Fast, scalable prediction of deleterious #noncoding variants from functional & population genomic data
https://www.Nature.com/articles/ng.3810 LINSIGHT, by @ASiepel et al., combines DNAse & conservation information
Yi-Fei Huang, Brad Gulko & Adam Siepel
Nature Genetics 49, 618–624 (2017)
doi:10.1038/ng.3810
Published online:
13 March 2017
3D clusters of somatic mutations…reveal numerous rare mutations as functional targets
https://GenomeMedicine.BiomedCentral.com/articles/10.1186/s13073-016-0393-x Introduces 3DHotSpots, which is one of a number of recent approaches (incl. CLUMPS, Hotspot3D, Mutation3D & HotMAPS) for finding groupings of somatic SNVs via structure
Batch Cloud Data Transfer for @AWScloud
https://AWS.amazon.com/snowball & https://AWS.amazon.com/snowmobile Snail mail trumps email for really large data. Interesting to calculate effective transfer rates as compared “Ethernet.” Surprised that I couldn’t find even crude estimates.
Accurate Prediction of Contact Numbers for Multi-Spanning Helical #MembraneProteins – via #NeuralNetwork w/ dropout
http://pubs.ACS.org/doi/abs/10.1021/acs.jcim.5b00517 In turn, this can enable accurate prediction of the rotation of TM helices & then the 3D #StructurePrediction of the whole protein
http://www.nature.com/nmeth/journal/v12/n4/full/nmeth.3314.html
Genome seq–independent Identification of #RNAediting http://www.Nature.com/nmeth/journal/v12/n4/full/nmeth.3314.html Accurate sites from uncorrelated SNV pair, spanned by reads
Naldi, A. et al. Reconstruction and signal propagation analysis of the Syk signaling network in breast cancer cells. PLOS Computational Biology 13, e1005432 (2017).
Reconstruction & signal propagation analysis of the Syk signaling #network http://journals.PLoS.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005432 Inferring potential targets of the kinase
http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.2001402
#mHealth: Tracking Physiomes & Activity w/ Wearable Biosensors, by @SnyderShot et al http://journals.PLoS.org/plosbiology/article?id=10.1371/journal.pbio.2001402 >250K/day data pts on 43 people
Similarity #network fusion for aggregating data types
http://www.nature.com/nmeth/journal/v11/n3/full/nmeth.2810.html Combines mRNA, miRNA & gene fusions to classify cancer subtypes http://compbio.cs.toronto.edu/SNF/SNF
Similarity #network fusion for aggregating data types http://www.nature.com/nmeth/journal/v11/n3/full/nmeth.2810.html Combines mRNA, miRNA & gene fusions to classify cancer subtypes
http://compbio.cs.toronto.edu/SNF/SNF
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