Dustin Atchley
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Most common mistake "I will remember how I got here". Take good NOTES!
Harvest .1 g of tissue, 100 micrograms of RNA from RNA isolation kit. Don't know we didn't sample the wrong part of the organ since the amount is so small. Be your hardest critic.
Isolated PolyA RNA (Ideally you get 2% of the RNA, protein coding)
Fragment the RNA before synthesizing.
DNA polymerase (reverse transcriptase) goes into mix, made cDNA.
Primers for amplification, you make a hexamer, which is every possible combination so that the complementary bases will bind.
Sequence cDNA that is the template to the RNA you are using. It is more stable.
Don't want full length DNAs, because we want about 75 bases for short reads.
Randomly fragmented which is wonderful.
Gel purified and reamplified (picture in PP)
We are sampling a wadded up tissue looking for proximal and distal end. Could have gotten serosa, need to be aware that we had to leave it frozen in order to not lose the RNA we needed.
Looking for genes involved in the up regulation of the amino acids that were measured in Figure 3 from 1a_pay_B4_pumpping_1995.
Housekeeping genes for liver and intestines (always on genes so we know we got the right cell)
Cell proliferation or cells growing (brush border growth, find these proteins)
Uptake genes
The trim fastQC files trimmed the frist 4 bases that which were the tags.
The Trimmomatic also removed bases with a quality score of less than 30 coming from the 3' end, only removed contiguous bases, and if the length of the read became 30 or less, the read itself was thrown out.
HTML FILES:
Untrimmed:
split_2no_i_fastqc.html
split_3no_i_fastqc.html
split_4fed_i_fastqc.html
split_5fed_i_fastqc.html
split_6fed_i_fastqc.html
Trimmed:
trim_1i_fastqc.html
trim_2i_fastqc.html
trim_3i_fastqc.html
trim_4i_fastqc.html
trim_5i_fastqc.html
trim_6i_fastqc.html
(Blast people found):
Intestines RNA and uptake regulator (80-90% is ribosomal and mitochondrial RNA)
(Online resource people):
Figured out how to download everything, but not the different cluster. There is a sequence searcher for all the genes that they had. No magic list of genes that were differentially regulated. Found a list in the supplementary materials in one of the papers.
(Quantification normalization) Library size and read depth varied. Total read count normalization is the typical route. DESeq is the other standard, which we are using today. Looking for a single number that considers the length and overrepresentation of the number based on that. Normalized for the length of the genes per million reads (Gene_results)
Normalization in DESeq compares same gene against itself. FPKM normalized number of reads among genes.
