![Logistic regression model (from [15]) predictions of Fe deficiency of
Arabidopsis plants across multiple experiments.
Ivan Baxter Briefings in Functional Genomics 2010;9:149-
156
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Bioinformatics ppt
- 1. Ionomics By Alfred Daramola, Sandra Unorji Sneha Chaganti Course: BIOT 5733 Lecturer : Dr. Stephens April 26, 2015
- 2. OUTLINE Introduction of Ionomics Applications of Ionomics in various fields Ionomics vs Bioinformatics Future Prospects of Ionomics.
- 3. Introduction Ions are atoms or molecules with net negative or positive charges as a result of unequal number of protons and electrons. Ionome is defined as all the mineral nutrient and trace elements in an organism. Plants take up nutrients in form of ions. These ions are classified based on their requirement- macro, micro and beneficial elements.
- 4. What is Ionomics? This is the measurement of the elemental composition of an organism and the changes in this composition using high-throughput elemental profiling. It is one of the major pillars of functional genomics. Ionomics was built from the early ideas of Robinson and Pauling, in the late 60’s, which was combining metabolomics with mineral ions.
- 5. Your text here Concept of Ionomics
- 6. Importance of Ionomics It helps in determining the functional status of the cell, tissue or organism. It provides an effective approach to the functional analysis of genes and gene network. The study of ions helps in the regulation of minerals available for plants productivity. - Amount of ions = Productivity of plants = Nutritional security.
- 7. Applications of Ionomics in various fields Importance of ionomics to plant and soil in field Heavy metal toxicity, nutrient deficiency and mineral detection. Long-term fertilizer treatment. Study of breast cancer , colorectal cancer and brain cancer. The model organisms Arabidopsis , yeast and now Lotus japonica rice (Oryza sativa), maize (Zea mays), soybean (Glycine max), mouse (Mus musculus), worm (Caenorhabditis elegans), and human cell lines.
- 8. Heavy Metal Stress Responses
- 9. Logistic regression model (from [15]) predictions of Fe deficiency of Arabidopsis plants across multiple experiments. Ivan Baxter Briefings in Functional Genomics 2010;9:149- 156 Published by Oxford University Press 2010. For permissions, please email: journals.permissions@oxfordjournals.org
- 10. The K (A) and Zn (B) concentrations of maize kernels from seven RILs of the Il14hxB73 nested association mapping population where the Sugary (Su) locus is segregating (n = 10–12 per line, n = 4–8 for su+/su− within each line). Ivan Baxter Briefings in Functional Genomics 2010;9:149- 156 Published by Oxford University Press 2010. For permissions, please email: journals.permissions@oxfordjournals.org
- 11. Different methods of displaying ionomics data from http://www.ionomicshub.org. Ivan Baxter Briefings in Functional Genomics 2010;9:149- 156 Published by Oxford University Press 2010. For permissions, please email: journals.permissions@oxfordjournals.org
- 13. DATA SEARCH
- 14. IONOMIC ATLAS • lonomics Atlas allows connections to be made between the genetic regulation of the ionome of plant populations and their landscape distribution. • This will allow scientists to compare ionomic variations in different environmental conditions
- 16. Bioinformatics in Ionomics The advancement of high-throughput phenotyping technologies has created a huge amount of data which can not be maintained without a data management tool. Bioinformatics provides an easily accessible tool for the data storage and retrieval system. It integrates the workflow information in the genomic-scale data acquisition and validation and provides an open access to data mining and discovery.
- 17. Bioinformatics in Ionomics The Purdue Ionomics Information Management System (PiiMS) provides integrated workflow control, data storage, and analysis to facilitate high throughput data acquisition, along with integrated tools for data search, retrieval, and visualization for hypothesis development. PiiMS is deployed as a World Wide Web-enabled system, allowing for integration of distributed workflow processes and open access to raw data for analysis by numerous laboratories.
- 18. Bioinformatics in Ionomics PiiMS currently contains data on shoot concentrations of P, Ca, K, Mg, Cu, Fe, Zn, Mn, Co, Ni, B, Se, Mo, Na, As, and Cd in over 60,000 shoot tissue samples of Arabidopsis (Arabidopsis thaliana), including Ethyl Methane Sulfonate, fast-neutron and defined T-DNA mutants, and natural accession and populations of recombinant inbred lines from over 800 separate experiments, representing over 1,000,000 fully quantitative elemental concentrations.
- 19. Bioinformatics in Ionomics Metadata Capture Data Security and Release eLaboratory Portal DATA SEARCH, DISPLAY, AND DOWNLOAD Forward-Genetic Search Reverse-Genetic Search SOFTWARE DESIGN AND AVAILABILITY
- 20. FUTURE PROSPECTS AND USES OF IONOMICS It will help in the identification of the gene, gene network and coordination among different genes controlling ion accumulation in the plant system The knowledge of ionomics can be used in the manipulation the ionomic profile of plants to regulate the accumulation of ions which may be dangerous to the plants, herbivores or humans The knowledge will also help in manipulating plants for biofortification with micronutrients
- 21. FUTURE PROSPECTS AND USES OF IONOMICS It will help in the identification of the gene and environment interaction at the different stages of growth Development of the database to create an environment where in silico experiments will be performed
- 22. References 1. Dragut, E.C., Ouzzani, M., Madkour, A., Mohammed,N., Baker, P. and Salt, D. (2012). Ionomic atlas: a tool to explore interconnected ionomic, genomic and environmental data. Purdue University Press. 2680-2682. 2. Salt,D., Baxter,I. and Lahner,B. (2008). Ionomics and the study of the plant ionome. Plant Biol. 59,709-842. 3. Ionomics. (2014, August 21). In Wikipedia, The Free Encyclopedia. Retrieved, April 23, 2016, from https://en.wikipedia.org/w/index.php? 4. Satismtuti,K. et al. (2013). Plant ionomics: a platform for identifying novel gene regulating plant mineral nutrition. J. Plant Sci. 4, 1309-1315.
- 23. Thank you for listening
Editor's Notes
- #10: Logistic regression model (from [15]) predictions of Fe deficiency of Arabidopsis plants across multiple experiments. Data for the shoot concentrations of Mn, Co, Zn, Mo, and Cd from Arabidopsis Col-0 grown in 357 different experiments (median n = 12 per experiment) from 04/21/2003 through 04/12/2007 were analyzed using the logistic regression model (simple model) and the data presented as the percentage of Arabidopsis plants predicted as Fe-deficient in each experiment. Over time different soil batches were used to grow the plants, and these are represented on the graph as a thick gray (batch 1) or dashed (batch 2) line. Also, during this extended period of experiments Fe was included in the fertilization solution as either Fe-tartrate (dashed arrow) or Fe-HBED (solid arrows). For Fe-HBED each new stock solution of Fe-HBED is represented by a different arrow.
- #11: The K (A) and Zn (B) concentrations of maize kernels from seven RILs of the Il14hxB73 nested association mapping population where the Sugary (Su) locus is segregating (n = 10–12 per line, n = 4–8 for su+/su− within each line). Error bars indicate standard deviation. In a linear model with line and Su as factors, line was significant (P < 1 × 10–5) for both elements and 9 of the 14 other elements measured, while Su was significant for K and five other elements.
- #12: Different methods of displaying ionomics data from http://www.ionomicshub.org. (A) Z-score plot of frd3-1, the positive control used in many Arabidopsis trays. The number of standard deviations away from the average of a control line. (B) A percent change plot of B from a tray of T-DNA lines. Note that all lines are slightly low, indicating that the Col-0 reference is slightly high in B in this tray and the low B of these lines should be disregarded. (C) A histogram of Mn values from the yeast knockout collection with the line YGL167C indicated. (D) Boxplots of data from the plate where YGL167C was run. The colored boxes denote the interquartile range containing 50% of the values (25–75%) while the whiskers denote the extremes of the values.