Deplancke Referred to recent pub (Science Nov 2013: http://t.co/1V91O7zcMH ) Classify heterozygote SNPs with PU.1 motifs #ESHG14
2:46am June 1st 2014 via Hootsuite
Deplancke: 50% of QTLs are >10kb from mark; 33% of PU.1 QTLs are local; QTLs w/in the mark, the greater the effect #ESHG14
2:44am June 1st 2014 via Hootsuite
Deplancke: Observed remarkable variation of VMMs - looked at QTL's within 250kb of the variable peak; relatively low 2% QTLs #ESHG14
2:43am June 1st 2014 via Hootsuite
Deplancke: Related to gene exp variation: RNApol II correlates with the gene; but majority of VMMs not ass'd with gene exp. #ESHG14
2:41am June 1st 2014 via Hootsuite
Deplancke: Chr21 APP genes - 275kb, 23 peaks, 5 molecular phenotypes; individual cell lines observe coord. global chromatin activity #ESHG14
2:39am June 1st 2014 via Hootsuite
Deplancke: Strong enrichment of var enhancers; >90% VMMs have at least 1 H3K27Ac peak - importance of that mark #ESHG14
2:38am June 1st 2014 via Hootsuite
Deplancke: 5 sites (RBP2, H3K4me3 etc) across Chr21 example - from 78k phen-phen ass'ns, 14.5K VMMs #ESHG14
2:37am June 1st 2014 via Hootsuite
Deplancke: This graph is binary relationships - but what about higher-order complexity? Variable Molecular Models (VMMs) #ESHG14
2:36am June 1st 2014 via Hootsuite
Deplancke: Distances up to 1MB apart - H3K4me3-RPB2 100bp away - but many over 'long distances' 100kb-500kb. #ESHG14
2:35am June 1st 2014 via Hootsuite
Deplancke: Across 47 lymphoblastoid lines: looking at H3K27ac marks against H3K4m1, PU.1 etc. 2.7M pairwise comparisons #ESHG14
2:34am June 1st 2014 via Hootsuite
First up: Bart Deplankce Univ Luasanne Variation and genetic control of chromatin in humans #ESHG14
2:32am June 1st 2014 via Hootsuite
.@LIFECorporation #ESHG14 Enjoyed D Smedley’s talk on prioritizing exome variants via model organisms - a new Exomiser tool
1:50am June 1st 2014 via Hootsuite in reply to
.@LIFECorporation Really enjoyed D Smedley’s talk on prioritizing exome variants through model organisms - a new tool called Exomiser.
1:18am June 1st 2014 via Hootsuite in reply to
RT @LIFECorporation: #ESHG14 Poster: Development and verification of an @IonTorrent AmpliSeq TP53 Panel http://t.co/S2POTVbtpS
1:15am June 1st 2014 via Hootsuite in reply to
RT @LIFECorporation: Editor-in-Chief @DaleYuzuki introduces 'Behind the Bench' A blog focused on #genomics, #sequencing, #PCR, #NGS...
8:45pm May 31st 2014 via Hootsuite
Smedley: 'Clearly shows the value of collecting comprehensive clinical phenotypic data' in exome sequencing research #ESHG14
2:02pm May 31st 2014 via Hootsuite
Smedley: Applied to the NIH Undiagnosed Disease Program: Found all in the top 10; 40% it was the top hit #ESHG14
2:01pm May 31st 2014 via Hootsuite
Smedley: Showed results from Exomiser tool. Their v2 in beta: includes OMIM and Orphanet along with protein-protein interactors #ESHG14
1:59pm May 31st 2014 via Hootsuite
Smedley: Are able to back-test their model with known pathogenic genes; made available here: http://t.co/ix0HgDSWXX #ESHG14
1:58pm May 31st 2014 via Hootsuite
Smedley: Cross-species phenotype comparison: "Phenotypic interpretation of variants in exomes" PubMed http://t.co/VjWq1ZxEua #ESHG14
1:55pm May 31st 2014 via Hootsuite
Smedley: Another method - using model organisms. Looking at mouse & zebrafish phenotype data PubMed: http://t.co/gbPaJU3vND #ESHG14
1:53pm May 31st 2014 via Hootsuite
Smedley: Looking at rare diseases via WES but 10's or 100's that are pathogenic & rare. Use linkage, trios, de novos from trios. #ESHG14
1:51pm May 31st 2014 via Hootsuite
Next: Damian Smedley (EBI, Cambridge UK) “Strategies for Exome Prioritization of Human Disease Genes” #ESHG14
1:50pm May 31st 2014 via Hootsuite
Meyn: They've enrolled 174 individuals to-date (first in Canada); looking at psychosocial impact, PGx, economic analyses #ESHG14
1:46pm May 31st 2014 via Hootsuite
Meyn: Use PhenoTips for phenotyping individuals (PubMed: http://t.co/i3kqRccN1J ) they use Complete Genomics for WGS. #ESHG14
1:43pm May 31st 2014 via Hootsuite
Meyn: Recently published their guidelines here PubMed: http://t.co/a4yO7ARujq #ESHG14
1:37pm May 31st 2014 via Hootsuite
Stephen Meyn (Hospital for Sick Children, Toronto Canada): "The SickKids Genome Clinic: Developing and evaluating a pediatric model for...
1:36pm May 31st 2014 via Hootsuite
Loviglio: Implied as well with Autism, found 'significant enrichment' both via differential gene exp as well as loop data #ESHG14
1:31pm May 31st 2014 via Hootsuite
Loviglio: Data suggested chromatin organization disrupted in a major way ("BRICKS") with the 600kb deletion / amplification #ESHG14
1:28pm May 31st 2014 via Hootsuite
Loviglio: They had 600kb duplication and 600kb deletion lines - 2 of each - and normals. Showed interaction in cis 1MB away #ESHG14
1:24pm May 31st 2014 via Hootsuite
Loviglio: Idea: higher-order chromatin structure, via 3C methods. They used 4C-Seq based method #ESHG14
1:21pm May 31st 2014 via Hootsuite
Loviglio: 600kb duplication underweight, deletion obesity; also macrocephaly / microcephaly symmetry in 16p11.2 region #ESHG14
1:20pm May 31st 2014 via Hootsuite
Next: Maria Nicla Loviglio (Lausanne Switzerland): “Chromatin loops and CNVs: the complex spatial organization of the 16p11.2 locus” #ESHG14
1:18pm May 31st 2014 via Hootsuite
Cao: They are seeing replications duplications 'everywhere'. 8% of the human genome viral? 'Viral integration sites in host genome' #ESHG14
1:17pm May 31st 2014 via Hootsuite
Cao: Showed human mapping; 'now the contiguity is near' cp PacBio v1, existing hg19, theirs #ESHG14
1:14pm May 31st 2014 via Hootsuite
Cao: Also showed a loss of one allele; saw 200kb deletion one allele, 70kb deletion other allele. Showed 4x90kb amplification #ESHG14
1:12pm May 31st 2014 via Hootsuite
Cao: Taking care of the sample - careful pipetting, showing 9kb vs 13kb difference to hg19 (insertion); uses redundant image info #ESHG14
1:10pm May 31st 2014 via Hootsuite
Cao: Shows the irysChip, video of 7base seq motif labeled, can use chemical moiety; 100k - 1MB; stretch in a nanochannel. #ESHG14
1:08pm May 31st 2014 via Hootsuite
Cao: Complementary tech. ("Blind spot" is put in Chinese Hanzi on slide - perhaps for a different audience?) #ESHG14
1:05pm May 31st 2014 via Hootsuite
Cao: 'Irys eliminates the genomic blind spot with long range info'. 200-500bp model of existing NGS. Bionano - minimal sample proc. #ESHG14
1:04pm May 31st 2014 via Hootsuite
Next: Han Cao (Bionanogenomics, San Diego USA) “Identification and Analysis of Complex Structural Variation Using Nanochannel Array” #ESHG14
1:01pm May 31st 2014 via Hootsuite
Wijmenga: eQTLs can help prioritize risk genes in autoimmune disorders #ESHG14
Wijmenga: Looking at trans-eQTLs: SNP is on chr4, but in trans affects a gene on Chr1 and Chr6. Both B-cell reg genes #ESHG14
12:57pm May 31st 2014 via Hootsuite
Wijmenga: lncRNAs: >200nt, no ORF, intronic, can be antisense, overlap ORF, can modify chromatin. 'Fn of most lncRNAs are unknown' #ESHG1
12:56pm May 31st 2014 via Hootsuite
Wijmenga: Illust. one risk SNP up-regulates one downstream gene but down-regulates three other upstream genes #ESHG14
12:55pm May 31st 2014 via Hootsuite
Wijmenga: 50% of the cis-eQTL effect is to a single gene. As many as 6 other genes for other cis-eQTLs. #ESHG14
Wijmenga: Found cis-eQTLs vary from 14% - 60%. 1/4 are on n.c. genes. Likely underestimated: these ncRNA are lower exp, tissue-spec #ESHG14
12:53pm May 31st 2014 via Hootsuite
Wijmenga: 14 autoimmune diseases; selected 543 risk SNPs; P<5x10^-8; had RNA-seq on 629 PBMCs from 'Lifelines Deep' cohort, others #ESHG1
12:51pm May 31st 2014 via Hootsuite
Wijmenga: ILMN Immunochip: a common subset for autoimmune disease-related SNPs. Found non-coding RNA genes enrichmed #ESHG14
12:50pm May 31st 2014 via Hootsuite
Wijmenga: 70 GWAS studies, 32 autoimmune diseases; 478 loci identified. Overlap with others. 2014 review: http://t.co/9gSLS6d8Vk #ESHG14
12:49pm May 31st 2014 via Hootsuite
