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Favorite Tweets from Late Nov. '17 Meetings - ERCC9, RSG '17+ misc (#ERCC9,i0exrna17,#RSGDream17, i0rsg17)
- @FertigLab Changes in expression come before changes in methylation, contrary to their initial expectations. #rsgdream17
- Talk on integrated time-series multi-omics from @FertigLab makes me want to read CoGAPS paper ASAP #RsgDream17 @iscb pic.twitter.com/0NBiOoGFMl
- .@FertigLab cites PatternMarkers & GWCoGAPS for novel data-driven biomarkers via whole transcriptome NMF https://academic.OUP.com/bioinformatics/article/33/12/1892/2975325 … Sparsely decomposes gene expression into patterns #rsgdream17
- .@FertigLab presents dynamics of therapeutic response in cancer. Focusing on EGFR inhibitors #rsgdream17
- Ziga Avsec presents his poster "Modeling positional effects of regulatory sequences with spline transformations increases prediction accuracy of DNN" at #RSGDream17 - paper now online https://doi.org/10.1093/bioinformatics/btx727 …
- Outstanding keynote by Christina Leslie on epigenomic signatures distinguishing 'reprogrammable' vs. 'non-reprogrammable' immune cells, and cell surface markers that can be used to distinguish them to guide cancer immunotherapy #RSGDream17 https://twitter.com/markgerstein/status/932329278729015307 …
- It is hard to bridge cutting edge data-science and cutting edge immunotherapy/the clinical side of things: Christina Leslie of MSKCC shows us how it’s done! #RsgDream17 pic.twitter.com/H68KkbuLhx
- @anshulkundaje @RalucaGordan I might have transcribed it wrongly but my impression was @RalucaGordan indicated that binding events in open chromatin regions appear "stronger" than otherwise #rsgdream17
- Leslie cites "Chromatin states define tumour-specific T cell dysfunction and reprogramming" https://www.Nature.com/articles/nature22367 … Has changing ATAC-seq peaks & gene expression betw. tumor & normal #rsgdream17
- @manoliskellis @markgerstein You forgot F2F :)
- Parida's talk has lots of useful #DataScience buzzwords: 3Vs for data (= velocity, volume, variety) + WfG (@IBMWatson for Genomics) as a transparent, comprehensive, navigable, & scaleable system + the hologenome for microbiome #rsgdream17
- @IBMWatson Genomics for Cancer cure. Excellent insights by Laxmi Parida at #RSGDream17
- Ooh. Interest piqued: Laxmi Parida talking about IBMResearch's findings working with the @Genographic Project genomics data ... #RSGDREAM17
- @markgerstein After P2P and B2B, this is M2M :-)
- Amazing talk on ribothrypsis by Manolis Maragkakis uncovers new mechanisms of ribo-stalling and mRNA degradation http://mourelatos.med.upenn.edu/index.php/team/manolis-maragkakis/ … #RSGDream17
- .@RalucaGordan reviews many key biases in chipseq - eg DS breaks depend on nt seq., PCR amplification, stronger signal related to open chromatin & high expression, different results w/ diff. antibodies (particularly variable in ENCODE comparisons for ATF2) #rsgdream17
- Raluca Gordon: Chip-seq =/= in vivo binding. Combine multiple assays! #RSGDream17
- Bayesian nets, linear deconvolution, adaptive multitask lasso & deep learning all before 12pm. Feeling at home at #RSGDREAM17 🤓
- Nice keynote by @dana_peer at #RSGDREAM17 Single cell RNAseq expts w/ high dropout use a little MAGIC, good application of manifold learning
- Chip-seq biases @RalucaGordan as comparing to cut&run from https://elifesciences.org/articles/21856 #rsgdream17
- #rsgdream17 made it to the fourth talk before a deep learning appearance (LINCS signature learning). I would have guessed fewer.
- Castro in @RichBonneauNYU lab: Use TF activities to model gene expression via inferred networks. Using Adaptive Lasso (w/ a variety of sparse & block penalties) #rsgdream17
- Single va Multitask learning and Network Inference from @RichBonneauNYU #RsgDream17
- A MAGICal keynote. Now Ryan Peckner on deconvolution of next gen mass spec data #RSGDream17
- Dana Pe'er: a potential downside of MAGIC is reduction of true stochasticity in the data #RSGDream17
- From Peer, some key aspects of MAGIC: recovers gene-gene relationships, archetypical cell states & shape (manifold) data. Allows one see entire EMT from a single time point #rsgdream17
- And here's the MAGIC pre-print https://www.biorxiv.org/content/biorxiv/early/2017/02/25/111591.full.pdf … (Markov Affinity-based Graph Imputation of Cells) #RSGDream17
- Dana Pe'er talking about MAGIC #RSGdream17 pic.twitter.com/w9qBopBm9o
- Dana Pe’er from @sloan Kettering kicking off #RSGDream17 talking about computation and single cell approaches to cancer pic.twitter.com/ciGvgSzwny
- Conceptual extension of the canonical model of drivers and passengers in cancer by @markgerstein from http://lectures.gersteinlab.org/summary/Mutations-in-2500-cancer-genomes--20171104-i0gi2017/ … (nicely summarizing a bunch of papers I've read recently) pic.twitter.com/TvulpVjv7Y
- Great talks at ISG 5yr Anniversary! Characterizing genomes w/ new methods, software, sequencing tools @markgerstein @graveley @ENCODE_NIH
- UConn Institute for Systems Genomics FIVE year celebration symposium November 8 https://isg.uconn.edu/wp-content/uploads/sites/694/2017/09/ISG-5-year-celebration-flyer.pdf … pic.twitter.com/rPZpIfq4pV
- Rozowsky: the pipeline ( http://github.gersteinlab.org/exceRpt ) hows differences in exo-/endo- genous profiles across body fluids #ERCC9
- Aleks Milosavljevic @bcmhouston: Biological insights from exRNA Atlas through cross-study analysis http://exrna-atlas.org #ERCC9 pic.twitter.com/OVzawfPAwK
- Seda Kilinc Avsaroglu @UCSF: Oncogene-regulated production and release of EVs and exRNAs http://www.oncogenes.net #ERCC9 pic.twitter.com/sUI6QC4VMt
- Daniel Dixon @KUMedCenter RNA binding protein HuR regulates EV secretion in colorectal cancer #ERCC9
- Marianita Santiana @nih_nhlbi: Transmission of viruses between organisms via vesicles https://www.ncbi.nlm.nih.gov/pubmed/26423944 #ERCC9 pic.twitter.com/2M0md3Rk6R