| Range |
≤65 %
|
| Organism |
Eukaryotes |
| Reference |
Vlastaridis P et al., The Pivotal Role of Protein Phosphorylation in the Control of Yeast Central Metabolism. G3 (Bethesda). 2017 Apr 3 7(4):1239-1249. doi: 10.1534/g3.116.037218. p.1240 left column 3rd paragraphPubMed ID28250014
|
| Primary Source |
Landry CR, Levy ED, Michnick SW. Weak functional constraints on phosphoproteomes. Trends Genet. 2009 May25(5):193-7. doi: 10.1016/j.tig.2009.03.003. AND Landry CR, Freschi L, Zarin T, Moses AM. Turnover of protein phosphorylation evolving under stabilizing selection. Front Genet. 2014 Jul 23 5: 245. doi: 10.3389/fgene.2014.00245.PubMed ID19349092, 25101120
|
| Comments |
P.1240 left column 3rd paragraph: "The advent of HTP [high throughput] phosphoproteomic technologies in the last decade has revolutionized the field, since hundreds or even thousands of p-sites may be identified within a single HTP experiment. Nevertheless, serious concerns have been raised about the quality of these p-site identifications in terms of both technical and biological noise (Lienhard 2008) indeed, it has been suggested that up to 65% of these p-sites may be nonfunctional (primary sources). In addition, the various phosphoproteomic protocols capture distinct fractions of the total phosphoproteome with moderate overlap among them (Bodenmiller et al. 2007). Hence, any analysis of phosphoproteomic data poses a series of challenges (Lee et al. 2015, Vlastaridis et al. 2016). Thus, before identifying p-sites with potentially significant impact on protein function and organismal phenotype, there is an urgent need to: (i) stringently filter these HTP data and (ii) compile datasets from many and diverse protocols to ameliorate any potential biases (Amoutzias et al. 2012)." |
| Entered by |
Uri M |
| ID |
113381 |