Author response: Vision, challenges and opportunities for a Plant Cell Atlas

Ahmed, Jahed; Alaba, Oluwafemi; Ameen, Gazala; Arora, Vaishali; Arteaga-Vázquez, Mario; Arun, Alok; Bailey‐Serres, Julia; Bartley, Laura E.; Bassel, George W.; Bergmann, Dominique C.; Bertolini, Edoardo; Bhati, Kaushal Kumar; Blanco‐Touriñán, Noel; Briggs, Steven P.; Brumós, Javier; Buer, Benjamin; Burlaocot, Adrien; Cervantes-Pérez, Sergio Alan; Chen, Sixue; Contreras-Moreira, Bruno; Corpas, Francisco J.; Cruz‐Ramírez, Alfredo; Cuevas-Velazquez, Cesar L.; Cuperus, Josh T.; David, Lisa; Folter, Stefan de; Ding, Pingtao; Dwyer, William; Mulenga, Evans; George, Nancy; Handakumbura, Pubudu; Harrison, Maria J.; Haswell, Elizabeth S.; Herath, Venura; Jiao, Yuling; Jinkerson, Robert E.; John, Uwe; Joshi, Sanjay; Joshi, Abhishek; Joubert, Lydia-Marié; Katam, Ramesh; Kaur, Harmanpreet; Kazachkova, Yana; Raju, Sunil Kumar Kenchanmane; Khan, Mather Ali; Khangura, Rajdeep S.; Kumar, Ajay; Kumar, Arun; Kumar, Pankaj; Kumar, Pradeep; Lavania, Dhruv; Lew, Tedrick Thomas Salim; Lewsey, Mathew G.; Lin, Chien‐Yuan; Liu, Dianyi; Liu, Le; Li, Shipeng; Lokdarshi, Ansul; Luong, Ai My; Macaulay, Iain C.; Mahmud, Sakil; Mähönen, Ari Pekka; Malukani, Kamal; Marand, Alexandre P.; Martin, Clay; McWhite, Claire D.; Mehta, Devang; Martin, Miguel; Mortimer, Jenny C.; Nikolov, Lachezar A.; Nobori, Tatsuya; Nolan, Trevor M.; Ogden, Aaron J.; Otegui, Marisa S.; Ott, Mark‐Christoph; Palma, José M.; Paul, Puneet; Rehman, Atique ur; Romera-Branchat, Maida; Romero, Luís C.; Roth, Ronelle; Sah, Saroj Kumar; Shahan, Rachel; Solanki, Shyam; Song, Baoxing; Sozzani, Rosangela; Stacey, Gary; Stepanova, Anna N.; Taylor, Nicolas L.; Tello-Ruiz, Marcela K.; Tran, Tuan Minh; Tripathi, Rajiv Kumar; Vadde, Batthula Vijaya Lakshmi; Varga, Tamás; Vidović, Marija; Walley, Justin W.; Wang, Zhiyong; Weizbauer, Renate; Whelan, James; Wijeratne, Asela J.; Xiang, T.; Xu, Shou-Ling; Yadegari, Ramin; Yu, Houlin; Yuan, Huajun; Zanini, Fabio; Zhao, Feng; Zhu, Jie; Zhuang, Xiaohong; Jha, Suryatapa Ghosh; Borowsky, Alexander T.; Cole, Benjamin; Fahlgren, Noah; Farmer, Andrew; Huang, Shao‐shan Carol; Karia, Purva; Libault, Marc; Provart, Nicholas J.; Rice, Sarah E.; Saura-Sánchez, Maite; Agarwal, Pinky; Ahkami, Amir; Anderton, Christopher; Brophy, Jennifer A N; Denolf, Peter; Costanzo, Luigi Franklin Di; Expósito-Alonso, Moisés; Giacomello, Stefania; Gómez-Cano, Fabio; Kaufmann, Kerstin; Ko, Dae Kwan; Kumar, Sagar; Malkovskiy, Andrey V.; Nakayama, Naomi; Obata, Toshihiro; Pálfalvi, Gergo; Quezada-Rodríguez, Elsa H; Singh, Rajveer; Uhrig, R. Glen; Waese, Jamie; Wijk, Klaas J. van; Wright, R Clay; Ehrhardt, David; Birnbaum, Kenneth D.; Rhee, Seung Y.

doi:10.7554/elife.66877.sa2

2021

DOI: 10.7554/elife.66877.sa2

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Author response: Vision, challenges and opportunities for a Plant Cell Atlas

Jahed Ahmed¹,

Oluwafemi Alaba²,

Gazala Ameen³

et al.

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2023

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Mapping theArabidopsis thalianaproteome in PeptideAtlas and the nature of the unobserved (dark) proteome; strategies towards a complete proteome

van Wijk,

Leppert,

Sun

et al. 2023

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This study describes a new release of the Arabidopsis thaliana PeptideAtlas proteomics resource providing protein sequence coverage, matched mass spectrometry (MS) spectra, selected PTMs, and metadata. 70 million MS/MS spectra were matched to the Araport11 annotation, identifying ~0.6 million unique peptides and 18267 proteins at the highest confidence level and 3396 lower confidence proteins, together representing 78.6% of the predicted proteome. Additional identified proteins not predicted in Araport11 should be considered for building the next Arabidopsis genome annotation. This release identified 5198 phosphorylated proteins, 668 ubiquitinated proteins, 3050 N-terminally acetylated proteins and 864 lysine-acetylated proteins and mapped their PTM sites. MS support was lacking for 21.4% (5896 proteins) of the predicted Araport11 proteome - the 'dark' proteome. This dark proteome is highly enriched for certain (e.g. CLE, CEP, IDA, PSY) but not other (e.g. THIONIN, CAP,) signaling peptides families, E3 ligases, TFs, and other proteins with unfavorable physicochemical properties. A machine learning model trained on RNA expression data and protein properties predicts the probability for proteins to be detected. The model aids in discovery of proteins with short-half life (e.g. SIG1,3, ERF-VII TFs) and completing the proteome. PeptideAtlas is linked to TAIR, JBrowse, PPDB, SUBA, UniProtKB and Plant PTM Viewer.

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Mapping theArabidopsis thalianaproteome in PeptideAtlas and the nature of the unobserved (dark) proteome; strategies towards a complete proteome

van Wijk,

Leppert,

Sun

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

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Author response: Vision, challenges and opportunities for a Plant Cell Atlas

Cited by 1 publication

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Mapping theArabidopsis thalianaproteome in PeptideAtlas and the nature of the unobserved (dark) proteome; strategies towards a complete proteome

Mapping theArabidopsis thalianaproteome in PeptideAtlas and the nature of the unobserved (dark) proteome; strategies towards a complete proteome

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