Dana Pascovici |
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Macquarie University
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Australian Proteome Analysis Facility (APF)
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21.59
Skills (4)
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275 Questions7971 Followers
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31 Questions4439 Followers
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278 Questions9633 Followers
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1252 Questions43829 Followers
Questions and Answers (3) View all
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Answer added in LC-MS/MS6 How should I treat my iTRAQ data when I have 2 biological replicates and 3 technical replicates?By Chun Hin Law · The University of Hong KongDana Pascovici · Macquarie UniversityJust some concrete points - as the others mentioned, what you actually do of course depends on the experiment design and aim. Regarding the averaging... [more]Just some concrete points - as the others mentioned, what you actually do of course depends on the experiment design and aim. Regarding the averaging, if you are reporting average ratios from replicates you should use the geometric mean for the overall trend. This incidentally corresponds to what the Protein Pilot documentation calls the average in log space; it is explained in some detail for instance in the ProteinPilot Online Help document why that is more appropriate. Regarding missing values, there is always some lack of overlap between runs; you'll never have exactly the same proteins identified even in repeated runs of the same sample. So you can come up with a heuristic that is relevant to your experiment, such as for example use proteins that appear in 2 out of 3 replicates. There is no set rule, you have to decide what is meaningful in your case.Following
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Answer added in Mass Spectrometry6 What is the meaning of Unused score in Protein Pilot software?By Gaurav Bhardwaj · University of GreifswaldDana Pascovici · Macquarie UniversityFrom the protein pilot documentation (Protein Pilot Online Help): ProtScore = -log( 1 - (PercentConfidence/100)) So you can undo that to get: Perce... [more]From the protein pilot documentation (Protein Pilot Online Help): ProtScore = -log( 1 - (PercentConfidence/100)) So you can undo that to get: PercentConfidence = 100*(1 - 10^(-ProtScore)) So for instance ProtScore of .47 ==> PercentConfidence 66% ProtScore of 1.3 ==> PercentConfidence 95% Hope that helps.Following
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Answer added in Next Generation Sequencing22 The future of scientific-data interpretation (e.g. bio-data)By Martin Akerman · Cold Spring Harbor LaboratoryDana Pascovici · Macquarie UniversityI think quite often the analysis of biological experiments becomes an exercise in story-telling; one hopes to see moves towards making some prediction... [more]I think quite often the analysis of biological experiments becomes an exercise in story-telling; one hopes to see moves towards making some predictions that can be measures and verified. I would love to see research papers making more concrete and measurable results statements that could be databased more directly, and tools aggregating and making use of these predictions. Ingenuity and Metacore of course already distill such results, but it is done by people reading the papers and making judgements about how to in fact interpret and store the results. I would also love to see the reproducible science initiative catch on more in biology/bioinformatics, with researchers submitting the code that generates the results along with their data, and tools evolving that can store both the data and the code in some joint ways, making it easier for people to test and easily recover the results.Following
Publications (14) View all
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Article: Proteomic analysis indicates massive changes in metabolism prior to the inhibition of growth and photosynthesis of grapevine (Vitis vinifera L.) in response to water deficit.
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ABSTRACT: BACKGROUND: Cabernet Sauvignon grapevines were exposed to a progressive, increasing water defict over 16 days. Shoot elongation and photosynthesis were measured for physiological responses to water deficit. The effect of water deficit over time on the abundance of individual proteins in growing shoot tips (including four immature leaves) was analyzed using nanoflow liquid chromatography - tandem mass spectrometry (nanoLC-MS/MS). RESULTS: Water deficit progressively decreased shoot elongation, stomatal conductance and photosynthesis after Day 4; 2277 proteins were identified by shotgun proteomics with an average CV of 9% for the protein abundance of all proteins. There were 472 out of 942 (50%) proteins found in all samples that were significantly affected by water deficit. The 472 proteins were clustered into four groups: increased and decreased abundance of early- and late-responding protein profiles. Vines sensed the water deficit early, appearing to acclimate to stress, because the abundance of many proteins changed before decreases in shoot elongation, stomatal conductance and photosynthesis. Predominant functional categories of the early-responding proteins included photosynthesis, glycolysis, translation, antioxidant defense and growth-related categories (steroid metabolism and water transport), whereas additional proteins for late-responding proteins were largely involved with transport, photorespiration, antioxidants, amino acid and carbohydrate metabolism. CONCLUSIONS: Proteomic responses to water deficit were dynamic with early, significant changes in abundance of proteins involved in translation, energy, antioxidant defense and steroid metabolism. The abundance of these proteins changed prior to any detectable decreases in shoot elongation, stomatal conductance or photosynthesis. Many of these early-responding proteins are known to be regulated by post-transcriptional modifications such as phosphorylation. The proteomics analysis indicates massive and substantial changes in plant metabolism that appear to funnel carbon and energy into antioxidant defenses in the very early stages of plant response to water deficit before any significant injury.BMC Plant Biology 03/2013; 13(1):49. · 3.45 Impact Factor -
Article: Label-free quantitative shotgun proteomics using normalized spectral abundance factors.
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ABSTRACT: In this chapter we describe the workflow used in our laboratory for label-free quantitative shotgun proteomics based on spectral counting. The main tools used are a series of R modules known collectively as the Scrappy program. We describe how to go from peptide to spectrum matching in a shotgun proteomics experiment using the XTandem algorithm, to simultaneous quantification of up to thousands of proteins, using normalized spectral abundance factors. The outputs of the software are described in detail, with illustrative examples provided for some of the graphical images generated. While it is not strictly within the scope of this chapter, some consideration is given to how best to extract meaningful biological information from quantitative shotgun proteomics data outputs.Methods in molecular biology (Clifton, N.J.) 01/2013; 1002:205-22. -
SourceAvailable from: Daniel Kolarich
Article: Characterization of N- and O-linked glycosylation changes in milk of the tammar wallaby (Macropus eugenii) over lactation.
Katherine Wongtrakul-Kish, Daniel Kolarich, Dana Pascovici, Janice L Joss, Elizabeth Deane, Nicolle H Packer[show abstract] [hide abstract]
ABSTRACT: As one of several biologically active compounds in milk, glycoproteins have been indicated to be involved in the protection of newborns from bacterial infection. As much of the physical and immune development of the tammar wallaby (Macropus eugenii) young occurs during the early phases of lactation and not in utero, the tammar is a model species for the characterization of potential developmental support agents provided by maternal milk.In the present study, the N- and O-linked glycans from tammar wallaby milk glycoproteins from six individuals at different lactation time points were subjected to glycomics analyses using porous graphitized carbon liquid chromatography electrospray ionization mass spectrometry. Structural characterization identified a diverse range of glycan structures on wallaby milk glycoproteins including sialylated, sulphated, core fucosylated and O-fucosylated structures. 30 % of N-linked structures contained a core (α1-6) fucose. Several of these structures may play roles in development, and exhibit statistically significant temporal changes over the lactation period. The N-glycome was found to contain structures with NeuGc residues, while in contrast the O-glycome did not. O-fucosylated structures were identified in the early stages of lactation indicating a potential role in the early stages of development of the pouch young. Overall the results suggest that wallaby milk contains structures known to have developmental and immunological significance in human milk and reproduction in other animals, highlighting the importance of glycoproteins in milk.Glycoconjugate Journal 10/2012; · 2.12 Impact Factor -
Article: Differential regulation of aquaporins, small GTPases and V-ATPases proteins in rice leaves subjected to drought stress and recovery.
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ABSTRACT: Mechanisms of drought tolerance are complex, interacting, and polygenic. This paper describes patterns of gene expression at precise physiological stages of drought in 35-day-old seedlings of Oryza sativa cv. Nipponbare. Drought was imposed gradually for up to 15 days, causing abscisic acid levels to rise and growth to cease, and plants were then re-watered. Proteins were identified from leaf samples after moderate drought, extreme drought, and 3 and 6 days of re-watering. Label-free quantitative shotgun proteomics resulted in identification of 1548 non-redundant proteins. More proteins were down-regulated in early stages of drought but more were up-regulated as severe drought developed. After re-watering, there was notable down regulation, suggesting that stress-related proteins were being degraded. Proteins involved in signalling and transport became dominant as severe drought took hold but decreased again on re-watering. Most of the nine aquaporins identified were responsive to drought, with six decreasing rapidly in abundance as plants were re-watered. Nine G-proteins appeared in large amounts during severe drought and dramatically degraded once plants were re-watered. We speculate that water transport and drought signalling are critical elements of the overall response to drought in rice and might be the key to biotechnological approaches to drought tolerance.Proteomics 03/2012; 12(6):864-77. · 4.43 Impact Factor -
Article: Shotgun proteomic analysis of long-distance drought signaling in rice roots.
Mehdi Mirzaei, Neda Soltani, Elham Sarhadi, Dana Pascovici, Tim Keighley, Ghasem Hosseini Salekdeh, Paul A Haynes, Brian J Atwell[show abstract] [hide abstract]
ABSTRACT: Rice (Oryza sativa L. cv. IR64) was grown in split-root systems to analyze long-distance drought signaling within root systems. This in turn underpins how root systems in heterogeneous soils adapt to drought. The approach was to compare four root tissues: (1) fully watered; (2) fully droughted and split-root systems where (3) one-half was watered and (4) the other half was droughted. This was specifically aimed at identifying how droughted root tissues altered the proteome of adjacent wet roots by hormone signals and how wet roots reciprocally affected dry roots hydraulically. Quantitative label-free shotgun proteomic analysis of four different root tissues resulted in identification of 1487 nonredundant proteins, with nearly 900 proteins present in triplicate in each treatment. Drought caused surprising changes in expression, most notably in partially droughted roots where 38% of proteins were altered in level compared to adjacent watered roots. Specific functional groups changed consistently in drought. Pathogenesis-related proteins were generally up-regulated in response to drought and heat-shock proteins were totally absent in roots of fully watered plants. Proteins involved in transport and oxidation-reduction reactions were also highly dependent upon drought signals, with the former largely absent in roots receiving a drought signal while oxidation-reduction proteins were strongly present during drought. Finally, two functionally contrasting protein families were compared to validate our approach, showing that nine tubulins were strongly reduced in droughted roots while six chitinases were up-regulated, even when the signal arrived remotely from adjacent droughted roots.Journal of Proteome Research 11/2011; 11(1):348-58. · 5.11 Impact Factor