary Metabolome | 3.16 |
| Priming | 3.32 |
| Principal Component Analysis (PCA) | 5.09 |
| private alleles | 5.02, 8.13 |
| Private SNPs | 5.12 |
| protective osmolites | 3.54 |
| protein content | 4.14 |
| proteomics | 6.35 |
| Protoplast | 7.07 |
| protoplast isolation | 6.12 |
| Protoplast isolation | 7.19 |
| protoplasts | 6.11 |
| Protoplasts | 7.04 |
| Protoplasts Validation Test | 7.14 |
| Prunus armeniaca L. | 6.28 |
| Prunus dulcis | 6.04 |
| Prunus dulcis resequencing | 6.14 |
| Prunus persica | 6.06 |
| Prunus persica L. Batsch | 6.25 |
| pumpkins | 3.19 |
| Pyrus communis | 6.32 |
| Q | |
| qPCR | 8.33 |
| QTL | 1.36, 2.16, 3.09, 8.43 |
| QTL analysis | 6.31 |
| QTL mapping | 1.02, 1.13, 3.16, 5.04 |
| QTLome | 1.06 |
| quantitative genetics | 4.01 |
| Quantitative trait loci | 3.44 |
| R | |
| R-genes | 5.15 |
| ramosus mutants | 1.28 |
| red and blue light | 8.03 |
| reduced irrigation | 1.22 |
| reference genome | 2.14 |
| Regeneration | 7.19 |
| regulatory genes | 8.24 |
| regulatory mechanisms | 3.58 |
| remote sensing | 2.18 |
| Reproductive Barriers | 7.19 |
| reproductive biology | 2.08 |
| Reproductive Development | 8.24 |
| Resilience | 3.16 |
| resilience | 4.08, 8.06 |
| RESILIENCE | 8.35 |
| resistance genes | 6.17 |
| Rhizosphere environment | 3.03 |
| Rice | 2.19, 5.09, 7.06 |
| rice | 3.08, 3.41 |
| Rice blast | 5.05 |
| Ricin | 2.06 |
| ripening | 6.18 |
| RNA guides validation | 7.13 |
| RNA sequencing | 3.55 |
| RNA-seq | 1.05, 2.09, 3.06, 3.12, 3.32, 3.61, 6.05, 6.33 |
| RNA-Seq | 1.09 |
| RNAi | 6.28 |
| RNP complex | 7.04 |
| Root architecture | 3.09, 3.44, 3.55 |
| root architecture traits | 4.07 |
| root growth | 3.47 |
| Root Growth Angle (RGA) | 1.36 |
| Root metabolomics | 3.03 |
| root traits | 3.46 |
| Rootstock–scion interaction | 2.13 |
| Rosa germplasm | 8.23 |
| RT-qPCR | 6.04, 6.05 |
| Ruby | 7.16 |
| S | |
| S-genes | 6.24, 7.12 |
| S. lycopersicum | 3.45 |
| S. lycopersicum var. cerasiforme | 3.43 |
| saline stress | 3.46 |
| salt | 3.08 |
| salt stress | 3.33 |
| Salt stress | 3.43, 3.58 |
| Salt stress molecular markers | 3.13 |
| salt tolerance | 3.35 |
| Sardinian Germplasm Bank | 8.20 |
| scented roses | 8.23 |
| secondary metabolites | 2.05, 8.12 |
| seed development | 3.51 |
| seed germination | 3.63 |
| Seed quality | 3.37 |
| Seedlessness | 7.15 |
| self-incompatibility | 2.09 |
| sensory evaluation | 8.25 |
| sequencing | 1.30 |
| Sequencing | 1.04, 3.12 |
| serotonin | 3.33 |
| Sesquiterpene Lactones | 1.05 |
| shoot branching | 2.04 |
| Shotgun sequencing | 8.37 |
| SIGS | 6.27 |
| silymarin | 2.14 |
| Simple Sequence Repeats (SSR) | 8.15 |
| Single Primer Enrichment Technology | 4.02 |
| single-nucleotide polymorphism (SNP) markers | 6.32 |
| slow ripening | 3.06 |
| Smallholder Farming | 3.07 |
| snake melon | 5.02 |
| SNP | 6.17 |
| SNP genotyping | 6.23, 8.04 |
| SNPs | 2.12, 6.03, 8.14, 8.27 |
| SNPs and SVs | 3.42 |
| Softening | 8.26 |
| Soil microbiome | 8.31 |
| Soil-Borne Cereal Mosaic Virus | 7.14 |
| Solanaceae | 3.26, 3.28 |
| Solanum | 8.24 |
| Solanum habrochaites | 3.16 |
| Solanum Lycopersicum | 3.13 |
| Solanum lycopersicum | 1.16, 1.26, 3.33, 7.16, 8.31, 8.32 |
| Solanum lycopersicum L. | 3.12 |
| Solanum lycopersycum L. | 1.04 |
| Solanum nigrum | 8.38 |
| Solanum tuberosum | 3.62 |
| Somaclonal variation | 3.42 |
| somatic embryogenesis | 6.09, 6.11 |
| Sorghum bicolor | 3.07 |
| South Italy | 8.01 |
| Southern refugia | 8.10 |
| SPET | 3.05 |
| Spike fertility | 4.11 |
| SSR | 3.24 |
| SSR markers | 6.19, 8.06 |
| Starch biosynthesis | 7.17 |
| startup | 5.14 |
| Statholits | 3.55 |
| Stem Water Potential | 8.39 |
| stomata | 4.05 |
| Stomata | 4.03 |
| stomata opening | 3.18 |
| stomatal regulation | 3.02 |
| stress | 3.08, 3.22 |
| Stress biomarker | 3.62 |
| stress memory | 1.26 |
| stress mitigation | 1.08 |
| stress tolerance | 3.24 |
| Striga hermonthica | 3.07 |
| strigolactone biosynthesis | 1.28 |
| Structural Variants | 1.24 |
| Structural variation | 6.06 |
| structural variation | 1.01, 1.33 |
| Structural variation induction | 7.09 |
| Style exsertion | 1.04 |
| sugar accumulation | 3.06 |
| Sugar accumulation | 2.13 |
| sugarloaf | 1.22 |
| SULTR | 3.17 |
| Summer truffle | 8.37 |
| supply chain | 4.08 |
| Susceptibility genes | 6.27 |
| sustainable agriculture | 3.17, 3.49 |
| T | |
| T2T genome assembly | 1.02, 1.29 |
| Taphrina deformans | 8.28 |
| Targetd RNA-seq | 3.31 |
| Taxus baccata | 8.14 |
| Teff | 1.21 |
| Temporal QTL mapping | 4.13 |
| TFL1 | 7.16 |
| Tilling | 3.56 |
| TILLING | 2.15, 8.17, 8.19, 8.42 |
| tilling | 2.20 |
| TILLING by sequencing | 5.13 |
| Tobamovirus fructirugosum | 5.07 |
| ToBRFV | 5.07 |
| tomato | 3.02, 3.27, 3.35 |
| Tomato | 3.16, 3.34, 5.07, 7.08, 8.26, 8.40 |
| tomato breeding | 2.10 |
| Tomato hairy roots | 7.13 |
| tomato wild species | 7.09 |
| tracheomycosis | 8.30 |
| training population | 1.16 |
| transcription factor | 2.20, 3.18 |
| transcription factors | 2.02 |
| Transcriptional reprogramming | 1.10 |
| transcriptome | 3.15, 7.02 |
| Transcriptome | 6.15 |
| transcriptome analysis | 1.29 |
| Transcriptome profiling | 8.40 |
| TRANSCRIPTOMIC ANALYSIS | 8.35 |
| Transcriptomic memory | 3.57 |
| transcriptomics | 1.08, 1.26, 1.27, 3.14, 3.26, 3.28, 6.18 |
| Transcriptomics | 1.02, 1.05, 1.19, 1.28, 1.31, 3.25, 6.30, 8.34 |
| Transfection | 7.19 |
| transformation | 7.16 |
| transgenic maize | 3.15 |
| transgenic tobacco | 8.08 |
| Translational biology | 1.02 |
| Transposable Elements | 1.11 |
| transposons | 5.15 |
| trichomes | 2.20 |
| Triticum durum | 1.30 |
| Truffle cultivation | 8.10 |
| tryptamine | 3.33 |
| Tuber aestivum | 8.10 |
| Tuber aestivum Vitt. | 8.37 |
| Tuber magnatum | 8.01 |
| Tubulin-Based Polymorphism | 8.16 |
| Tuscia red | 6.35 |
| V | |
| VEA | 8.08 |
| vertical farming | 2.17 |
| Vgt1 | 7.18 |
| viability | 4.08 |
| VIGE | 7.03 |
| VIGS | 6.28, 8.38 |
| Viral Vector | 7.03 |
| Vision | 4.09 |
| Vitis vinifera | 3.42, 6.11, 6.34, 7.12 |
| Vitis Vinifera | 7.11 |
| Vitis vinifera L. | 6.16 |
| Volatile organic compounds | 1.10 |
| VviINO | 7.15 |
| W | |
| water deficit | 3.27 |
| Water management | 5.09 |
| water stress | 3.14 |
| Water stress | 3.29, 3.61, 6.34 |
| Weed control | 8.38 |
| WGA | 8.08 |
| WGCNA | 2.05, 3.32, 3.61 |
| WGS | 1.20 |
| Wheat | 8.29 |
| wheat | 8.25 |
| wheat genetic resources | 3.49 |
| Whole Genome Duplication | 1.12 |
| Whole genome duplication | 3.25 |
| Whole genome resequencing | 6.07 |
| whole-genome resequencing | 5.02 |
| whole-genome sequencing | 2.12 |
| Wild olive | 6.19 |
| Wild relative | 6.21 |
| wild rice | 5.08 |
| Woolly poplar aphid | 2.03 |
| WRKY125 transcription factor | 7.02 |
| X | |
| Xylella fastidiosa | 1.20, 3.23, 6.17 |
| Y | |
| Yellow rust | 3.64 |
| Yield | 2.11, 3.38 |
| Yield and protein content | 4.04 |
| yield stability | 3.54 |
| Z | |
| Zaxinone | 7.08 |
| Zaxinone synthase | 7.08 |
| Zea mays | 1.35, 3.04 |
| Zea mays L. | 7.02 |
| β | |
| β-Carotene | 8.42 |
| 2 | |
| 2D-PAGE | 3.13 |
| 9 | |
| 90K SNP array | 4.11 |
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