Keyword search (4,163 papers available)

"Localization" Keyword-tagged Publications:

Title Authors PubMed ID
1 Human short-term memory learning based on dynamic glutamate levels and oscillatory activities: concurrent metabolic and electrophysiological studies using event-related functional-MRS and EEG modalities Mohammadi H; Zargaran SJ; Khajehpour H; Adibi I; Rahimiforoushani A; Karimi S; Serej ND; Alam NR; 41171530
PERFORM
2 Deformable detection transformers for domain adaptable ultrasound localization microscopy with robustness to point spread function variations Gharamaleki SK; Helfield B; Rivaz H; 40640235
PHYSICS
3 PARPAL: PARalog Protein Redistribution using Abundance and Localization in Yeast Database Greco BM; Zapata G; Dandage R; Papkov M; Pereira V; Lefebvre F; Bourque G; Parts L; Kuzmin E; 40580499
BIOLOGY
4 Metrics for evaluation of automatic epileptogenic zone localization in intracranial electrophysiology Hrtonova V; Nejedly P; Travnicek V; Cimbalnik J; Matouskova B; Pail M; Peter-Derex L; Grova C; Gotman J; Halamek J; Jurak P; Brazdil M; Klimes P; Frauscher B; 39608298
SOH
5 Otilonium Bromide Exhibits Potent Antifungal Effects by Blocking Ergosterol Plasma Membrane Localization and Triggering Cytotoxic Autophagy in Candida Albicans Zhen C; Wang L; Feng Y; Whiteway M; Hang S; Yu J; Lu H; Jiang Y; 38995235
BIOLOGY
6 The MyLo CRISPR-Cas9 Toolkit: A Markerless Yeast Localization and Overexpression CRISPR-Cas9 Toolkit Bean BDM; Whiteway M; Martin VJJ; 35708612
BIOLOGY
7 Optical Fiber Array Sensor for Force Estimation and Localization in TAVI Procedure: Design, Modeling, Analysis and Validation Bandari N; Dargahi J; Packirisamy M; 34450813
ENCS
8 Fast oscillations >40 Hz localize the epileptogenic zone: An electrical source imaging study using high-density electroencephalography. Avigdor T, Abdallah C, von Ellenrieder N, Hedrich T, Rubino A, Lo Russo G, Bernhardt B, Nobili L, Grova C, Frauscher B 33450578
PERFORM
9 Effects of Independent Component Analysis on Magnetoencephalography Source Localization in Pre-surgical Frontal Lobe Epilepsy Patients Pellegrino G, Xu M, Alkuwaiti A, Porras-Bettancourt M, Abbas G, Lina JM, Grova C, Kobayashi E, 32582009
PERFORM
10 Editorial: RNA Regulation in Development and Disease. Chartrand P, Jaramillo M, Gamberi C 32411184
BIOLOGY
11 Accuracy and spatial properties of distributed magnetic source imaging techniques in the investigation of focal epilepsy patients. Pellegrino G, Hedrich T, Porras-Bettancourt M, Lina JM, Aydin Ü, Hall J, Grova C, Kobayashi E 32386115
PERFORM
12 W361R mutation in GaaR, the regulator of D-galacturonic acid-responsive genes, leads to constitutive production of pectinases in Aspergillus niger. Alazi E, Niu J, Otto SB, Arentshorst M, Pham TTM, Tsang A, Ram AFJ 30298571
CSFG
13 Detection and Magnetic Source Imaging of Fast Oscillations (40-160 Hz) Recorded with Magnetoencephalography in Focal Epilepsy Patients. von Ellenrieder N, Pellegrino G, Hedrich T, Gotman J, Lina JM, Grova C, Kobayashi E 26830767
PERFORM
14 Intracranial EEG potentials estimated from MEG sources: A new approach to correlate MEG and iEEG data in epilepsy. Grova C, Aiguabella M, Zelmann R, Lina JM, Hall JA, Kobayashi E 26931511
PERFORM
15 Source localization of the seizure onset zone from ictal EEG/MEG data. Pellegrino G, Hedrich T, Chowdhury R, Hall JA, Lina JM, Dubeau F, Kobayashi E, Grova C 27059157
PERFORM
16 Clinical yield of magnetoencephalography distributed source imaging in epilepsy: A comparison with equivalent current dipole method. Pellegrino G, Hedrich T, Chowdhury RA, Hall JA, Dubeau F, Lina JM, Kobayashi E, Grova C 29024165
PERFORM
17 Reproducibility of EEG-MEG fusion source analysis of interictal spikes: Relevance in presurgical evaluation of epilepsy. Chowdhury RA, Pellegrino G, Aydin Ü, Lina JM, Dubeau F, Kobayashi E, Grova C 29164737
PERFORM

 

Title:PARPAL: PARalog Protein Redistribution using Abundance and Localization in Yeast Database
Authors:Greco BMZapata GDandage RPapkov MPereira VLefebvre FBourque GParts LKuzmin E
Link:https://pubmed.ncbi.nlm.nih.gov/40580499/
DOI:10.1093/g3journal/jkaf148
Publication:G3 (Bethesda, Md.)
Keywords:Saccharomyces cerevisiaebudding yeastdeep neural networkduplicated geneshigh-content screeningparalogsphenomicsprotein abundanceprotein subcellular localization
PMID:40580499 Category: Date Added:2025-07-01
Dept Affiliation: BIOLOGY
1 Department of Biology, Concordia University, 7141 Sherbrooke St. W., Montreal, QC, H4B 1R6, Canada.
2 Centre for Applied Synthetic Biology, Centre for Structural and Functional Genomics, Concordia University, 7141 Sherbrooke St. W., Montreal, QC, H4B 1R6, Canada.
3 Canadian Centre for Computational Genomics (C3G), McGill University, 1010 Sherbrooke St. W. Suite 1800, Montreal, QC, H3A 2R7, Canada.
4 Victor Phillip Dahdaleh Institute of Genomic Medicine, McGill University, 740 Dr Penfield Ave, Montreal, QC, H3A 0G1, Canada.
5 Institute of Computer Science, University of Tartu, Narva mnt 18, Tartu, 51009, Estonia.
6 Department of Human Genetics, McGill University, 3640 University, Room W 315 D, Montreal, QC, H3A 0C7, Canada.
7 Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SA, UK.
8 Rosalind & Morris Goodman Cancer Institute, McGill University, 1160 Pine Ave W, Montreal, QC, H3A 1A3, Canada.

Description:

Whole-genome duplication (WGD) events are common across various organisms; however, the retention and evolution of WGD paralogs is not fully understood. Quantitative measure of protein redistribution in response to the deletion of their WGD paralog provides insight into sources of gene retention. Here, we describe PARPAL (PARalog Protein Redistribution using Abundance and Localization in Yeast), a web database that houses results of high-content screening and deep learning neural network analysis of the redistribution of 164 proteins reflecting how their subcellular localization and protein abundance change in response to their paralog deletion in the budding yeast, Saccharomyces cerevisiae. We interrogated a total of 82 paralog pairs in two genetic backgrounds for a total of ~3,500 micrographs of ~460,000 cells. For example, Skn7-Hms2 exhibited dependent redistribution and Cue1-Cue4 showed compensatory redistribution response. PARPAL also links to other studies on trigenic interactions, protein-protein interactions and protein abundance. PARPAL is available at https://parpal.c3g-app.sd4h.ca and is a valuable resource for the yeast community interested in understanding the retention and evolution of paralogs and can help researchers to investigate protein dynamics of paralogs in other organisms.





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