Here’s the paper in Cell
https://www.cell.com/pb-assets/products/coronavirus/CELL_CELL-D-20-00892.pdf Apparently done by Nathan Grubagh here at Yale.
https://www.nytimes.com/2020/05/07/us/new-york-city-coronavirus-outbreak.html
Here’s the paper in Cell
https://www.cell.com/pb-assets/products/coronavirus/CELL_CELL-D-20-00892.pdf Apparently done by Nathan Grubagh here at Yale.
https://www.nytimes.com/2020/05/07/us/new-york-city-coronavirus-outbreak.html
Here’s the paper in Cell
https://www.cell.com/pb-assets/products/coronavirus/CELL_CELL-D-20-00892.pdf Apparently done by Nathan Grubagh here at Yale.
https://www.nytimes.com/2020/05/07/us/new-york-city-coronavirus-outbreak.html
The focus is on improving detection vs. treatment of late stage disease.
https://www.wsj.com/articles/cancer-is-still-beating-uswe-need-a-new-start-11570206319
quite relevant
https://www.nature.com/articles/nmeth.4534
Resolving systematic errors in widely used enhancer activity assays in human cells
Felix Muerdter
, Łukasz M Boryń
, Ashley R Woodfin
, Christoph Neumayr
, Martina Rath
, Muhammad A Zabidi
, Michaela Pagani
, Vanja Haberle
, Tomáš Kazmar
, Rui R Catarino
, Katharina Schernhuber
, Cosmas D Arnold
& Alexander Stark
Nature Methods volume 15, pages141–149 (2018)
Inferring [w. NMF] chromatin-bound protein complexes [of TFs] from [ENCODE ChIP-seq] binding assays, by @ElementoLab
http://genome.cshlp.org/content/23/8/1295.full
Giannopoulou E, Elemento O. 2013. Inferring chromatin-bound
protein complexes from genome-wide binding assays. Genome Research, Published in Advance April 3, 2013, doi: 10.1101/gr.149419.112.
This study uses nonnegative matrix factorization (NMF) of ENCODE CHIP-seq data (transcription
factors and histone modifications) to predict complexes of
transcription factors that bind DNA
together; it then assesses how these predicted complexes regulate gene expression. It goes beyond
previous studies in that it attempts to treat the TFs as complexes rather than individuals. A handful of
the predicted complexes correspond to known regulatory complexes, e.g. PRC2, and overall, the
complexes were enriched for known protein-protein interactions. Linear regression and random forest
models were then used to predict the effects of the complexes on the expression of adjacent genes. In
both models, the complexes performed better than those predicted from a scrambled TF read count
matrix. Overall, this study provides a large set of hypotheses for combinations of TFs that may
function together, as well as potential new components of known complexes.
Identification of…expr.-Modulating Variants using #MPRA, by @sabeti_lab http://www.cell.com/cell/fulltext/S0092-8674(16)30421-4 Some w. allelic skew related to PWM change
Learning the…Determinants of Alternative #Splicing [in a largely linear model] from Millions of Random Sequences
http://www.cell.com/cell/abstract/S0092-8674(15)01271-4
** Rosenberg et al Cell. 2015
Builds a model of splicing using a library of randomized sequence Also, builds a generalized model for predicting effect of a SNP in the Geuvadis RNAseq
7mer model does well with lots of data
Multiplatform assessment of #transcriptome profiling [w.] RNAseq http://www.nature.com/nbt/journal/v32/n9/full/nbt.2972.html Nice plots showing great effect of poly-A selection