Clark: 30 diseases caused by expansion repeat disorders. Often with high GC content to 100% #ESHG2016
8:07am May 22nd 2016 via Hootsuite
Tyson ClarK (@pacbio CA) Enrichment of unamplified DNA & long-read SMRT Sequencing to unlock repeat expansion disorders #ESHG2016
8:05am May 22nd 2016 via Hootsuite
Q: Design and costs of MIPS? Hoischen: Designs on Shendure's page. But not for GC >80% #ESHG2016
8:04am May 22nd 2016 via Hootsuite
Hoischen: Second application: de novo mut's in spermatogenesis. Looking at 'seflish' mutations, looking for FGFR2, Apert syndrome #ESHG2016
8:00am May 22nd 2016 via Hootsuite
Hoischen: Able to get maternal plasma, found mutant at 4%; from smMP: 10/234 unique molecule (4.2%). #ESHG2016
7:55am May 22nd 2016 via Hootsuite
Hoischen: Look at haplotype-tagged SNPs, pick up specific allele. Proof-of-concept: autosomal dom affected father, brother #ESHG2016
7:54am May 22nd 2016 via Hootsuite
Hoischen: smMIPs were made strand-specific; footprint made smaller (80nt). Looked for DMPK mutations. But repeat expansion... #ESHG2016
7:52am May 22nd 2016 via Hootsuite
Hoischen: Request for monogenic disorders in cf maternal DNA. 10ng, 2800 genome copies; fetal fx is 10%; a het is only 140 copies #ESHG2016
7:51am May 22nd 2016 via Hootsuite
Hoischen: Single-molecule MIPS - use UMI '13 ref https://t.co/eNoCM1efxL reducing error rate with PCR and seq 2x5 Nx - 4^10 counts#ESHG2016
7:49am May 22nd 2016 via Hootsuite
Hoischen: Using MIPs, '12 Science ref https://t.co/NCTvITW3WP can scale, 1 to 100 candidate genes at once. Tech traced to '94 #ESHG2016
7:47am May 22nd 2016 via Hootsuite
Alex Hoischen (Univ Radboud Nijmegen Netherlands) Ultra-sensitive mosaic mutation detection for clinical applications #ESHG2016
7:45am May 22nd 2016 via Hootsuite
Guindo-Martínez: Will focus rest of talk on bladder cancer - showed Manhattan plots across cancers. ZCCHC7 var w/bladder ca #ESHG2016
7:25am May 22nd 2016 via Hootsuite
Guindo-Martínez: N.B.: Here is the GWImp-COMPs homepage https://t.co/Atu6kBq3KP saving computational time for imputation #ESHG2016
7:23am May 22nd 2016 via Hootsuite
Guindo-Martínez: 'Genotype imputation is increasingly better with novel reference panels' incl TGP. Dev tool GWImp-COMPSs #ESHG2016
7:22am May 22nd 2016 via Hootsuite
Guindo-Martínez: Using european genome-phenome data and dbGAP, 500K samples. 9 cancers, n=33.6K Reviews imputation #ESHG2016
7:19am May 22nd 2016 via Hootsuite
Marta Guindo-Martínez (Barcelona Supercomputing Ctr) ID novel low freq var's assoc'd with susceptibility with a variety of cancers #ESHG2016
7:18am May 22nd 2016 via Hootsuite
Q: New avenues? Tukiainen: Patterns of tissue-tissue interplay. #ESHG2016
7:16am May 22nd 2016 via Hootsuite
Q: Role of inflammation genes and BMI? Tukiainen: Can be tissue phenomenon; but at adipocyte level can be significant #ESHG2016
7:15am May 22nd 2016 via Hootsuite
Tukiainen: GTex Portal here: https://t.co/LrMxPMbi0t Evidence for causal effects - subcut. adipose: energy metab genes to BMI #ESHG2016
7:11am May 22nd 2016 via Hootsuite
Tukiainen: GTex overview - 44 tissue types, 450 autopsy donors. Of tissue types, fat tissue most highly significant; skin second #ESHG2016
7:10am May 22nd 2016 via Hootsuite
Taru Tukiainen (Inst Mol Med Finland) Multi-tissue transcriptome analysis reveals causal links between obesity and gene expression #ESHG2016
7:08am May 22nd 2016 via Hootsuite
Groelz: Two protocols in dev: std one similar to existing circulating NA protocols, and a large fragment one. #ESHG2016
6:22am May 22nd 2016 via Hootsuite
Groelz: QIAsymphony 'generic' currently exists, works with EDTA/Streck, also w/PAXgene #ESHG2016
6:20am May 22nd 2016 via Hootsuite
Groelz: 1, 3, 7d RT storage; assayed via therascreen. Event at 1d, EDTA crosses lower Ct threshold (but not PAX) #ESHG2016
6:18am May 22nd 2016 via Hootsuite
Groelz: For ctDNA, with QIAGEN Manchester, spiked reference DNA and mutant DNA (companion Dx) into whole-blood #ESHG2016
6:15am May 22nd 2016 via Hootsuite
Groelz: Found less hemolysis with PAXgene (showed photo comparing the two), but did not affect their analysis. #ESHG2016
6:14am May 22nd 2016 via Hootsuite
Groelz: Study via LifeCodexx, to stabilize maternal blood for NIPS. n=18, feta fraction >4%. Showed equal performance cp to Streck #ESHG2
6:13am May 22nd 2016 via Hootsuite
Groelz: Gel shows lack of apoptotic bands after 7d storage. qPCR data of 18s rDNA assay at d1 to d10, EDTA vs PAX, clr difference #ESHG2016
6:10am May 22nd 2016 via Hootsuite
Groelz: cell-free Streck comparison - Agilent scan shows some increase, widening in size of cfDNA peak (likely from apoptosis) #ESHG2016
6:08am May 22nd 2016 via Hootsuite
Groelz: Plastic Vacutainer, hemogard closure (minimized risk of tube breakage, safer for personnel). Optimized for QIAsymphony #ESHG2016
6:06am May 22nd 2016 via Hootsuite
Groelz: PaxGene Blood ccfDNA Tube: non-crosslinking preservation; minimal hemolysis; 10mL; 7d at 15-25C, 1d at 30C #ESHG2016
6:05am May 22nd 2016 via Hootsuite
Groelz: BioAnalyzer trace in EDTA after 0d, then 6d; huge increase of apoptotic bands #ESHG2016
6:03am May 22nd 2016 via Hootsuite
Groelz: Immediate separation of plasma is a problem; physician's offices not suitable for centrifugation. #ESHG2016
6:02am May 22nd 2016 via Hootsuite
Groelz: Blood collected in EDTA, blood will apoptose rapidly. also in citrate or heparin. Plasma must be separated w/in 6h #ESHG2016
6:01am May 22nd 2016 via Hootsuite
Groelz: ccfDNA <<100ng per mL of plasma; one found 40 ug tumor DNA in 10mL of whole blood. #ESHG2016
5:59am May 22nd 2016 via Hootsuite
Groelz: Joint-venture with BD since 1998. ccfDNA is 'strongly fragmented' <500bp, 150-200bp. >500bp from necrotic processes #ESHG2016
5:58am May 22nd 2016 via Hootsuite
Groelz: New PAXgene Blood ccfDNA tube, and QIASymphony procedure ('later this year') Disclaims RUO status. #ESHG2016
5:56am May 22nd 2016 via Hootsuite
Daniel Groelz (QIAGEN) PAXgene Blood ccfDNA System - Workflow for Integrated Collection, Stabilization, and Purification of ccfDNA #ESHG2016
5:55am May 22nd 2016 via Hootsuite
Q: Lowest level of heteroplasmay? García-Arumí: Down to 1%; only high-quality reads. About 4000x depth#ESHG2016
5:50am May 22nd 2016 via Hootsuite
García-Arumí: Leigh syndrome is most common pediatric presentation of mtDNA disorders. Long list of associated genes #ESHG2016
5:45am May 22nd 2016 via Hootsuite
García-Arumí: Showed incomplete results with GeneRead panel v1; v2 was much butter. 197 targets, 400 amplicons #ESHG2016
5:42am May 22nd 2016 via Hootsuite
García-Arumí: Shows list (about 15) of autosomal genes involved with mtDNA maintenance, integrity #ESHG2016
5:39am May 22nd 2016 via Hootsuite
García-Arumí: Their datasets are from Ion Torrent PGM, MiSeq. Coverage plots compare / highlight areas of low coverage of mtDNA #ESHG2016
5:35am May 22nd 2016 via Hootsuite
García-Arumí: Lays out detailed flowchart of molecular genetics approaches - long PCR, RT-PCR for copy number, WES too #ESHG2016
5:33am May 22nd 2016 via Hootsuite
García-Arumí: About 4M mtDNA diseases in the US yearly. Affecting multiple organs, phenotypes complex https://t.co/YnfTtBtfv6 #ESHG2016
5:31am May 22nd 2016 via Hootsuite
García-Arumí IHospital Vall d´Hebron, Barcelona) Molecular genetics approaches to mitochondrial OXPHOS system diseases #ESHG2016
5:29am May 22nd 2016 via Hootsuite
Stegle: Can model single-cell DNA methyl var from seq; 100 predictive motifs, 25% assoc'd w/ changes in epigenetic heterogeneity. #ESHG2016
3:48am May 22nd 2016 via Hootsuite
Stegle: Can single-cell DNA methylation be tied back to sequence var's? A stacked neural network, called DeepCpG #ESHG2016
3:42am May 22nd 2016 via Hootsuite
Stegle: One '15 ref https://t.co/E0NCbaqwg0 Substantial heterogeneity between cells. Show's 100's of associations in 61 cells #ESHG2016
3:39am May 22nd 2016 via Hootsuite
Stegle: Relate RNA to DNA methylome. Via FACS, separate RNA from DNA, simultaneous analysis of bisulfate-treated single-cell DNA #ESHG2016
3:35am May 22nd 2016 via Hootsuite
