日期:
2020 年 — 2026 年
2020
2021
2022
2023
2024
2025
2026
影响因子:

Pho-Tip: One-Pot Dephosphorylation for Rapid and Sensitive Analysis of DIA Phosphoproteomics Data

Pho-Tip:用于快速灵敏分析DIA磷酸化蛋白质组学数据的单锅脱磷酸化方法

Faisst, Katharina D; Lau, Kate; Sinn, Ludwig R; Szyrwiel, Lukasz; Demichev, Vadim

Performance Characteristics of Zeno Trap Scanning DIA for Sensitive and Quantitative Proteomics at High Throughput

Zeno Trap 扫描 DIA 在高通量灵敏定量蛋白质组学中的性能特征

Sinn, Ludwig R; Wang, Ziyue; Alvarez, Claudia P; Chelur, Anjali; Batruch, Ihor; Pribil, Patrick; Ludwig, Daniela; Tate, Stephen; Castro-Perez, Jose; Messner, Christoph B; Demichev, Vadim; Ralser, Markus

Unbiased mapping of cereblon neosubstrate landscape by high-throughput proteomics.

利用高通量蛋白质组学对 cereblon 新底物景观进行无偏映射

Steger Martin, Nishiguchi Gisele, Wu Qiong, Schwalb Bjoern, Shashikadze Bachuki, McGowan Kevin, Actis Marisa, Aggarwal Anup, Shi Zhe, Price Jeanine, Mayasundari Anand, Yang Lei, Bednarz Anastasia H, Machata Sophie, Graef Tobias, Bartoschek Denis, Demichev Vadim, Ohmayer Uli, Yang Jun, Daub Henrik, Rankovic Zoran

Data-Independent Immunopeptidomics Discovery of Low-Abundant Bacterial Epitopes

无需依赖数据的免疫肽组学方法发现低丰度细菌表位

Willems, Patrick; Staes, An; Miret-Casals, Laia; Demichev, Vadim; Devos, Simon; Impens, Francis

SynchroSep-MS: Parallel LC Separations for Multiplexed Proteomics.

SynchroSep-MS:用于多重蛋白质组学的平行液相色谱分离

Lancaster Noah M, Chen Li-Yu, Zhao Bingnan, Anderson Benton J, Probasco Mitchell D, Demichev Vadim, Polasky Daniel A, Nesvizhskii Alexey I, Overmyer Katherine A, Quarmby Scott T, Coon Joshua J

Slice-PASEF: Maximising Ion Utilisation in LC-MS Proteomics

Slice-PASEF:最大化液相色谱-质谱蛋白质组学中的离子利用率

Sinn, Ludwig R; Szyrwiel, Lukasz; Grossmann, Justus; Lau, Kate; Faisst, Katharina; Qin, Di; Mutschler, Florian; Khoury, Luke; Leduc, Andrew; Ralser, Markus; Coscia, Fabian; Selbach, Matthias; Slavov, Nikolai; Nagaraj, Nagarjuna; Steger, Martin; Demichev, Vadim

quantms: a cloud-based pipeline for quantitative proteomics enables the reanalysis of public proteomics data

quantms:一个基于云的定量蛋白质组学流程,支持对公共蛋白质组学数据进行重新分析。

Dai, Chengxin; Pfeuffer, Julianus; Wang, Hong; Zheng, Ping; Käll, Lukas; Sachsenberg, Timo; Demichev, Vadim; Bai, Mingze; Kohlbacher, Oliver; Perez-Riverol, Yasset

Neat plasma proteomics: getting the best out of the worst

纯净血浆蛋白质组学:从最差的情况中获得最佳结果

Metatla, Ines; Roger, Kevin; Chhuon, Cerina; Ceccacci, Sara; Chapelle, Manuel; Pierre-Olivier Schmit; Demichev, Vadim; Guerrera, Ida Chiara

Initial recommendations for performing, benchmarking and reporting single-cell proteomics experiments

单细胞蛋白质组学实验的执行、基准测试和报告的初步建议

Gatto, Laurent; Aebersold, Ruedi; Cox, Juergen; Demichev, Vadim; Derks, Jason; Emmott, Edward; Franks, Alexander M; Ivanov, Alexander R; Kelly, Ryan T; Khoury, Luke; Leduc, Andrew; MacCoss, Michael J; Nemes, Peter; Perlman, David H; Petelski, Aleksandra A; Rose, Christopher M; Schoof, Erwin M; Van Eyk, Jennifer; Vanderaa, Christophe; Yates, John R 3rd; Slavov, Nikolai

MSBooster: improving peptide identification rates using deep learning-based features

MSBooster:利用基于深度学习的特征提高肽段识别率

Yang, Kevin L; Yu, Fengchao; Teo, Guo Ci; Li, Kai; Demichev, Vadim; Ralser, Markus; Nesvizhskii, Alexey I