TM:Today, cheaper to synthesize DNA, use NGS to readout activity. Massively parallel reporter assay (MPRA) http://t.co/24EekqlIgg #ASHG14
12:36pm October 22nd 2014 via Hootsuite
TM: Gene reporter assays: GFP, promoter bashing (oh the memories!) Tedious, indiv cloning, protein/enz reporters #ASHG14
12:35pm October 22nd 2014 via Hootsuite
TM: (via comparative analysis, genome-wide chromatin, TF mapping, and genetics). But no way to determine regulatory regions #ASHG14
12:34pm October 22nd 2014 via Hootsuite
TM: Using assays can't provide 'proof' but highly complementary to find reg. elements. We have 100's of MB cis-regulatory elements #ASHG14
12:33pm October 22nd 2014 via Hootsuite
Tarjei Mikkelsen (Broad) High-throughput screening for causal non-coding variants #ASHG14
12:32pm October 22nd 2014 via Hootsuite
MD:A2:Not more variants; a larger construct of integrated data (using GTEx in this way) #ASHG14
MD:Q:Fx of missing herit. from cooperativity? A:Don't think it's here, other than 'shadow' effects. Need to integration, mechanisms #ASHG14
12:31pm October 22nd 2014 via Hootsuite
MD:Lastly, technology to do large-scale studies is available 'so let's get back to work' #ASHG14
12:27pm October 22nd 2014 via Hootsuite
MD:Summary - the reg data is there already; modeling of annotation is advanced (not perfect) but need to be used #ASHG14
12:26pm October 22nd 2014 via Hootsuite
MD: Integrating eQTL with GWAS - why GTEx is 'so important' Nica et al 2010 PLOS Genet. http://t.co/Jw7ZgvybDw #ASHG14
MD: Figures from this 2013 Science Kilpinen ref: http://t.co/203d906QQY #ASHG14
12:24pm October 22nd 2014 via Hootsuite
MD: Cooperativity is impt aspect that many are trying to dissect, coming from lots of data #ASHG14
12:22pm October 22nd 2014 via Hootsuite
MD:Local regulatory networks:w/in 1MB window, finding QTLs under genetic control. From only 47 indivs 'what could we do with 100's' #ASHG14
MD: Longitudinal eQTL - blood samples from Twins UK study (presented yesterday, J Bryois) #ASHG14
12:19pm October 22nd 2014 via Hootsuite
MD: Tumor-spec eQTLs - germline and tumor mutations. Enriched for driver genes. (back to Ongen et al) #ASHG14
12:18pm October 22nd 2014 via Hootsuite
MD: eQTLs studied for a while, now in 45 tiss. 2013 Nature http://t.co/ZZZ5PRElkR eQTL combined with ASE, strengthening enrichment #ASHG14
12:17pm October 22nd 2014 via Hootsuite
MD:Non-synon. changes: pinpoint cis-reglatory regions, Ongen Nature paper: http://t.co/Py16UPP0fh #ASHG14
12:16pm October 22nd 2014 via Hootsuite
MD:Env. alone cannot affect genes in trans. Looked at via discordant MZ twin analysis: find SNPs response that drives ASE #ASHG14
12:14pm October 22nd 2014 via Hootsuite
MD:But cis x trans interaction - about 20% ASE var. A factor in trans- that modulates difference in exp. Epistatic eff. widespread #ASHG14
12:13pm October 22nd 2014 via Hootsuite
MD:Buil et al poster:40-50% ASE var is likely due to GxG interaction and Env factors. Strong eQTL, lots of cis- (due to rare var) #ASHG14
12:12pm October 22nd 2014 via Hootsuite
MD:Advantage:look at K's of var's at once as indiv. random var's. as part of the context. EUVADIS, GTEx http://t.co/g3ZVLWCMr8 #ASHG14
12:11pm October 22nd 2014 via Hootsuite
MD:Not one var affecting one gene is enough. But: where (tiss. spec.) and what time, sex, disease, GxG, GxE, #ASHG14
12:10pm October 22nd 2014 via Hootsuite
MD:10's of K of conf. and unbiased reg. variants. Bona fide annotations for reg var's needed. And: lg-scale expts to derive context #ASHG14
12:09pm October 22nd 2014 via Hootsuite
MD:It's the same, but different context. Paris vs Las Vegas, all about context. For the regulatory genome, what's needed? #ASHG14
12:07pm October 22nd 2014 via Hootsuite
MD:What does the variation do? From the gene: illus. with a photo of the Eiffel tower. Context: "Paris and Las Vegas not the same" #ASHG14
12:06pm October 22nd 2014 via Hootsuite
MD:ENCODE's good work on the regulatory genome: we have 10's or 100's of thousands of WES and WGS, variation well recorded #ASHG14
12:05pm October 22nd 2014 via Hootsuite
MD:On top of that: cellular hetergeneity, the stochasticity and aging process of individual cells. 'Not about ave. beh. of a tissue #ASHG14
12:04pm October 22nd 2014 via Hootsuite
MD: Complexity of environmental variables: behavior, infections, external factors, properly est. of effects #ASHG14
MD: Where seq is unique, but unknown how the genetic variation affecting organismal phenotype. A comb. of env. and genetics #ASHG14
12:03pm October 22nd 2014 via Hootsuite
First: Manolis Dermitzakis (Univ Geneva) Cis-regulatory variation: interpretation, mechanisms and relevance to disease #ASHG14
12:02pm October 22nd 2014 via Hootsuite
RT @talastal: Interesting poster on the need of sanger seq confirmation of variants identified by NGS. #ASHG14 http://t.co/ESnNIvHpaB
9:25am October 22nd 2014 via Hootsuite
RT @iontorrent: #ASHG Poster: Development of a hotspot freq ladder for NGS assay workflows http://t.co/n9Y9FVOPEE http://t.co/KSz4QoyXSH
9:20am October 22nd 2014 via Hootsuite
RT @ceclindgren: Hilarious - but is it really true? "Why do academics dress so badly - because they are happy?" http://t.co/rXKeMa6MsJ
9:06am October 22nd 2014 via Hootsuite
.@ReSurfX @claritas4kids ...he is careful in what compar's are made with tumor/norm using LCM to pick up signal. Yet to be published 3/3
9:04am October 22nd 2014 via Hootsuite
.@ReSurfX @claritas4kids ...that could explain the higher prevalence of TNBC among African Amer females. If you look up his work... (2/3)
9:02am October 22nd 2014 via Hootsuite
.@ReSurfX @claritas4kids Talking w/ML about his results after his talk, he's looking for subtle differences in br tissue subtypes... (1/3)
9:01am October 22nd 2014 via Hootsuite in reply to
RC: Conclude: CRISPR/Cas9 appears to be highly specific; varying by guide RNA design #ASHG14
9:29pm October 21st 2014 via Hootsuite
RC: Study 2:broad spread of gRNA efficiency; paired guides most efficient. #ASHG14
9:25pm October 21st 2014 via Hootsuite
RT @claritas4kids: #ASHG14 Lora Bean: 5825 VUS currently in Emory database, ~17400 genes of unknown significance
9:24pm October 21st 2014 via Hootsuite
RC: In Study 1 found 3' microhomology on Chr2 w/segment from Chr4 translocation; otherwise minimal off-target genome editing #ASHG14
RC: SORT1 and LINC00116 lines + parental all WGS to 60x; Study 2 in CD34+ HPSCs, heterogeneous. 3,390x w/targeted capture #ASHG14
9:22pm October 21st 2014 via Hootsuite
RC: Study 1 in 2014 Cell Stem Cell ref http://t.co/ReEP9FEu4R unbiased and genome-wide. Deep WGS in hESC clones #ASHG14
9:20pm October 21st 2014 via Hootsuite
RC: Single guide RNAs will introduce small muts. ds break, small indels or subst. Can co-transfect 2, for larger rearr like a del. #ASHG14
9:18pm October 21st 2014 via Hootsuite
Ryan Collins - MGH WGS char's multiple mut. mechanisms from off-target effects of CRISPR-Cas9 and TALEN treatments in hESC #ASHG14
9:17pm October 21st 2014 via Hootsuite
CK:Q:Can it be connected to single-cell RNA-Seq? A:Started with brain tissue #ASHG14
9:16pm October 21st 2014 via Hootsuite
CK: No RNA or FDG values are at the cell level. Illus. their multi-step deconvolution workflow; 7.5M datapoints down to 5 clusters #ASHG14
9:12pm October 21st 2014 via Hootsuite
CK:18 smpls from MSKCC; 27 biomarkers, 30 regions; 300-500 cells per region. ID markers that correlate to FDG uptake #ASHG14
9:09pm October 21st 2014 via Hootsuite
CK: Illustrates method MultiOmyx 2013 PNAS ref http://t.co/chEE4IkSUN Repeated DAPI staining and bleaching 10-cycles, 4 mkrs/cycle #ASHG14
9:07pm October 21st 2014 via Hootsuite
CK: "Both complete and partial deconvolution methods attempt to capture both cell-level and whole system-level context" #ASHG14
9:05pm October 21st 2014 via Hootsuite
CK:Cell types contribute to clinical phenotypes for br cancer. Chart of sample heterogeneity, sample-prep sorting difficulty #ASHG14
9:04pm October 21st 2014 via Hootsuite
