@markgerstein: Navin mentions: Tracing the tumor lineage
http://t.co/pDTQBxmd54 Has nice schematic showing different tumor progression models #BTGCG14
http://www.sciencedirect.com/science/article/pii/S1574789110000323?via=ihub
@markgerstein: .nlbigas mentions: OncodriveCLUST: exploiting the [local] positional clustering of somatic mutations…
http://t.co/JbMYp6C7bF #BTGCG14
http://bioinformatics.oxfordjournals.org/content/29/18/2238.long
@markgerstein: .@nlbigas mentions: Evolution of the cancer genome http://t.co/DLwrOlzqch Drivers provide selective advantage #BTGCG14
@markgerstein: Korbel mentions: Criteria for Inference of
Chromothripsis in Cancer Genome
http://t.co/TslcrZsmNv #BTGCG14
http://www.sciencedirect.com/science/article/pii/S0092867413002122
Server for predicting damaging missense #mutations
http://www.nature.com/nmeth/journal/v7/n4/full/nmeth0410-248.html Polyphen2 uses both structure & sequence (eg ASA & conservation)
http://www.ncbi.nlm.nih.gov/pubmed/20354512
Polyphen2 includes both structural and sequence features to predict the effect of nonsynonymous substitutions on protein function. Similar to many other methods, Polyphen2 uses evolutionary conservation as one of the features to identify functionally important residues. Integration of 3D-structure, membrane-specific features (PHAT matrix for TM regions) and other features such as protein-domain and active-site are the strengths of Polyphen2 compared to other sequence-based software making it a good tool for prediction.
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
Signaling #hypergraphs
http://www.cell.com/trends/biotechnology/abstract/S0167-7799(14)00071-7 Edges from interactions of 2 sets of nodes. Better representation of assemblies & #complexes.
QT:{{”
each edge is defined not by interaction of 2 nodes (as in graphs), but 2 sets of nodes (known as hypernodes in hypergraphs)……The use of hypernodes also represents three concepts better than directed or non-directed graphs: protein complexes, protein assemblies and regulation (especially involving complexes/assemblies).
“}}
Signaling hypergraphs. Ritz et al. (2014) TIB
This opinion paper advocates the use of hypergraphs to complement graph-based signaling network and pathway analyses, where each edge is defined not by interaction of 2 nodes (as in graphs), but 2 sets of nodes (known as hypernodes in hypergraphs). They argue that
hypergraphs is a set-based method that acts like a more general version of a graph. The use of hypernodes also represents three concepts better than directed or non-directed graphs: protein complexes, protein assemblies and regulation (especially involving complexes/assemblies). They propose that hypergraphs can be very useful in situations where the effects of individual proteins might be neglected in graphs but will have a noticeable effect when these proteins are included in protein complexes as hypernodes. They use 3 applications as examples: pathway enrichment, pathway reconstruction, and pathway crosstalk.
Searching for missing heritability… rare variant association studies http://www.pnas.org/content/111/4/E455.abstract Pessimistic on #RVAS in #noncoding regions
Nice overview of study design. Good journal-club material.
Bourne mentions “What Big Data means to me”
(http://jamia.bmj.com/content/21/2/194.extract ) in connection with the creation of a digital ecosystem #ydod2014
http://www.nature.com/ng/journal/vaop/ncurrent/full/ng.3101.html
An interesting report of potential non-coding drivers without actually doing any wet lab work.
“These methods identify recurrent mutations in regulatory elements upstream of PLEKHS1, WDR74 and SDHD, as well as previously identified mutations in the TERT promoter”. In the text they mention “Khurana et al. also reported WDR74 promoter mutations in 2 of the 20 prostate cancer genomes analyzed”.