OD: Correlation between peaks (ex H3K27ac & PU1 <1MB away). Found 78.4K significant associations. 10's to 100's of kb apart #ASHG14
2:35pm October 20th 2014 via Hootsuite
OD: 47 lymphoblastoid lines from 1000GP CEU, DNA var, ChIP, RNA-Seq. One gene: variants, 3 histone marks + polII + PU1, mRNA #ASHG14
2:34pm October 20th 2014 via Hootsuite
Olivier Delaneau (Univ Geneva) Genetic control of chromatin in a Human population #ASHG14
2:33pm October 20th 2014 via Hootsuite
XZ: Paper is on preprint server here: http://t.co/DfiiFySnlG #ASHG14
2:32pm October 20th 2014 via Hootsuite
XZ: Genetic effects underlie gene exp variation, not age or sex. Trans variation explains gene exp var, not cis #ASHG14
2:30pm October 20th 2014 via Hootsuite
XZ: Higher power in baboons compared to YRI: greater var in baboons (>21% power); high MAF SNPs >34%; longer LD >43% #ASHG14
2:27pm October 20th 2014 via Hootsuite
XZ: Focus on heterozygous sites at high coverage. 1.7k eQTL genes of 10.4K; 510 ASE genes of 2.2K. eQTL and ASE overlap 240 genes #ASHG14
2:25pm October 20th 2014 via Hootsuite
XZ:Assessing quality: 69 YRI from HapMap; put thru pipeline % concordance of 98%. eQTL genes measured: 59% fewer (in absence of ref) #ASHG14
2:23pm October 20th 2014 via Hootsuite
XZ: 63 baboons, no reference; use RNA-Seq for both exp and to call cSNVs together to look at eQTL and ASE via GEMMA #ASHG14
2:21pm October 20th 2014 via Hootsuite
XZ: Much work on mechanism of gene exp var in humans, but not in primates. Wild yellow baboons fr. Kenya, 8 generations data #ASHG14
2:20pm October 20th 2014 via Hootsuite
Xiang Zhou (U Chicago): Characterizing the genetic architecture of gene expression variation in wild baboons via RNA sequencing #ASHG14
2:19pm October 20th 2014 via Hootsuite
AB: Data shows need to trim to >10 bp from exon end, and need for including annotation. Down sampling to examine replicate ability #ASHG1
2:14pm October 20th 2014 via Hootsuite
.@TheGBLab Yes great to see you too Jose!
2:11pm October 20th 2014 via Hootsuite in reply to
AB:Illustrates limitation of short-read seq, getting collection of junction reads of exon-exons.How many bases req'd of each? #ASHG14
2:08pm October 20th 2014 via Hootsuite
AB:Looking at GTEx: 13 brain regions, data available here: http://t.co/RTOlpXCB9D #ASHG14
2:06pm October 20th 2014 via Hootsuite
AB:Looking at splicing variation impt for brain function in etiology for psychiatric illness. Cell types, tissues, individual var #ASHG14
2:05pm October 20th 2014 via Hootsuite
Andrea Byrnes (MGH): Multi-sample isoform quantification from RNA-seq #ASHG14
2:04pm October 20th 2014 via Hootsuite
MG:Q:What's the relevance for alt transcripts? A:Working on meaning, 'maybe we'll get there' #ASHG14
2:03pm October 20th 2014 via Hootsuite
MG: URL for GENCODE website: http://t.co/pen3paRs0h #ASHG14
2:01pm October 20th 2014 via Hootsuite
MG: 'Transcript level quantification unreliable': the dominant transcript from two tools 2013 Genome Biol http://t.co/RXMYNe6hPR #ASHG14
2:00pm October 20th 2014 via Hootsuite
MG: Transcript complexity makes calling var consequence more difficult. Simplifying Gencode: reducing set of xcrpts to only coding #ASHG14
1:57pm October 20th 2014 via Hootsuite
MG:Consistently annotates CDSes; reference 2013 Science http://t.co/curbQCuuc8 Genes Dev '11 paper splice fig http://t.co/pdXaV5oVDP #ASHG14
1:56pm October 20th 2014 via Hootsuite
MG: GENCODE covers 1.3% vs RefSeq NM/XM 1% of genome; chart of RNA-Seq read count of unique exons and introns are similar for both #ASHG14
1:53pm October 20th 2014 via Hootsuite
MG:GENCODE has a lot more genes 20 vs. 8 for the example; 7.6 transcripts vs. 3.4 for RefSeq per gene #ASHG14
1:52pm October 20th 2014 via Hootsuite
MG:Manual assisted by UCSC, Yale, CNIO, CRG: feeding in hints, experimental data to help. GENCODE & RefSeq are different. SCL25A17 #ASHG
1:51pm October 20th 2014 via Hootsuite
MG: GENCODE gene annotation - combination of HAVANA, Vega, Sanger teams (manual), and Ensembl computational one #ASHG14
1:50pm October 20th 2014 via Hootsuite
Mark Gerstein substituting for AF: Consider the geneset: Why the transcripts used for variant annotation matter #ASHG14
1:49pm October 20th 2014 via Hootsuite
Adam Frankish (Wellcome Trust): Consider the geneset: Why the transcripts used for variant annotation matter #ASHG14
1:48pm October 20th 2014 via Hootsuite
RT @markgenome: Great to see David Valle honored w McKusick Award at #ASHG14. Wrote a profile of him 3 years ago: http://t.co/Q52JBjBjbX
1:30pm October 20th 2014 via Hootsuite
RT @cgonzagaj: I enjoy these Award talks. History and advancements in genetics that we know from papers and text books more personal #ASHG14
1:15pm October 20th 2014 via Hootsuite
SO: (Work published in Science 2013: http://t.co/HehA0acgxa ) Now proceeding with stem cells, then genome editing & reintroduced #ASHG14
1:00pm October 20th 2014 via Hootsuite
SO: Sickle cell disease: from GWAS the BCL11a gene ID, removal of enhancer region via TALENs, req'd in erythroid but not B-cells #ASHG14
12:57pm October 20th 2014 via Hootsuite
SO: GATA1 driver for erythroid lineage development. 'A window into diverse biology and disease'. All steps of hematopoiesis #ASHG14
12:52pm October 20th 2014 via Hootsuite
SO: In '86: gene cloned for chronic granulomatous without PCR and rudimentary sequencing methods. Then: GATA1 xcr factor #ASHG14
12:50pm October 20th 2014 via Hootsuite
SO: First beta-thalassemia, discovering all the defects, facilitating pre-natal dx in Sardinia by DNA #ASHG14
12:49pm October 20th 2014 via Hootsuite
SO: Back at BCH D Nathan gave oppy to start a lab. Lived through new fields - from recomb DNA to genome eng #ASHG14
12:48pm October 20th 2014 via Hootsuite
SO: Shows photo of NIH Bldg 6: with Phil Leder, learned to purify Eco RI, doing molecular biology 'without a kit' #ASHG14
12:46pm October 20th 2014 via Hootsuite
SO: 'An MD is enough for Kornberg, so one degree is enough for you' (Harvard) #ASHG14
12:45pm October 20th 2014 via Hootsuite
Stuart Orkin: First exposure was Salvador Luria at MIT in the 'early days'. Most were physics and engineering; few biologists then #ASHG14
HK: Worked on beta-thalassemia; first positional cloning; '89 found first transcriptional regulator of hematopoeisis #ASHG14
12:41pm October 20th 2014 via Hootsuite
HK: '72 after HMS, went to NIH working with Phil Leder, learning pre-cloning molecular biology. Then back to Boston Children's #ASHG14
12:39pm October 20th 2014 via Hootsuite
Haig Kazazian introduces Stuart Orkin (Harvard) William Allan Award Presentation. A chemistry teacher inspired him to go to MIT #ASHG14
12:37pm October 20th 2014 via Hootsuite
MD: Privileged to work at the cutting edge of science. 'It is on us to deliver on the promise of genetics for this generation' #ASHG14
12:35pm October 20th 2014 via Hootsuite
MD: From genetics to therapies: our training responsibilities, a skilled workforce 'as expert as possible' #ASHG14
12:34pm October 20th 2014 via Hootsuite
KK: False connections between mutations and disease: "We as a community and as a society need to be proactive in these issues" #ASHG14
12:33pm October 20th 2014 via Hootsuite
MD: Applying 1980's style analysis ideas to current genome-wide studies; large schizophrenia study from the summer. #ASHG14
12:31pm October 20th 2014 via Hootsuite
MD: Fundamental changes: from HGP, to microarrays and HapMap through NGS, now the numerical realities of complex diseases #ASHG14
12:29pm October 20th 2014 via Hootsuite
MD: Many credited as mentors 'when there was nothing in it for them'. Many, many photos. #ASHG14
12:28pm October 20th 2014 via Hootsuite
MD: History: software and algorithms dev as an undergrad (Lander, Kruglyak); applied to IBD later; human var & HapMap (Altschuler) #ASHG
12:27pm October 20th 2014 via Hootsuite
MD: Start with MIT Tech Rev cover: "You promised me Mars colonies, instead I got Facebook" Joined Lander when he was just starting #ASHG14
12:24pm October 20th 2014 via Hootsuite
