Carvalho: Also subtypes of Br ca - ER+, HER+, ER/PR+, TNBC by methylation pattern. Also Lung ca - FLT3-ITD status by methylation (!) #AGBT17
9:23am February 14th 2017 via Hootsuite
Carvalho: Now actively working on being able to classify tissue of origin by methylation pattern in cfDNA #AGBT17
9:22am February 14th 2017 via Hootsuite
Carvalho: Shows methylation profile from this '14 ref https://t.co/418txoazUz but methyl arrays are underpowered as done by TCGA #AGBT17
9:21am February 14th 2017 via Hootsuite
Carvalho: Looks at hyper- and hypo-methylated sites, infer active transcriptional factor networks #AGBT17
9:19am February 14th 2017 via Hootsuite
RT @melaniesinche: Check it out--@jacksonlab is hiring at #agbt17! https://t.co/2UzawiF1D5
9:17am February 14th 2017 via Hootsuite
Carvalho: Training set uses a few thousand DMRs, shows 100% ROC; Test set of 35 cases/controls #AGBT17
Carvalho: 38K DMRs (differentially methylated regions) - shows data Cp methlyation profile in normal/tumor tissue vs plasma #AGBT17
9:15am February 14th 2017 via Hootsuite
Carvalho: Used PDx (pt-derived xenograft) model to demonstrate enrichment of ctDNA (2x from 1% to 2% detected) #AGBT17
9:13am February 14th 2017 via Hootsuite
RT @mason_lab: Cell free methylation DNA Immunoprecipitation (cfMeDIP-seq) from D De Carvahlho, similar to https://t.co/FRCkhbuS3S #agbt17
9:12am February 14th 2017 via Hootsuite
Carvalho: Cp their method against RRBS of cfDNA; by interrogating more features can exceed limitations of barcoding #AGBT17
Carvalho: Challenges - low amt. Doing a version of MeDIP - pull-down of methylated DNA. Add 'filler' lambda DNA to up the input #AGBT17
9:10am February 14th 2017 via Hootsuite
Carvalho: Shows unpub data of sens (probability of det) against LoD - down to 0.01% as a function of depth (theoretical calc) #AGBT17
9:08am February 14th 2017 via Hootsuite
Carvalho: At ASCO2016, Pathway genomics indicates 6/42 stg I had one pos mutation above LoD #AGBT17
9:06am February 14th 2017 via Hootsuite
Carvalho: Detection needs to go down way below 1%. (Note: @SeraCare has reference materials: https://t.co/YjV6rUwPQW ) #AGBT17
Carvalho: Into using fig from '13 https://t.co/AGX5kOpvhM to describe liquid biopsy ctDNA detection #AGBT17
9:04am February 14th 2017 via Hootsuite
Daniel De Carvalho (Princess Margaret Cancer, CAN) Highly sensitive tumor detection, classification using plasma DNA methylome #AGBT17
9:02am February 14th 2017 via Hootsuite
Hey #agbt17 you need to check out @QIAGEN in 313 - not bad! (See photo.) https://t.co/dayyQLt9N0
7:36am February 14th 2017 via Hootsuite
Also great to meet new people from @bcmhouston @ChildrensMercy @AgilentGenomics at #AGBT17 (you know who you are)
6:34am February 14th 2017 via Hootsuite
Great to see @RocheSequencing as well as meet former and current @AppliedBio @iontorrent and @illumina developers last night at #AGBT17
6:31am February 14th 2017 via Hootsuite
Hi #AGBT17 @masonlab will do an interpretive dance before Thursday's 230pm talk if this gets to 100 RT's. @coregenomics @DeciBio
6:26am February 14th 2017 via Hootsuite
Q: How much missing data in s.c. data? Greenleaf: Aggregates across lots of sites in the same cell, getting around drop-out prob #AGBT17
7:14pm February 13th 2017 via Hootsuite
Q: GWAS connection? Greenleaf: TF data, is there larger GWAS hits for association? #AGBT17
7:13pm February 13th 2017 via Hootsuite
Greenleaf: Can ID transitional and re-activatable regulatory elements - some complex axes along w/colors for 3rd dimension for mark #AGBT17
7:12pm February 13th 2017 via Hootsuite
RT @ScienceJill: .@WJGreenleaf Hematopoiesis Rorschach Test -> Do you see the "manta ray"? #ATACseq #agbt17 https://t.co/c1sdgJilQS
7:08pm February 13th 2017 via Hootsuite
Greenleaf: Looks at cis-acting elements - accessibility, looking at regions that correlate to open-ness. Showing CCC vs ATAC-seq #AGBT17
7:01pm February 13th 2017 via Hootsuite
Greenleaf: Able to show Nanog in eSCs, single-cell variability defining celltypes, revealing trans-factor noise in single-cells #AGBT17
6:59pm February 13th 2017 via Hootsuite
Greenleaf: Shows dynamic range of 0 to 2: representing two copies of DNA in a single-cell. #AGBT17
6:57pm February 13th 2017 via Hootsuite
Greenleaf: Down to single-cell: due to TFs can vary dramatically. Collaborating with @fluidigm '15 ref https://t.co/gKIYlZbagf #AGBT17
6:56pm February 13th 2017 via Hootsuite
Greenleaf: Recent work in Nature Gen https://t.co/ZrYEqu5wdJ in mouse eSCs #AGBT17
6:53pm February 13th 2017 via Hootsuite
Greenleaf: Regions of accessibility; positioning nucleosomes; can infer transcription factor presence; can call chromatin state #AGBT17
6:52pm February 13th 2017 via Hootsuite
Greenleaf: The regulome: if 'on', accessible; if 'off', inaccess. Assay of Transposase Accessible Chromatin https://t.co/GciDyUUwnS #AGBT17
6:51pm February 13th 2017 via Hootsuite
Greenleaf: Shows Waddington's 1957 image https://t.co/AY1u4nHuYE of development. 3m into 5um via folding. #AGBT17
6:49pm February 13th 2017 via Hootsuite
William Greenleaf (Stanford Univ CA) ATAC-in single-cell regulatory variation #AGBT17
6:47pm February 13th 2017 via Hootsuite
Churchman: Yeast ribo profiling work '09 Science ref https://t.co/jN3MIlGuTA Sees rapid and dynamic regulation of xlation in mito #AGBT17
6:41pm February 13th 2017 via Hootsuite
Churchman: Nucl, mito have orthogonal mechanisms, orthogonal machinery. In yeast: looking at ribosome profiling cross-compartments #AGBT17
6:40pm February 13th 2017 via Hootsuite
Churchman: Onto nuclear and mitochondrial genomes: 10 genes vs ~10K genes that are transcribed. #AGBT17
6:35pm February 13th 2017 via Hootsuite
Churchman: BRD4 is a 'master regulation of transcription', showed effect of the two compounds. #AGBT17
Churchman: One cmpd targeting BRD4 is JQ1, '10 Nature ref https://t.co/EFcaJPdzbC also '15 Science https://t.co/6EikMCBjva #AGBT17
6:32pm February 13th 2017 via Hootsuite
Churchman: Looking at dysregulated transcription, and Rx targets such as BRD4 common in many cancers #AGBT17
6:30pm February 13th 2017 via Hootsuite
RT @healthhashtags: New Health Hashtag
6:29pm February 13th 2017 via Hootsuite
Churchman: In HeLa S3: a 50b promoter-proximal pause, and not at a single nucleotide but at several specific sites. #AGBT17
6:28pm February 13th 2017 via Hootsuite
Churchman: Isolate nuclei, ID 3' end of nascent RNA, ligate hexamer to 3' end. Can map Pol II density genome-wide #AGBT17
6:26pm February 13th 2017 via Hootsuite
Churchman: nt resolution, maps across yeast, drosophila, mammalian cells '15 Cell ref https://t.co/nDJj6AzzX1 #AGBT17
6:25pm February 13th 2017 via Hootsuite
Churchman: Describes her Net-Seq method '11 Nature ref https://t.co/HKbfxaHcf0 #AGBT17
6:24pm February 13th 2017 via Hootsuite
Churchman: Many intermediate stages, and impt roles. From DNA/chromatin, NET-seq looking at xcription. RNApol not a wind-up car #AGBT17
6:23pm February 13th 2017 via Hootsuite
Churchman: Starts off with the Central Dogma and where genes are regulated; transcripts are degraded; RNA processed is degraded #AGBT17
6:21pm February 13th 2017 via Hootsuite
Stirling Churchman (Harvard MA) Gene expression at high resolution, from the nuclear and mitochondrial genomes #AGBT17
6:20pm February 13th 2017 via Hootsuite
J. Hadfield: @coregenomics perhaps use CRSPR to fix? Kung: A daunting task for cancer, to fix 100% of cells. Maybe T-cells for ca #AGBT17
6:19pm February 13th 2017 via Hootsuite
Q: 1/1M sens? Kung: Done with Adaptive Biotechnologies (ha, I was right!) About 10-20ug of DNA, getting into the noise of the assay #AGBT17
6:18pm February 13th 2017 via Hootsuite
Q:Can you get blinded data? Kung: Hard Q - proving the value of these kinds of tests. #AGBT17
6:16pm February 13th 2017 via Hootsuite
