October 2014

RT @neilfws: We have hit a new low. MT @ImranSHaque: Winning the prize for most egregious Venn diagram at #ASHG14 http://t.co/B9l5kV2hAX

4:02pm October 29th 2014 via Hootsuite

MT @phylogenomics: Fron teh brilliamt foks @Stanford: Gemonics & Other Omics http://t.co/Sqb0lJuQE2 (w/ pic to fix) http://t.co/a656Heji

3:05pm October 29th 2014 via Hootsuite

RT @iontorrent: w/ Ion AmpliSeq Whole Transcriptome, Dr. Ku is able to get from sample to library in less than a day http://t.co/AuA59MRoQq

2:02pm October 29th 2014 via Hootsuite

Have a local copy of the UCSC Genome Browser | Bioinformatics J http://t.co/KBrXG8cpPd #GBiB HT @GenomeBrowser

1:05pm October 29th 2014 via Hootsuite

RT @medskep: Hospital infections kill more people than car crashes or lung cancer via @CJR http://t.co/yrzBtvkAgr

12:00pm October 29th 2014 via Hootsuite

RT @SUEtheTrex: "Inside the Field Museum's Hidden Flesh-Eating Beetle Room." Great headline? Or GREATEST headline? http://t.co/ELv6j8tiqM

11:01am October 29th 2014 via Hootsuite

RT @angiegaddy: First of kind in NAm. BC pharmacy & @GenomeBC study to make personalized med a reality for patients http://t.co/o66yeHsA

10:05am October 29th 2014 via Hootsuite

New post: Behind the scenes with Dr. Ku and the new @iontorrent AmpliSeq Transcriptome http://t.co/vJkeEWo2wK

8:01am October 29th 2014 via Hootsuite

BA-L:Q:Will SNVs in reg. regions affect proteomics work? A:We need to better understand protein complexes affecting reg regions #ISCB14

7:58am October 29th 2014 via Hootsuite

BA-L: ...and adapt combination therapy in response to longitudinal profiling. "I believe it isn't far away" #ISCB14

7:49am October 29th 2014 via Hootsuite

BA-L:What is the future? Cancer as a chronic condition. By developing a cabinet of mechanistically different drugs #ISCB14

7:49am October 29th 2014 via Hootsuite

BA-L:Working on the simplest models for combinations suggested by network connectivity. #ISCB14

7:48am October 29th 2014 via Hootsuite

BA-L:This network view also integrated into canSAR. Now onto combinatorial drug screens: profiles w/in the interactome #ISCB14

7:45am October 29th 2014 via Hootsuite

BA-L: Analyzed network as subgraphs (several types); cancer targets enrich for particular subgraphs; they communicate in paths #ISCB14

7:44am October 29th 2014 via Hootsuite

BA-L: Of 12.5K proteins, constructed a human interactome, removed by text mining and co-IP, resulted in 10K and mapped drug targets #ISCB14

7:42am October 29th 2014 via Hootsuite

BA-L:Their druggable targets (of which they have discovered several) they are making available for others to pursue #ISCB14

7:39am October 29th 2014 via Hootsuite

BA-L: Looked at a novel RNA-binding protein, top target in a list, and shown their candidate interferes with RNA binding #ISCB

7:37am October 29th 2014 via Hootsuite

BA-L:Rhabdomyosarc. unknown drivers before; novel targets being also explored #ISCB

7:35am October 29th 2014 via Hootsuite

BA-L: RD cell lines against negative control: 41 druggable targets; ID existing targets from this list (some kinases, GPCRs) #ISCB

7:34am October 29th 2014 via Hootsuite

BA-L:Their proteomewide analysis:intersection of the potential cancer drivers, and druggable targets. Can get rapid ID of drug re-use #ISCB

7:33am October 29th 2014 via Hootsuite

BA-L:Diff. to get first trial in children for this; had genomic / whole transcriptome data from 100; also clinical data #ISCB

7:31am October 29th 2014 via Hootsuite

BA-L:Rhabdomyosarcoma is most common soft tiss. sarcoma in children; rare (3% of childhood ca); no targeted therapy, poor prognosis #ISCB

7:30am October 29th 2014 via Hootsuite

BA-L: Another:a DNA repair gene, of 450, explored synthetic lethality, work recently accepted for publication #ISCB

7:28am October 29th 2014 via Hootsuite

BA-L:The problem:the cancer driver genes have little known abt fn. Ex:SMARCA4(BRG1) has 2 druggable domains, tumor supp&oncogene #ISCB

7:27am October 29th 2014 via Hootsuite

BA-L: 'We are not exploring the broad biological/chemical space as we could do'. W/in CGC: 46 druggable ca drivers ignored chemically #ISCB

7:26am October 29th 2014 via Hootsuite

BA-L: 1536 human proteins have bioactive sm molecule tools (193K cmpds) #ISCB

7:24am October 29th 2014 via Hootsuite

BA-L:What is cancer chemical biology exploring? 941 genes w/coding somatic muts: only 50 mutations found in cell lines #ISCB

7:23am October 29th 2014 via Hootsuite

BA-L:Another example, unknown RNA-binding protein discovered via this method #ISCB

7:22am October 29th 2014 via Hootsuite

BA-L: Shown completely novel targets not in training sets; also allosteric pockets (e.g. RAS!) ID an area outside the GTP pocket #ISCB

7:21am October 29th 2014 via Hootsuite

BA-L: Use 3D structure of known targets; calculate phys properties; feed into training set; predict druggability #ISCB

7:20am October 29th 2014 via Hootsuite

BA-L: Drugs fall into four main families; presumes all equally druggable, but kinases are particularly difficult #ISCB

7:19am October 29th 2014 via Hootsuite

BA-L:Overlap the human proteome and subset that cause disease, and can be modulated by drugs. #ISCB

7:18am October 29th 2014 via Hootsuite

BA-L: To meet unmet needs - 'we can't stick to pathways we already know a lot about' Metch: big cancer gene data to target selection #ISCB

7:17am October 29th 2014 via Hootsuite

BA-L: Showed a gene word cloud (!) to illustrate. 385/513 in CGC not in TCGA nor Vogelstein as drivers #ISCB

7:17am October 29th 2014 via Hootsuite

BA-L: Onto target choice: large datasets for cancer gene ID; Venn of Vogelstein, TCGA and CGC is only 58 genes #ISCB

7:16am October 29th 2014 via Hootsuite

BA-L: RNAi data is being worked on to put into canSAR. Struct data, prot-prot interaction. canSAR 50K unique users, heavily used #ISCB

7:14am October 29th 2014 via Hootsuite

BA-L: Entire proteome, all cpds from database. Most popular: a single-page summary report. #ISCB14 Updated NAR 2014 http://t.co/wRJyqDKXvb

7:13am October 29th 2014 via Hootsuite

BA-L:canSAR is a free resoruce; NAR 2011 ref: http://t.co/wh6hJ1iChA Extensible, modular content. Huge list of 12 sources of data #ISCB14

7:12am October 29th 2014 via Hootsuite

BA-L:3 major efforts: global multidisc. knowledge 'canSAR resource'; objective selection of novel targets; adaptive combinations #ISCB14

7:10am October 29th 2014 via Hootsuite

BA-L: Lays out increased clinical attrition; drugs/$B chart. Walks through all the reasons for attrition, not to mention resistance #ISCB14

7:09am October 29th 2014 via Hootsuite

BA-L: (#ISCB14 LatAm sched. here: http://t.co/GnAdg140vZ ) Plot of sequence in GeneBank, cp to drug approval: a mirror image!

7:06am October 29th 2014 via Hootsuite

BA-L: Biography (UCL): http://t.co/iPoI0YPVOt Pipeline for drug discovery: ID, Hit ID, lead optimization, preclin dev, PhI-II-III #ISCB14

7:05am October 29th 2014 via Hootsuite

Live-tweeting one ISCB-LatAm talk in Belo Horizonte Brazil. 'Integrative computational approaches in cancer drug disc' B. Al-Lazikani

7:03am October 29th 2014 via Hootsuite

The @Lifetech #ASHG14 YouTube playlist | YouTube http://t.co/9MYMO17Ior

7:00am October 29th 2014 via Hootsuite

Too late to start? Late bloomers and the quarter-life crisis | Funders & Founders http://t.co/z3EPvx2dPB HT @yanghemary

6:05am October 29th 2014 via Hootsuite