Meetings_Minutes_Susi

>> http://www.ncbi.nlm.nih.gov/pubmed/20064378
 * **meeting date: Feb 21st. Scott and Susi / Feb 22nd Vishal and Susi**
 * **Discussed:**
 * **compare and contrast long RNA seq data with microarray data (get data from Wejun)**
 * **overlay our signature (from long RNA seq) with existing datasets from different cancers (breast cancer set from Carol Prives or others)** http://www.ncbi.nlm.nih.gov/pubmed/22265415 **(ask Agustin for data)**
 * **look at Mkk4 mutation status in (pancreatic) cancers - start getting people from the cBio group involved**
 * http://www.ncbi.nlm.nih.gov/pubmed/18772397
 * **connect p53 and Mkk4 based on genomic data: codeletion of Mkk4 and p53. mutational status of Mkk4 and p53?**


 * **Meeting Date: Feb 1st 2012. Time: 9:00 am to 10:15 am**
 * **Discussed:**
 * Current progress on RNA-seq data
 * Clarified Aim3
 * Clarified that Quality control should be done immediately. Both Parties agreed to this.
 * Vishal will spend the day doing QC today.
 * Susi asked questions on Ingenuity. Vishal suggested using DAVID as well as looking up help from Ingenuity. Ask Molly, if she replies
 * ITRAQ data analys
 * We downloaded the RAW data
 * Looked over the excel file that Darrill had sent
 * discussed strategy to analyze using existing excel file. Details below.
 * **TO BE DELIVERED TODAY Feb 1st:**
 * **Initial QC of data (5 hours)**
 * **Heat map scale (1 hour)**
 * **Try to finish the guide loop (but I am not hopeful that I will have time for that)**


 * For susi to do:
 * Try DAVID website for pathway analysis
 * Problem with Ingenuinity: Does not recognise MicroRNA name. Look at manual for miRNA analysis


 * **FUTURE PLAN For Itraq Data: To be completed by: Feb 28th (4 weeks)**
 * SUSI: Clean up the data as much as you can
 * Remove any rows that have dashes for BOTH excel files.
 * Send both clean excel files
 * Vishal:
 * Take the clean files. Sort the data on the MATCH column and remove further the rows that have less than 10% match. (Suggested by Susi on Darril's recommendation)
 * Do a ratio of interested columns A/b/C/b to get the ratio of A/C.
 * Also need to Look and FIND out about ratio statistical significance.

>>> ALL FILES HAVE BEEN HIGHLIGHTED (IN RED OR ORANGE) ON THE SERVER VISHAL-FOR SUSI-miRNA AND iTRAQ
 * **NEXT MEETING: FEB 7th 2012 9:30 am**
 * MEETING NOTES: Feb 8th 2012, time 10:10 am
 * Our hypothesis is that when miRNA is down, the protein levels for the protein the miRNA targets should be UP.
 * or if the miRNA is UP, then the protein it targets is DOWN.
 * We need to test this with our miRNA data and ITRAQ protein data. This is how we will do it:
 * We have 2 files.
 * ITRAQ proteins and their values for 1309 day 4 (both reps) and 1224 (KD) day 4 rep 5
 * We have a miRNA prediction file from TARGETSCAN. This contains GeneID for miRNAs that are UP or Down in muP53 compared to KD
 * We need to combine the 2 protein files to get a file that has all proteins. (4 hours) **DONE Feb 8th.**
 * We will now combine the 2 data sets on the GENE ID field so that we get all the proteins that are expressed and are common between miRNA predicted genes and ITRAQ gene--protein dataset ( 4-5 hours) (For one, for second, 1 hour)
 * Next once we have the common genes / protiens, we will take the ratio of 1309/4d with 1224 day 4/rep5 for both replicates of 1309. (4 hours)
 * According to our hypothesis above, we should see that miRNAs that are up should have proteins whose ratio is down and vice versa. Lets see!
 * March 6th.
 * Meeting notes:
 * Part 1) We need to find the overlap between the 2 FOLD up and down expressed ITRAQ proteins (TRAQ%20deregulated%20proteins%201309_10_vs_1224_12.xls) and get the gene ID from them and then overlap them with the Long RNA SEQ (results_longRNA_ordered_combined)
 * part 2) So find the overlap between the ITRAQ proteins and the predicted mRNA targets (predicted mRNA targets are stored here: mutp53 upregulated miRNAs.xls and mutp53 dowregulated miRNAs.xls). You can do this by selecting the genes from the file in miRNA and ITRAQ/mutp53_down_miRNAs.csv and similarly for up
 * Search for the gPCR's in the results_longRNA_orderedCombined.txt file. Just select these ones. Order them by fold change. Take the significant ones. Do a heatmap.
 * Do the same above for GiPCR's
 * GENE FILES FOR THAT ARE STORED IN THE FOLDER 'NEW LEAD'