Patient Tutor to teach Time Series Cell Cycle RNA Seq. analysis to construct co-expression and regulatory networks to visually impaired student
I am looking for a patient tutor, who can teach me remotely with TeamViewer and Skype every step in analyzing the 7 publically available RNA Seq online yeast cell cycle datasets listed below. These are all time series datasets but they have many more time points than the microarray yeast cell cycle datasets based on the Yeast 2 chip from Affymetrics, which I have learned to analyze and implement in my R script last week. In a next step I would like my tutor to teach me how to construct co-expression and regulatory networks based on the time series data from each of the 7 RNA Seq. datasets below.
1.) E-MTAB-3605 - Yeast transcriptome profiling in replicative ageing https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-3605/?query=time+series+yeast
2.) Series GSE80474 Title Investigating Conservation of the Cell-Cycle-Regulated Transcriptional Program in the Fungal Pathogen, Cryptococcus neoformans https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE80474
3.) Janssens GE, Meinema AC, González J, Wolters JC, Schmidt A, Guryev V, Bischoff R, Wit EC, Veenhoff LM, Heinemann M, 2015E-MTAB-3605 - Yeast transcriptome profiling in replicative ageing
Publicly available at the EMBL-EBI ArrayExpress (Accession no: E-MTAB-3605).
4.) Janssens GE, Meinema AC, González J, Wolters JC, Schmidt A, Guryev V, Bischoff R, Wit EC, Veenhoff LM, Heinemann M, 2015Aging Yeast
Publicly available at the EMBL-EBI PRIDE Archive (Accession no: PXD001714).
5.) Series GSE85595 Time course analysis of gene expression during hypoxia in S. cerevisiae using RNA-Seq https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE85595
6.) Series GSE81932 TitleRibosome profiling of synchronous, non-arrested yeast cells identifies translational control of lipid biosynthesis enzymes in the cell cycle https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE81932
7.) Series GSE80474 Investigating Conservation of the Cell-Cycle-Regulated Transcriptional Program in the Fungal Pathogen, Cryptococcus neoformans https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE80474
I need the results of at least some of the datasets above to complete my dissertation. Please let me know if you can help.
Some general questions about cyclical gene expression as it could hold the key for truly understanding and subsequently completely abolishing aging?
1.) What is actually the advantage of constructing co-expression and regulatory networks from time-series data compared to non-time series data?
2.) Can time-series data add new dimensions to our findings that other data cannot?
3.) Could it be that the relatively transient very brief, but periodically reoccurring and highly oscillating mRNA levels, which may fluctuate synchronously for some genes while asynchronously fluctuate for others.
4.) Will relative timings between the oscillation periods for different genes change as the yeast ages?
5.) Will the relative expression change between different genes over time? Are the time periods of oscillations equal for all genes?
6.) Could it be that the highly regulated, very rapid, but equally transient mRNA oscillations serve any purpose , other than progressing through the cell cycle stages and checkpoints with the only final objective to maximize replication output or may it have some still undiscovered benefits?
7.) How come that even most genes, which belong to pathways that are generally believed to remain unaffected by the cyclical fluctuations of the cell cycle seem to follow its two major temporal anti-correlated expression motifs very strongly?
8.) Which biological properties other than replication are lost or altered when the cell cycle stops?
9.) Are the amplitude or period of the cyclical expression change with advancing age?
10.) How is the cyclical behavior of the cell cycle similar to, or, different from the sleep-wake-cycle?.
11.) Does yeast have a sleep-wake-cycle too?
12.) Are there any experiments or datasets exploring the cyclical expression, which can be assumed for the sleep-wake-cycle?
13.) In humans, many of our cells, especially the very rapidly dividing polypoetic stem cells, red blood precursor cell, bone marrow cells, stomach lining and immune cells, keep dividing very rapidly. Hence, even in humans, cell cycle induced fluctuations should be more noticeable. Has the human or mouse transcriptome ever been analyzed in short enough time intervals for detecting the relatively short but surprisingly intense fluctuations driven by the cell cycle?
14.) If we keep spreading our time intervals to far apart for detecting these transient periodically reoccurring cell cycle driven concentration changes aren't we at risk of missing something very essential? .
Could you please email me links to cell cycle and sleep-wake cycle datasets?
Where can I find proteomic cell cycle datasets for yeast?
Thanks in advance for considering helping me out.
Thomas Hahn ... Show more