October 2013

DeLuca: Conclude: GEM attractive for eQTLs, ASE; TH2 preferred for splicing-based eQTLs #ASHG2013

2:31pm October 24th 2013 via Hootsuite

DeLuca: Conclude: 'Different aligners are optimal for different applications'. Tophat, GEM have reas comput costs. #ASHG2013

2:30pm October 24th 2013 via Hootsuite

DeLuca: Computational perf: Star alignment speed shines, orders of mag. better. Converted to $: Stars is 'incred cheap' in AWS #ASHG2013

2:28pm October 24th 2013 via Hootsuite

DeLuca:Split reads, adipose TH2 leads, sim. data TH2 again leads, two-split reads (3-exon spans), TH1/2 much better #ASHG2013

2:27pm October 24th 2013 via Hootsuite

DeLuca: Looking at FP rate, GEM also led in comparison. In/dels for simulation - also GEM leads. Insertions still challenging #ASHG2013

2:26pm October 24th 2013 via Hootsuite

DeLuca: TopHat1, TH2, Star and GEM compared; GEM had better call rate for non-ref bases. #ASHG2013

2:25pm October 24th 2013 via Hootsuite

DeLuca: Referred to Grant et al as a nice place to start, only from 2011. PubMed: http://t.co/VDHvpcWE0U #ASHG2013

2:23pm October 24th 2013 via Hootsuite

DeLuca: Challenged by lack of 'ground truth', spectrum of parameters. Wanted obj comparisons #ASHG2013

2:20pm October 24th 2013 via Hootsuite

DeLuca: GTEx looks at eQTLs, ASE and splice site QTLs; wanted to align, accommodate split reads, accurate and tolerate var's #ASHG2013

2:19pm October 24th 2013 via Hootsuite

Up next: D. DeLuca, Broad Inst., on RNA-seq aligners for QTL and ASE analysis for GTex. #ASHG2013

2:17pm October 24th 2013 via Hootsuite

Quinones: Maintains a database of sequenced HIV strains, assesses viral load via RT-PCR, calc. intrinsic error rate of seq. #ASHG2013

1:48pm October 24th 2013 via Hootsuite

Quinones: 'DeepGen': an all-inclusive test. Complex mapper to account for the thousands of highly variable HIV strains. #ASHG2013

1:46pm October 24th 2013 via Hootsuite

Quinones: 'So it came down to cost'. Turnaround time is vital too for any clinical setting. #ASHG2013

1:44pm October 24th 2013 via Hootsuite

Quinones: Fig from PLoS paper, he believes that 'Hi-Q will be a game-changer'. Paper concl. "no difference in det. HIV-1 tropism' #ASHG2013

1:43pm October 24th 2013 via Hootsuite

Quinones: Listed publications across vendors. He compared all four platforms on HIV-1, published in PLoS http://t.co/WwwhVxmb0s #ASHG2013

1:42pm October 24th 2013 via Hootsuite

Quinones: Doing 3 or 4 different drug-resistance tests may require multiple boxes and assays for HIV. With NGS: a single method. #ASHG2013

1:41pm October 24th 2013 via Hootsuite

Quinones: HIV-1 minority variants - using allele-specific PCR or OLA for drug-resistant variants. Until now - 454-based #ASHG2013

1:39pm October 24th 2013 via Hootsuite

Quinones: Since Sanger is only 20% sensitive to heterogeneous samples, wanted a deep-sequencing approach. #ASHG2013

1:39pm October 24th 2013 via Hootsuite

Quinones: Up to this year used Sanger sequencing to detect mutations. About 1 year ago, developed an NGS-based one. #ASHG2013

1:38pm October 24th 2013 via Hootsuite

Up next: Dr. Miguel Quinones-Mateu, Case Western Univ. 'Deepgen HIV' #ASHG2013

1:35pm October 24th 2013 via Hootsuite

Allcock: 50ng vs. 1.5-2ug input for capture. Have sequenced 300 smpls in 6 mo. for neuromuscular disease. #ASHG2013

1:31pm October 24th 2013 via Hootsuite

Allcock: Proton 80M reads, 14GB, 235bp nice inc. in readlength. 95% on target reads. #ASHG2013

1:28pm October 24th 2013 via Hootsuite

Allcock: Orphan cases- 10-15% success rates. Hyb capture has limits, 3d, large input. AmpliSeq: rapid PCR based Exome targeting #ASHG2013

1:26pm October 24th 2013 via Hootsuite

Allcock: Looks at synonymous variants for minicore disease. Found 4bp deletion in Titan gene, very diff. to seq. Last 2bp of exon #ASHG2013

1:24pm October 24th 2013 via Hootsuite

Allcock: 3.2MB target fits 16 smpl/run, much easier to analyze, one failure in 6 mo but still callable data #ASHG2013

1:21pm October 24th 2013 via Hootsuite

Allcock: WES using TargetSeq on Proton fits 2 smpl/run, chose >20x target, only needed 60x coverage /smpl #ASHG2013

1:19pm October 24th 2013 via Hootsuite

Alcock: Neg. clinical results get consented to feed back to the NGS res. lab. WES, 336 gene panel of 3.2MB #ASHG2013

1:17pm October 24th 2013 via Hootsuite

R. Allcock, Univ W Australia, getting 12-14GB per run on Ion Proton, 'very reliable'. Photo: http://t.co/YTjnFc3RSf

1:14pm October 24th 2013 via Hootsuite

Proven by math (!) RT@HarvardBiz Make Better Decisions by Getting Outside Your Social Bubble http://t.co/tpKjzvJyIq

1:10pm October 24th 2013 via Hootsuite

Life Technologies' Andy Felton at #ASHG2013 lunchtime @iontorrent workshop. http://t.co/fwNfGySjTz

1:01pm October 24th 2013 via Hootsuite

RT @appliedbio: .@iontorrent lunch seminar Today 12:30 Room 259, Level 2 Exome, Metagenomics, HIV Genotyping #ASHG2013 @LIFECorporation

10:11am October 24th 2013 via Hootsuite

Pearson Q&A: The API is open. 'We're not hoping to make $ from this; goal is to broaden understanding of the platform, including those..

9:35am October 24th 2013 via Hootsuite

Pearson: first project on myopia. Live examination of the projec, invite collaboration #ASHG2013

9:27am October 24th 2013 via Hootsuite

Pearson

9:24am October 24th 2013 via Hootsuite

Was told by a friend at @Ingenuity that @GenomeNathan was the driving force behind the EWG project. Congratulations Nathan! #ASHG2013

9:18am October 24th 2013 via Hootsuite

Pearson: For info about the Empowered Whole Genome cohort, here's a press release via Yahoo Oct 15 http://t.co/5fCozQuHJn #ASHG2013

9:18am October 24th 2013 via Hootsuite

N. Pearson up next: The Empowered Whole Genome Cohort: Shareable Joint Genome Interpretation for Research and Personal Insight. #ASHG2013

9:17am October 24th 2013 via Hootsuite

Towne: Q&A: 'We do are best not to see (VUS)" #ASHG2013

9:01am October 24th 2013 via Hootsuite

Towne: Concl: "WES is a valuable tool"; "may shorthen time"; "clinician involvement... is crucial" #ASHG2013

8:59am October 24th 2013 via Hootsuite

Towne: Research group did show reduced cost and time. Ave cost for WES was $4.5K/family. Over 15mo cost (to Dec 2012) ~$2K to ~$1K #ASHG2013

8:58am October 24th 2013 via Hootsuite

Towne: Pipelines include Codified genomics, Xbrowse. Data from 79 families: 26 in analysis; 22 found known muts; 17 w/novel genes #ASHG2013

8:52am October 24th 2013 via Hootsuite

Towne:48% unk. Dx; 45% unk. molecular cause (e.g. mt disease); 5% primary etiology known #ASHG2013

8:49am October 24th 2013 via Hootsuite

Towne: 17 with extended families due to consanguinity or other factors. #ASHG2013

8:49am October 24th 2013 via Hootsuite

Towne: Majority is WES, reporting primary findings back, 1408 pts from 427 families; 375 samples as trios (82), 45 probands only #ASHG2013

8:48am October 24th 2013 via Hootsuite

Up next: M. Towne, BCH: Efficiency of whole exome/genome sequencing for achieving a diagnosis in rare presentations. #ASHG2013

8:47am October 24th 2013 via Hootsuite

Khurana: Gerstein lab developed a tool called FunSeq. #ASHG2013 Gerstein lab FunSeq link http://t.co/U6NqMesEbt

8:46am October 24th 2013 via Hootsuite