Gruenbaum: Had woodchuck chronic carriers, HCC, resolved and uninfected; had cell and marker phenotype data #RNASeq2014
2:34pm June 19th 2014 via Hootsuite
Gruenbaum: Had to produce recomb. woodchuck IFN; est. of a model for testing drug candidates. Infection time-course data #RNASeq2014
2:32pm June 19th 2014 via Hootsuite
Gruenbaum: A second animal model: woodchuck (!) [N.B. - have never seen woodchuck data before.] 'Few reagents available' #RNASeq2014
2:30pm June 19th 2014 via Hootsuite
Gruenbaum: Drug candidate RNA-Seq shows similar induction of interferon stim. genes as Pegasys #RNASeq2014
2:27pm June 19th 2014 via Hootsuite
Gruenbaum: Unexpectedly found human hemoglobin in mouse blood (i.e. likely human hematopoetic stem cells carried over) #RNASeq2014
2:25pm June 19th 2014 via Hootsuite
Gruenbaum: But the humanization of mouse liver in their model: 'highly variable' (by meas. of human albumin) #RNASeq2014
2:24pm June 19th 2014 via Hootsuite
Gruenbaum: No animal models fully reflect human disease for HBV. Humanized SCID mice uPA+/+ mice model used w/hu hepatocyte xp #RNASeq2014
2:21pm June 19th 2014 via Hootsuite
Gruenbaum: Therapy (nucleosides): low rate of clinical cure (~10%); interferon therapy (Pegasys) also has limitations #RNASeq2014
2:19pm June 19th 2014 via Hootsuite
Gruenbaum: Risk for hepatocellular carcinoma high cumulative risk with specific HBsAg antigen; also survival with cirrhosis #RNASeq2014
2:16pm June 19th 2014 via Hootsuite
Gruenbaum: Illustrates the worldwide situation with chronic HBV infection. 4 phases 2005 review: http://t.co/RNciODvcG4 #RNASeq2014
2:14pm June 19th 2014 via Hootsuite
Lore Gruenbaum (Roche): "Using RNA-Seq to characterize animal models of chronic viral infection" #RNASeq2014
2:12pm June 19th 2014 via Hootsuite
von Shack: Q&A: Not doing TCR NGS, they are considering it - "interpretation is difficult"; looking at Ab-interactions directly #RNASeq2
1:14pm June 19th 2014 via Hootsuite
von Shack: Sure enough, principals at HiFiBio also behind RainDance (Weitz, Griffiths) http://t.co/i4GHSkROys #RNASeq2014
1:10pm June 19th 2014 via Hootsuite
von Schack (Pfizer): Shows animation from HiFiBio, for single-cell analysis. Reminiscent of RainDance http://t.co/z8Cvy35n7q #RNASeq2014
1:08pm June 19th 2014 via Hootsuite
Tibbitts: Myrna: uses Bowtie + R for interval calcs; Hadoop running in the cloud "a little bit of work.. works well" #RNASeq2014
12:00pm June 19th 2014 via Hootsuite
Tibbitts: Universal sample ID (carried throughout); number of vendors for seq; cloud pipelines (Omicsoft) still TBD; Myrna #RNASeq2014
11:55am June 19th 2014 via Hootsuite
Tibbitts: Plan to do long-term archiving by pushing FASTQ files into 'cold storage'; $100/TB/year for this #RNASeq2014
11:51am June 19th 2014 via Hootsuite
Tibbitts: Decided to outsource seq; keep data in the cloud; use MSFT & AMZN; hard drives shipped from seq vendor to them #RNASeq2014
Tibbitts: Legacy SQL systems could not keep up; looked at Myrna/Hadoop for RNA-Seq data, and CouchDB to replace Oracle #RNASeq2014
11:48am June 19th 2014 via Hootsuite
Thomas Tibbitts (Infinity Pharmaceuticals): "Exploring scalable options for processing and storing of RNA-Seq data" #RNASeq2014
11:44am June 19th 2014 via Hootsuite
Lowe:Q&A: What about sizing of lib's for ENCODE? Known but this is a tool for screening and discovery #RNASeq2014
Lowe:Q&A: BAM uploads okay, but they prefer their own alignment to insure quality; integrated mRNA, miRNA, DNA var, ChIP, Me-Seq #RNASeq
11:42am June 19th 2014 via Hootsuite
Lowe: Data submitted for publ; able to now sequence through base mods with new method, showing cp to TCGA #RNASeq2014
11:37am June 19th 2014 via Hootsuite
Lowe: Showed some other examples of novel tRNA expression data from public sources; Demethylation library for mod RNA detection #RNASeq2014
11:35am June 19th 2014 via Hootsuite
Lowe: Hurto 2011 reference: http://t.co/tMombxxDpr Other roles for tRNAs #RNASeq2014
11:32am June 19th 2014 via Hootsuite
Lowe: Now looking at the same tRNA in ENCODE ncRNAs across cell lines: a pattern of 3' fragment in 2 of them; alt role of tRNAs #RNASeq2014
11:31am June 19th 2014 via Hootsuite
Lowe: Matching analysis to the library prep kit; not re-inventing 'yet another pipeline'. #RNASeq2014
11:30am June 19th 2014 via Hootsuite
Lowe: Maverix: 'Analytic kits' that anyone can use; may not be perfectly optimized kit, but 90% there w/o the opp'y cost #RNASeq2014
11:29am June 19th 2014 via Hootsuite
Lowe: Points out more fragments are there in Prostate and Br cancer tissues of these tRNAs in the TCGA datasets #RNASeq2014
11:25am June 19th 2014 via Hootsuite
Lowe: tRNA fragments relevant to cancer. 2013 PNAS: http://t.co/ZNCL3OOibG But only in cell lines; cp to TCGA? @MaverixBiomics #RNASeq2014
11:23am June 19th 2014 via Hootsuite
Lowe: Referred to a figure from this 2012 Turchinovich review http://t.co/8foHjQcmMb #RNASeq2014
11:21am June 19th 2014 via Hootsuite
Todd Lowe (Maverix): "Beyond known microRNAs: exploring the rest of the small RNA transcriptome" #RNASeq2014
11:19am June 19th 2014 via Hootsuite
Haynes:Q&A: Fusion param's take into account freq of 3'/5' partners #RNASeq2014
10:06am June 19th 2014 via Hootsuite
Haynes: A:(con't): 105-110bp library insert size. #RNASeq2014
10:05am June 19th 2014 via Hootsuite
Haynes:A (con't): all the 'junk on the right' were still useful (as low as ~20% exonic, ~10% intronic, ~75% intergen) #RNASeq2014
10:04am June 19th 2014 via Hootsuite
Haynes:Q&A: Can intragenic sequence be gDNA contam? Bar chart of exonic/intronic/intergenic, all libraries were used #RNASeq2014
10:03am June 19th 2014 via Hootsuite
Haynes: S Crosby comment before a question: "Fantastic talk". I agree. #RNASeq2014
10:01am June 19th 2014 via Hootsuite
Haynes: Training / test cohorts: models of FFPE trained very good sens. (.95) and spec (0.61) to FNA test set #RNASeq2014
9:59am June 19th 2014 via Hootsuite
Haynes: PC1/2 nice division between benign / malignant; also FNA vs FFPE (due to blood contamin of FNA) #RNASeq2014
9:58am June 19th 2014 via Hootsuite
Haynes: Applied to 123 FFPE including orig 68; also 65 FNAs retrospective. Nice concordance bet. targeted to WT RNA-Seq #RNASeq2014
9:57am June 19th 2014 via Hootsuite
Haynes: 41 gene signature migrating to targeted RNA-Seq assay, using targeted multiplex PCR approach. Only 50K reads/sample #RNASeq2014
9:55am June 19th 2014 via Hootsuite
Haynes: Of 47% 'no mutation' of malignants; 50% novel fusions, 33% no fusions, 17% published. Manuscript in prep #RNASeq2014
9:54am June 19th 2014 via Hootsuite
Haynes: "15 fusions spec to malignancies; remaining (of the 26) are likely trans-splicing" (trace levels) #RNASeq2014
9:53am June 19th 2014 via Hootsuite
Haynes: Sees concordance to TCGA cohort; ID 40K fusions in 68 samples (lots of FPs); 41 passed filtering; 26 conf. by Sanger #RNASeq2014
9:52am June 19th 2014 via Hootsuite
Haynes: 68 FFPE blocks of resected tumors (not FNA), about half malignant; about half of those had unknown drivers #RNASeq2014
9:49am June 19th 2014 via Hootsuite
Haynes: Thyroid cancer FNA samples: many are indeterminate via cytometry and single-gene approaches #RNASeq2014
9:47am June 19th 2014 via Hootsuite
Haynes: From 500 samples, success rate downstream on the order of 90% #RNASeq2014
9:46am June 19th 2014 via Hootsuite
Haynes: 50M reads 2x50 PE's. Their report is called 'Surasight'. They use chimerascan 2011 pub: http://t.co/bdfEZDgDK9 #RNASeq2014
Haynes: Algorithm choice for fusion breakpoints in FFPE matters; they have developed 3'/5' sensitive params. #RNASeq2014
9:44am June 19th 2014 via Hootsuite
Haynes: Gene fusions from FFPE: high FP (homologous genes); trans-splicing in normal cells occur; passenger fusions; short fx #RNASeq2014
9:43am June 19th 2014 via Hootsuite
