Diaz: Shows figure from nice '14 JCO review https://t.co/HFB67RnFx7 ctDNA source comes from secretion, necrosis, apoptosis #AGBT16
10:07am February 11th 2016 via Hootsuite
Diaz: From somatic cancer genome data: to prognostic markers, dynamic biomarkers, immune antigens, predictive markers #AGBT16
10:03am February 11th 2016 via Hootsuite
.@h2so4hurts Looks like a nice #AGBT16 resource, Brian - thanks! (Hope to see you in Sept at the 'other' AGBT event...)
10:02am February 11th 2016 via Hootsuite in reply to h2so4hurts
Luis Diaz (Johns Hopkins) “Novel therapeutic and diagnostic applications of somatic mutations in solid tumor malignancies” #AGBT16
10:00am February 11th 2016 via Hootsuite
RT @LAbizar: #AGBT16 Chiu, host depletion very challenging therefore selected low cellularity samples.
Chiu: Shows Metrichor @nanopore software video with 2min recognition of Ebola virus. #AGBT16 https://t.co/e0IMWy7aU5
9:55am February 11th 2016 via Hootsuite
Chiu: Working with the FDA, offer a consult service, plan to launch in May '16 for CSF, plasma and lavage later in '16 #AGBT16
9:52am February 11th 2016 via Hootsuite
Chiu: Challenge was completeness of reference databases. #AGBT16
9:48am February 11th 2016 via Hootsuite
Chiu: 3rd:15yo w/T1D, hemorrhagic encephalitis 0.23% of reads of Balamuthia mandrillaris encephalitis by day 7; pt died day 1 #AGBT16
9:47am February 11th 2016 via Hootsuite
Chiu: Person likely contracted disease from undercooked crab in Fiji. Be careful! #AGBT16
9:46am February 11th 2016 via Hootsuite
Chiu: Case 2: 35yo on honeymoon, CSF WBC w/ rash and lesions in MRI. Ended up with a parasitic infection, a rat lungworm (yikes) #AGBT16
9:45am February 11th 2016 via Hootsuite
Chiu: 35 samples, wide variety. Presents cases: '14 NEJM https://t.co/tzOqYKW9C5 undiagnosed neuroleptospirosis (#AGBT14!) #AGBT16
9:44am February 11th 2016 via Hootsuite
Chiu: Showed results from plasmodium to rhinovirus to salmonella. Preparing a manuscript (Naccache et al '16) 27 pts over 22 months #AGBT16
9:42am February 11th 2016 via Hootsuite
Chiu: Three samples - plasma, lavage and CSF: less background, turnaround is 24h-48h #AGBT16
9:40am February 11th 2016 via Hootsuite
Chiu: SURPI bioinf. pipeline - '14 NAR ref https://t.co/QuTvT9dLXQ Reviews CLIA covers analyses as well, need for validation, QA #AGBT16
Chiu: Metagenomic sequencing - cast a wide net: a shotgun random approach, unbiased, feasible due to NGS capacity #AGBT16
9:38am February 11th 2016 via Hootsuite
Chiu: Meningitis (50-70% unk), fever/sepsis 30% of the time. Narly all microbes can be ID'd - bact, virus, fungi, parasites #AGBT16
9:37am February 11th 2016 via Hootsuite
Chiu: Want to implement CLIA lab for actionable, early-treatment info. Precision Dx: targeted unmet need (pneumonia 15% unk) #AGBT16
9:36am February 11th 2016 via Hootsuite
Charles Chiu (UCSF SOM) “Metagenomic deep sequencing for diagnosis of infectious diseases” #AGBT16
9:34am February 11th 2016 via Hootsuite
Aparicio:A2:Still will remain a problem, to get sufficient coverage. #AGBT16
Aparicio:Q:Low single-cell cov., errors in WGA? A:Diversity is low - only 15% represented. SNVs called at 1000's of cells as groups #AGBT16
9:33am February 11th 2016 via Hootsuite
Aparicio:Q:Implications for ctDNA? A:It may come, of course challenges #AGBT16
9:30am February 11th 2016 via Hootsuite
Aparicio:Q:How applied clinically? A:Now being applied to FFPE, a meas of clonality becoming an important metric for clinicians. #AGBT16
9:29am February 11th 2016 via Hootsuite
Aparicio: Pts 'vary widely' in clonal complexity. 'We are grateful to the anonymous patients who donated their tissue for research' #AGBT16
9:27am February 11th 2016 via Hootsuite
Aparicio: Only a few single-cell WGS datasets available for comparison; feel that 'this is the future' for clinical samples #AGBT16
Aparicio: Here's a 2013 NEJM review of implications of clonal genome evolution for cancer medicine https://t.co/n1lr8BGSXX #AGBT16
9:23am February 11th 2016 via Hootsuite
Aparicio: Called 'low resolution targeted lookups' w/targeted NGS. Doing WGS at scale - Carl Hansen for microfluidic cell proc #AGBT16
9:22am February 11th 2016 via Hootsuite
RT @LAbizar: #AGBT16 Aparicio, Xenografts do NOT recapitulate original tumor as clonal diversity very different in graft vs donor
9:20am February 11th 2016 via Hootsuite
Aparicio: PDX (Patient-derived xenograft) work - fig from '15 Nature https://t.co/gjpcOTWXta Looking at single-cell clonal dynamics #AGBT16
Sam Aparicio, Br Columbia Cancer Research Centre “Clonal evolution and cancer medicine at single cell resolution” #AGBT16
9:18am February 11th 2016 via Hootsuite
RT @matthewherper: Walgreens threatens to end Theranos agreement https://t.co/vS1paVXH5j
6:47am February 11th 2016 via Hootsuite
Bloodthirsty Ticks Have a Seriously Weird Genome | Gizmodo https://t.co/djmlX3vGDo
5:05am February 11th 2016 via Hootsuite
Machine Learning and the Profession of Medicine |JAMA ($) https://t.co/FhL7Ryjqmr
4:05am February 11th 2016 via Hootsuite
The Tragedy of the Woolly Mammoth: Does An Economic Problem Explain Their Extinction? - Pacific Standard https://t.co/RJxihSy5zz
9:50pm February 10th 2016 via Hootsuite
From G Church (PNAS): Genetically encoded sensors enable real-time observation of metabolite production. - PubMed https://t.co/nW8A7Q1c4A
8:10pm February 10th 2016 via Hootsuite
Sullivan: Mentions iVirus platform - website here: https://t.co/dTR4wJsPJe Credits Moore Foundation for funding, and Swift Bio too #AGBT16
7:02pm February 10th 2016 via Hootsuite
Sullivan RT @claritas4kids: #AGBT16 MS: Synechococcus and viruses are best predictors of carbon flux
7:01pm February 10th 2016 via Hootsuite
Sullivan: This is beyond pairwise comparison; #AGBT16 Science Daily writeup: https://t.co/lp06lpKeEk
6:58pm February 10th 2016 via Hootsuite
Sullivan: Which organisms and metabolisms drive carbon flux? ID'd taxa to sink carbon, can be scaled to any measurement #AGBT16
Sullivan: Found viruses 'sinking': Tara Oceans study: carbon flux from optical meas Today's Nature https://t.co/o161Ib58iR #AGBT16
6:57pm February 10th 2016 via Hootsuite
Sullivan: '15 eLife has 14 new phyla using method https://t.co/X0Ks74wpAw #AGBT16
6:54pm February 10th 2016 via Hootsuite
Sullivan: Problem - only 39 of the 5476 spp are known. '14 eLife https://t.co/JydMD9F0lc VirSort method #AGBT16
6:53pm February 10th 2016 via Hootsuite
Sullivan: Mapping ocean currents - but the statistics suggest adjust abundance due to environmental pressure #AGBT16
6:51pm February 10th 2016 via Hootsuite
Sullivan: For Tara, ID'd 5476 populations globally; now enables tracking of viruses through the water column (!) #AGBT16
Sullivan: Method for viral-tagged metagenomics 2014 Nature https://t.co/Wt2eDYOmB1 Looking at cyanophage seq space clusters #AGBT16
6:50pm February 10th 2016 via Hootsuite
Sullivan: Wanted to define what a 'viral population' is. The problem: viruses exchange genes all the time. #AGBT16
6:48pm February 10th 2016 via Hootsuite
Sullivan: Describes intense set of work called Tara Ocean project '15 Science https://t.co/8L9Exbwgcc 4th gen #AGBT16
6:47pm February 10th 2016 via Hootsuite
Sullivan: Now can get a 'gene ecology' - core and pan-gene sets; 90% of genes are unknown. #AGBT16
6:46pm February 10th 2016 via Hootsuite
Sullivan: This '12 review https://t.co/smBGLXlSYC shows their efforts down to 100 femtograms, and DNA virus-based. Swift Bio helped #AGBT16
6:45pm February 10th 2016 via Hootsuite
Sullivan: No common gene (like 16S) for viruses. Thus surveys are hard; 1st gen was 4MB via Sanger; 2nd gen was still hard. #AGBT16
6:44pm February 10th 2016 via Hootsuite
