Comprehensive assessment of #RNAseq… by #SEQC http://www.nature.com/nbt/journal/v32/n9/full/nbt.2957.html Spikeins as negative controls, so all diff expression results in FPs.
Archive for the 'SciLit' Category
Clonal evolution in breast cancer revealed by single nucleus genome sequencing : Nature : Nature Publishing Group
October 13, 2014Realizing the promise of cancer predisposition genes : Nature : Nature Publishing Group
October 13, 2014Realizing the promise of cancer predisposition genes : Nature : Nature Publishing Group
http://www.nature.com/nature/journal/v505/n7483/full/nature12981.html
@markgerstein: .@rahman_nazneen mentions: Realizing the promise of cancer predisposition genes [of which there’s >100]
http://t.co/BbGNK6VJko #BTGCG14
Tracing the tumor lineage — ScienceDirect
October 13, 2014@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
OncodriveCLUST: exploiting the positional clustering of somatic mutations to identify cancer genes
October 13, 2014@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
Access : Evolution of the cancer genome : Nature Reviews Genetics
October 13, 2014@markgerstein: .@nlbigas mentions: Evolution of the cancer genome http://t.co/DLwrOlzqch Drivers provide selective advantage #BTGCG14
Criteria for Inference of Chromothripsis in Cancer Genomes — ScienceDirect
October 13, 2014@markgerstein: Korbel mentions: Criteria for Inference of
Chromothripsis in Cancer Genome
http://t.co/TslcrZsmNv #BTGCG14
http://www.sciencedirect.com/science/article/pii/S0092867413002122
Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, Kondrashov AS, Sunyaev SR. A method and server for predicting damaging missense mutations. Nature Methods (2010) 7: 248-249.
October 11, 2014Server 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.
Multi-platform assessment of transcriptome profiling using RNA-seq in the ABRF next-generation sequencing study : Nature Biotechnology : Nature Publishing Group
October 10, 2014Multiplatform 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: Trends in Biotechnology
October 9, 2014Signaling #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).
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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.
