Max van Min (Cergentis B.V.) "Targeted locus amplification for hypothesis neutral NGS and haplotyping of selected genomic loci" #ASHG14
7:48pm October 21st 2014 via Hootsuite
ME: Eval of WGA methods: showed missing data from single-cells. Their 'interpreted' single-cell haplotypes use B-allele freq #ASHG14
7:35pm October 21st 2014 via Hootsuite
ME: Reconstruction of haplotypes across multiple loci. WGA introduces drop-outs, pref amplification, allele drop-in, also chimeras #ASHG14
7:34pm October 21st 2014 via Hootsuite
We have the Technology: Next-gen Genomic Methods Hall B1 Masoud Esteki - (KU Leuven) Whole-genome single-cell haplotyping for PGD #ASHG14 \
7:31pm October 21st 2014 via Hootsuite
New post: Comparing Benchtop Sequencers as Technologies Mature | Behind the Bench http://t.co/jjZMWaCzMt
7:26pm October 21st 2014 via Hootsuite
RT @CancerGeek: MT: Great post--> @Inc: Why Social Media Is All About the Scorpion, Not the Frog @tullman http://t.co/aJkmDcHJnl
6:30pm October 21st 2014 via Hootsuite
DF:Data suggests post-integration insertion; in tissue culture, observing de novo L1 insertions in these Tn-free regions #ASHG14
2:55pm October 21st 2014 via Hootsuite
DF: Sequence context is AT-rich near insertion sites.Tn-free regions (about 2% of genome) ref: http://t.co/smA7hzXsrv #ASHG14
2:54pm October 21st 2014 via Hootsuite
DF: Larger chromosome = more insertions. Degenerate consensus cleavage site: TTTTAA is the consensus across all four cell types #ASHG14
2:52pm October 21st 2014 via Hootsuite
DF: Using @PacBio reads to unambiguously det. site of integration in four cell types; HeLa, PA-1, NPC, hESC. CCS about 500bp reads #ASHG14
2:51pm October 21st 2014 via Hootsuite
DF: 1000's of events examined in hESC, also cells derived from hESC figure from Levin review paper http://t.co/oClJwbcfhk #ASHG14
2:49pm October 21st 2014 via Hootsuite
DF: Then target-site primer reverse-transcription. L1 endonucl cut - what determines site of integration? #ASHG14
2:47pm October 21st 2014 via Hootsuite
DF: Tn-elements account for 45% of the human genome. LINE1 retrotransposon structure: copy&paste struct. Xcr, transl of prots, RNP #ASHG
2:46pm October 21st 2014 via Hootsuite
Diane Flasch (UMich) High-throughput Determination of Long INterspersed Element-1 Integration Preferences in the Human Genome #ASHG14
2:45pm October 21st 2014 via Hootsuite
JB:Q:Take into acct changes in whole blood composition, other health effects? A:Didn't look at cell type, excluded sick indivs #ASHG14
2:41pm October 21st 2014 via Hootsuite
JB: 60yo, twin registry, 105 indivs, looking at heritability of gene exp. 63 genes gain heritability over time, 719 lost #ASHG14
2:33pm October 21st 2014 via Hootsuite
J Bryois (Univ Geneva) Longitudinal Study Of Whole Blood Transcriptomes In a Twin Cohort #ASHG14
2:31pm October 21st 2014 via Hootsuite
MS:Q:Was blood composition measured? A:Yes, not shown; cell populations return back quickly. Cy-TOF analysis in progress #ASHG14
2:30pm October 21st 2014 via Hootsuite
MS: Getting responses to perturbations. Concl: do-able, learn many things at the biochemical level #ASHG14
2:27pm October 21st 2014 via Hootsuite
MS: Weight gain: inflammation; weight loss 'reverses some of this'. Firmicutes change in Ins sens group after wt loss #ASHG14
MS: Then weight loss. 10 insulin res, 10 insulin sens. First results: clear differences in changes between res/sens groups #ASHG14
2:26pm October 21st 2014 via Hootsuite
MS: 1y ago - ~70 prediabetics enrolled; similar profiling, many measurements. One subset: high BMI then put on a high calorie diet #ASHG14
2:24pm October 21st 2014 via Hootsuite
MS: Stool distribution: 'it went berzerk' under fever. A system-wide change, system-wide measurement #ASHG14
2:23pm October 21st 2014 via Hootsuite
MS: A viral inf., microbiome from nasal cp. healthy to fever to recovery. Strep. pneumon. 'the world's most $ way to figure out" #ASHG14
2:22pm October 21st 2014 via Hootsuite
MS: Adding DNA methylome and microbiome are important additions. 2.5y ago introduced, not fully analyzed. #ASHG14
2:21pm October 21st 2014 via Hootsuite
MS: His detailed 'omics profiling: proteome, transcriptome, metabolome. Saw changes in biochemical levels. #ASHG14
2:20pm October 21st 2014 via Hootsuite
MS: Got T2D concurrent to an RSV infection. Glucose level shot up to 6.7. Risk factor ID from genome sequence prior. #ASHG14
2:19pm October 21st 2014 via Hootsuite
MS:Health is a product of genome and exposure. 54 months, 82 timepoints, 6 viral infections 2012 Cell paper http://t.co/QWsbczjpUj #ASHG14
2:18pm October 21st 2014 via Hootsuite
Mike Snyder (Stanford) Dynamics Personal Omics Profiles During Periods of Health, Disease, Weight Gain and Loss #ASHG14
2:14pm October 21st 2014 via Hootsuite
AV:Q:Any gender bias? A:Current study is all female #ASHG14
2:13pm October 21st 2014 via Hootsuite
AV:Q:Could skin be affected by sun exp? A:Taken from abdominal area, to minimize effect #ASHG14
2:12pm October 21st 2014 via Hootsuite
AV: EuroBATS webste: http://t.co/RyDB4fpOoV #ASHG14
AV: 3 possible models tested: data suggests age affecting exp by methylation #ASHG14
2:10pm October 21st 2014 via Hootsuite
RT @claritas4kids: #ASHG14 KR: Most of time, don't know breakpoints or orientation for dupls, breakpoint seq 184 dupls from 170 subjects
2:09pm October 21st 2014 via Hootsuite
RT @claritas4kids: #ASHG14 TK: ~150 born in US with de novo balanced chromosome rearrangements, 20% will have health issues
2:08pm October 21st 2014 via Hootsuite
AV:Could it be explained by methylation w/age? Looked at 39k CpGs ass'd with age, using EuroBAT datasets #ASHG14
AV: Genotype-by-age interactions: QTL of young vs old. Found 1: CD82, metastasis-supp gene. (Difficult to detect.) #ASHG14
2:06pm October 21st 2014 via Hootsuite
AV: Cp genes in fat, skin blood: only 6 genes in common with age, of 4.3K genes in skin that change w/age; APOE alt splicing also #ASHG14
2:04pm October 21st 2014 via Hootsuite
Ana Viñuela (UCL) Analysis of the Genetic Variation and Age Effects on Gene Expression Using RNA-seq Data from Multiple Tissues #ASHG14
2:01pm October 21st 2014 via Hootsuite
IC:Q:Plan to recontact families due to inc recurr risk? A:No clearance to do so but considering. Parent of origin impt. #ASHG14
1:59pm October 21st 2014 via Hootsuite
RT @LifeTech: Some news about NGS accuracy just in time for #ASHG14 http://t.co/Fv3eOxSqIS http://t.co/abdWYWkRcY
1:57pm October 21st 2014 via Hootsuite
IC: Concl: "Post-zygotic mutagenesis is an impt and unrecognized source of human mutations" #ASHG14
IC: Parents who are somatically mosaic ~2x more likely to transmit to children than germline. #ASHG14
1:56pm October 21st 2014 via Hootsuite
IC:Sanger sequence confirmed each in the parent. Primordial germ cell lineage and hematopoeisis. Website: http://t.co/YiQGGpPVWQ #ASHG14
1:55pm October 21st 2014 via Hootsuite
IC:Designed a custom aCGH, then PCR for deletions; tested 100 family trios via PBCs. 4 families - deletion in proband, also 1 parent #ASHG14
1:52pm October 21st 2014 via Hootsuite
IC: Describes their method for PCR detection of a deletion, explaining Smith-Magenis syndrome Recent publ: http://t.co/sa19pxpVjW #ASHG14
1:50pm October 21st 2014 via Hootsuite
IC: To adulthood: 10^16 mitoses, about the same (in meters) to Alpha Centari. Mutations during replication results in a mosaic #ASHG14
1:48pm October 21st 2014 via Hootsuite
Ian Campbell (Baylor) Parental somatic mosaicism contributes an under-recognized source of potentially recurrent new mutations #ASHG14
1:47pm October 21st 2014 via Hootsuite
KY:Q:Evidence of mosaicism? A:Yes, some where read-counts not at 50% #ASHG14
1:46pm October 21st 2014 via Hootsuite
KY:Q:Were all SVs validated? Rate low? A:Yes, wanted high sens., then long process of validation #ASHG14
