How to extract the numerical expression values for plotting time series curves for this NCBI GEO dataset using the GPL19046 Yeast_WG chip / platform?
I would like to plot the mRNA expression time series curves based on the data of the following NCBI GEO dataset.
Series GSE60112 The title of this dataset is:
Time-series DNA occupancy during the yeast respiratory cycle
The URL of this dataset is:
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE60112
The citation of the publication, which goes along with this dataset, is:
Cornelia Amariei, Rainer Machné, Viktor Stolc, Tomoyoshi Soga, Masaru Tomita and Douglas B. Murray. Time resolved DNA occupancy dynamics during the respiratory oscillation uncover a global reset point in the yeast growth program. Microbial Cell, 2014, Vol. 1, No. 9, pp. 279 – 288
This publication is freely available at http://microbialcell.com/researcharticles/time-resolved-dna-occupancy-dynamics-during-the-respiratory-oscillation-uncover-a-global-reset-point-in-the-yeast-growth-program/
My problem is that the platform / chip seems to be
GPL19046 Yeast_WG chip
What do I need to do to extract the numerical gene expression values from this dataset, which are most meaningful for plotting time series curves?
Thanks a lot
Thomas
I think your problem is that the dataset you refer to is not an expression study but ChIP on chip data using a tiled array.
What is a ChiP on chip data using titled array? What is a tilted array. My adviser asked me to analyze on of such kind of Chip on chip on titled array data but I could not envision what it is, what it would be good for and I could not find anybody, who could teach me how to analyze such kind of data. What kind of information can such ChiP on tilted array data provide? What is actually a titled array. What kind of information about the cell can it store. It must be useful for anti-aging research but even in the very hardest of my molecular biology classes there was no mentioning of such kind of ChiP on tilted array data. There are so many strange and confusing data formats, I cannot make any sense of, and for which I need to find somebody, who can teach me how to use it. Since people spend money to store such kind of data on the net it must have some kind of benefit. How many different types of data are actually available? I'd like to know when looking at datasets to at least know to which category they belong especially since it seems that aging could be regulated by the interplay between many different kinds of data-dimensions, all of which provide a tiny fraction of information and dependencies, which must be manipulated in such a way that over evolved internal suicide clock driven by our developmental genes can be stopped and reversed because our lives should no longer depend on the kind of evolution that selects for mechanisms, which prevent it from lasting indefinitely long as it was still the case in the RNA world, where evolution could not select against an individual RNA strand without adversely affecting its replication rate. Because back then, everything, which helped the RNA strand to withstand stressors also helped its replication but now evolution can select against individual parents without adversely affecting replication. As long as the entire individual was entirely composed of matter needed for replication, like RNA, there was by default no aging at all. It only evolved when proteins became dominant because then not all of the matter, of which the parents were composed, was needed for replication. Only this distinction allowed evolution to select for killing programs driven either b direly programmed destruction, e.g. apoptosis, or indirect negligence to maintain, repair and restore essential functions, e.g. chaperone aided protein folding, peroxisome degradation, acidity, proton, salinity, ion and nutrient gradients across membrane because our in-built suicide clock killed faster than those processes became the lifespan limiting factor.
If you don't know what ChIP-chip is, why do you want to analyze it then? Ask your advisor why he/she wants you to analyse ChIP-chip data.
I think using google or wiki can explain you a lot already about what ChIP-chip is or what a tiled array is:
https://en.wikipedia.org/wiki/Tiling_array
https://en.wikipedia.org/wiki/ChIP-on-chip
Nowadays most studies use ChIP-seq, so I wouldn't waste time on learning how to analyze these ChIP-chip array data, but that is just my opinion.