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day 0
sample 1 - untreated
day 3
sample 2 - treated with "A"
sample 3 - treated with "B"
sample 4 - treated with "C"
day 6
sample 5 - treated with "A"
sample 6 - treated with "B"
sample 7 - treated with "C"
day 9
sample 8 - treated with "A"
sample 9 - treated with "B"
sample 10 - treated with "C"
They want to see DE across time as well as across treatments. I am just a bioinformatician. Some body else did the experiments and I have the rnaSeq data for it. This is all I have been told.
Ashu
Originally posted by Simon Anders View Post45 comparisons still would only take an hour or so. However, my feeling is that you are doing something fundamentally wrong if you are comparing pairs of samples, rather than pairs of conditions or time points.
Maybe explain a bit more about the biology of your experiment.
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Originally posted by ashuchawla View Postday 0
sample 1 - untreated
day 3
sample 2 - treated with "A"
sample 3 - treated with "B"
sample 4 - treated with "C"
day 6
sample 5 - treated with "A"
sample 6 - treated with "B"
sample 7 - treated with "C"
day 9
sample 8 - treated with "A"
sample 9 - treated with "B"
sample 10 - treated with "C"
They want to see DE across time as well as across treatments. I am just a bioinformatician. Some body else did the experiments and I have the rnaSeq data for it. This is all I have been told.
Ashu
I think you need a "untreated" control for every time point, or the time will be a extra variable.
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I think I have found a solution. CuffDiff actually does output a count of each feature (genes.count_tracking, cds.count_tracking, etc). I ran cuffDiff with each sample treated as a separate treatment, c.f. different samples from the same treatment being identified as replicates using the -L/--labels feature. This gave me count data for each feature in each sample.
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The field of epigenetics has traditionally concentrated more on DNA and how changes like methylation and phosphorylation of histones impact gene expression and regulation. However, our increased understanding of RNA modifications and their importance in cellular processes has led to a rise in epitranscriptomics research. “Epitranscriptomics brings together the concepts of epigenetics and gene expression,” explained Adrien Leger, PhD, Principal Research Scientist...-
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by seqadmin
Proteins are often described as the workhorses of the cell, and identifying their sequences is key to understanding their role in biological processes and disease. Currently, the most common technique used to determine protein sequences is mass spectrometry. While still a valuable tool, mass spectrometry faces several limitations and requires a highly experienced scientist familiar with the equipment to operate it. Additionally, other proteomic methods, like affinity assays, are constrained...-
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