Difference between revisions of "Notes"
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Snakes 1,2,3 not fed | Snakes 1,2,3 not fed | ||
Snakes 4,5,6 fed | Snakes 4,5,6 fed |
Latest revision as of 19:28, 4 February 2016
Snakes 1,2,3 not fed Snakes 4,5,6 fed --info in excel file RNA seq in multiplex --12 different samples, amplified with unique barcode (3 letter code) - simplified in excel file as individual letters.
How to seq RNA: -snakes fed and decapitated upon mouse tail disappearing -organs removed and flash frozen to preserve RNA -tissue from organ shaved off to ~.1g to begin RNA isolation kit (2 organs, 6 snakes, 12 total samples)
may not have sampled right part of organ, may have sampled connective tissue rather than proper organ
-use mRNA to make cDNA copy, cDNA more stable ---used magnetic beads with TTTTTT (poly T) end that binds to AAAAA (poly A), use magnet to attract beads, attached are mRNA ---High throughput sequencing gives only short reads (~75 base pairs from each end) -fragment mRNA into smaller strands so that with short reads ^^ each bp can be read -use reverse transcriptase (RT)- enzyme in HIV/retroviruses- to form cDNA from mRNA + nucleotides -primers made of every possible 6 letter bp combination --> allows us to amplify every primer fragment
1/14/16 looking for papers that identify enzymes involved in uptake -- involved in transport proteins across membrane: - SGLT1 --- glucose transporter
housekeeping genes -- orthologs, python
antiporter - ion travels opposite desired substance
simporter - ion travels across membrane with desired substance
1/19/16
antibodies - react based on shape and charge, thus can with substances other than target
2/2/16
Reports on individual tasks:
- intestines -> only one notable over-expressed genes "PREDICTED: Python bivittatus solute carrier family 15 (oligopeptide transporter), member 1 (SLC15A1), mRNA" snake 2
-someone found genes linked to gene ontology terms in excel file, will use to annotate genome
-normalizing RNA read data - > total read-count normalization (TC) divide transcript read count by total number of reads, rescale by factor of counts per million -> DESeq *** what we are using*** have to normalize both # of reads and length of genes - b/c longer genes (because randomly fragmented) will be overexposed compared to shorter genes