Cuppen: Illustrated 4 lost fragments, many 'variants'. Family-based (trio) molecular phenotyping. iPS cells, ChIP, RNA, 4C... #ESHG14
11:41am June 1st 2014 via Hootsuite
Cuppen: 23 breakpoint junctions across 5 chromosomes? Does it drive congenital disease phenotypes? Event was in 'normal' mother #ESHG14
11:40am June 1st 2014 via Hootsuite
Next: Edward Cuppen (Hubrecht Inst Utrecht Netherlands) "Chromotrypsis" #ESHG14
11:39am June 1st 2014 via Hootsuite
N. Robinson in analysis of Exome sequencing: "Bioinformatics... still doesn’t work for genetics without an expert” #ESHG14
11:37am June 1st 2014 via Hootsuite
#ESHG14 Poster: NGS to Sanger Sequencing, Synchronizing Variant Files - YouTube http://t.co/uHLegsrVJZ
8:49am June 1st 2014 via Hootsuite
.@Clarksearch Certainly - I don't use Tweetdeck only because when I tested it, it didn't have what Hootsuite did (i.e. Firefox add-on)
8:47am June 1st 2014 via Hootsuite in reply to
.@Clarksearch I think you should give #ESHG14 and #ASCO14 search panels on a tool like HootSuite a try.
7:17am June 1st 2014 via Hootsuite in reply to
#ESHG14 Today at 11:45-13:45 in Amber 7&8 "Application of digital Next Generation Sequencing in clinical research "
4:45am June 1st 2014 via Hootsuite
Marioni: Th2 differentiation genes against cell cycle genes: 200 genes overlap #ESHG14
4:00am June 1st 2014 via Hootsuite
Marioni: Plot of distribution of non-technical variance by cell cycle and biol variation; showed enrichment for T-cell spec genes #ESHG14
3:57am June 1st 2014 via Hootsuite
Marioni: Used 300 mouse ES cells, 96 at G1, S and G2M phases, looked at correlation by cell cycle #ESHG14
3:55am June 1st 2014 via Hootsuite
Marioni: Used prior model of noise, along with ERCC spike-ins: have a cell cycle, biol var, and technical noise components #ESHG14
3:54am June 1st 2014 via Hootsuite
Marioni: Single-cell latent variable model (like a GWAS with confounding pop. effects). Correcting for cell cycle genes instead #ESHG14
3:52am June 1st 2014 via Hootsuite
Marioni: Infer cell cycle 'effect' of observe expression profile. 549 genes ass'd to cell cycle; 65224 genes not annotated #ESHG14
3:51am June 1st 2014 via Hootsuite
Marioni: Th2: confounding factors is cell-cycle related genes. Each cell is a different stages of cell cycle and expression level. #ESHG14
Marioni: His Nature Methods 2013 paper laying out these details about technical noise: http://t.co/gdxS0EUjjZ #ESHG14
3:50am June 1st 2014 via Hootsuite
Marioni: Mentions ERCC controls (available from @LIFECorporation ), looking at T-cell Th2 differentiation with the C1 Fludigm #ESHG14
3:49am June 1st 2014 via Hootsuite
Marioni: Fit data by a quadratic equation; a 'typical' gene at 1000 reads: cv2 of 0.2 #ESHG14
3:46am June 1st 2014 via Hootsuite
Marioni: In Thaliana - more spread; v / sq of mean; plot against ave normalized read count; more var with more expression #ESHG14
3:45am June 1st 2014 via Hootsuite
Marioni: Goal - to ID technical noise. Took A. thaliana 13 cells, spiked with 50pg of HeLa RNA. Rocket plot spreads at ~100 counts #ESHG14
3:44am June 1st 2014 via Hootsuite
Marioni: 10K reads to zero from 'single cell to single cell' equivalent #ESHG14
3:42am June 1st 2014 via Hootsuite
Marioni: This was bulk RNA diluted to 50pg and 5pg level, then standard single-cell RT and amplification #ESHG14
Marioni: 2 biol replicates of bulk RNA-Seq 'rocket plot' - typical result; showing dilution from Arabidopsis; 50pg and 5pg ugly #ESHG14
3:41am June 1st 2014 via Hootsuite
Marioni: 50pg of RNA, even lysing is a source of noise - use RT + univ primers (more noise) and 20 rounds ampl (more noise) #ESHG14
3:40am June 1st 2014 via Hootsuite
Marioni: Modelling - and technical noise a sig challenge in single cells #ESHG14
Marioni: Pioneer (Surani) early 2009 work (on SOLiD) Nature Methods http://t.co/1JZYz0gxaq #ESHG14
3:39am June 1st 2014 via Hootsuite
Marioni: Bulk are fine for many cases; but for development, for cancer - need single-cell resolution #ESHG14
3:38am June 1st 2014 via Hootsuite
John Marioni (EBI, Wellcome Trust Cambridge UK) “Computational challenges in single-cell transcriptomics” #ESHG14
3:37am June 1st 2014 via Hootsuite
RT @Andrea_Pillmann: Most frequently cited papers in 50 years of Human Genetics: http://t.co/2Y5oruvEGM Free to read. #ESHG14
3:33am June 1st 2014 via Hootsuite
Georges: Working on a 'burden test' with 3K cases / controls, and plan to double it #ESHG14
3:30am June 1st 2014 via Hootsuite
Georges: Another: "Burden Test" - mutational load from cases/controls. "Low power: use it well". But need v.large datasets #ESHG
3:28am June 1st 2014 via Hootsuite
Georges: Useful to come up with 'formal methods to discover a causative gene': A reciprocal hemizygosity test, alas not for humans #ESHG
3:24am June 1st 2014 via Hootsuite
Georges: Counting nsSNVs: for Crohns, majority of non-synonymous is significant (at a given p-cutoff) #ESHG
3:22am June 1st 2014 via Hootsuite
Georges: >50% have less than 5 variants per cluster "a remarkable achievement" Thus: 60% the causative marker ID'd at present #ESHG
3:20am June 1st 2014 via Hootsuite
Georges: Over 60% of the loci have 1 cluster, but range 1-15 (but conservative params chosen) ave 1.4 cl/locus; 134 clusters total #ESHG
3:19am June 1st 2014 via Hootsuite
.@pathogenomenick That's a funny post (prob unintentional) from Seqonomics. Oh well, they just ignore Helicos and PacBio...
3:17am June 1st 2014 via Hootsuite in reply to
Georges: 4th method multivariate regression of p<10^-6. Example of 'Results 1 NOD2' single variant test across 4 methods #ESHG
3:14am June 1st 2014 via Hootsuite
Georges: Other models - Metropolis-Hastings method ("MCMC"), a chaining of possibilities to fit data; also Bayesian method #ESHG
3:12am June 1st 2014 via Hootsuite
Georges: A statistical test of each marker. (NB: Dr. Georges has a great, precise and clear teaching style!) #ESHG
3:11am June 1st 2014 via Hootsuite
Georges: Many risk loci are in high LD; threshold r^2 0.9; reconstruction of haplotype history based upon mutations #ESHG
3:10am June 1st 2014 via Hootsuite
Georges: Use of Immunochip dataset (18K Crohns, many 10's of K UC and other); Broad uses a forward selection (biggest effect) #ESHG
3:08am June 1st 2014 via Hootsuite
Georges: 'Top' SNP ('sentinel') may not be causative; can have secondary effects etc. Try a systematic, 'conditional analysis' #ESHG
3:07am June 1st 2014 via Hootsuite
Georges: In IBD, >160 WGAS-identified Crohn's risk loci. Jostins 2012 Nature http://t.co/7KKefP7RLg #ESHG14
3:05am June 1st 2014 via Hootsuite
Next: Michel Georges (University of Liège) “Control of gene expression in disease” #ESHG
3:02am June 1st 2014 via Hootsuite
Deplancke: Credits to Manolis Dermitzakis (Univ Geneva), Sebastian Waszak in his lab. #ESHG14
2:56am June 1st 2014 via Hootsuite
Deplancke: Refers to Bing Ren's work (UCSD) about chromatin topology suggested 2013 Nature pub http://t.co/xUZPOAwl8M #ESHG14
2:55am June 1st 2014 via Hootsuite
Deplancke: VMM's show strong co-variability; do they reflect chromatin topology? #ESHG14
2:53am June 1st 2014 via Hootsuite
Deplancke: Example of UGTB17 gene locus - where one binding allele is deleted, explaining low VMM activity #ESHG14
2:52am June 1st 2014 via Hootsuite
Deplancke: PU.1 binding event variability - believe that it is the VMM rather than the minority that have variation in the PU.1 site #ESHG14
2:50am June 1st 2014 via Hootsuite
Deplancke Detail around the PU.1 motif and allele-specific binding patterns when disrupted - but how when site not variable? #ESHG14
2:49am June 1st 2014 via Hootsuite
