Kinome-wide Decoding of #Network-Attacking Mutations Rewiring Cancer http://www.cell.com/cell/abstract/S0092-8674(15)01108-3 Mapping NAMs onto well-known signaling pathways
Evolutionary Origins of Hierarchy
http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1004829 Simulations explain how connection cost drives formation in sparse #networks
http://science.sciencemag.org/content/352/6285/600.long
Yang I. Li1,
Bryce van de Geijn2,
Anil Raj1,
David A. Knowles3,4,
Allegra A. Petti5,
David Golan1,
Yoav Gilad2,*,
Jonathan K. Pritchard1,6,7,*
http://bioinformatics.oxfordjournals.org/content/early/2015/11/05/bioinformatics.btv565
GERV: a statistical method for generative evaluation of regulatory variants for transcription factor binding
Haoyang Zeng
Tatsunori Hashimoto
Daniel D. Kang
David K. Gifford
How does multiple-testing correction work
http://www.nature.com/nbt/journal/v27/n12/abs/nbt1209-1135.html Intuition for teaching: genome-wide error rate on a single gene v family
Reg. variation in cplx traits by @LeonidKruglyak
http://www.nature.com/nrg/journal/v16/n4/full/nrg3891.html nice teaching figure for #eQTLs, showing how mostly cis + hotspots http://www.nature.com/nrg/journal/v16/n4/full/nrg3891.html
Landscape of somatic retrotransposition in human cancers
http://science.sciencemag.org/content/337/6097/967.long 194 insertions in 43 WGS, mostly L1s w. ~50% near genes
Landscape of Somatic Retrotransposition in Human Cancers
Eunjung Lee1,2,
Rebecca Iskow3,
Lixing Yang1,
Omer Gokcumen3,
Psalm Haseley1,2,
Lovelace J. Luquette III1,
Jens G. Lohr4,5,
Christopher C. Harris6,
Li Ding6,
Richard K. Wilson6,
David A. Wheeler7,
Richard A. Gibbs7,
Raju Kucherlapati2,8,
Charles Lee3,
Peter V. Kharchenko1,9,*,
Peter J. Park1,2,9,*,
The Cancer Genome Atlas Research Network
Science 24 Aug 2012:
Vol. 337, Issue 6097, pp. 967-971
DOI: 10.1126/science.1222077
The paper describes the analysis of transposable elements (TE) insertions at single nucleotide resolution in 43 high coverage whole genome datasets from five cancer types. The authors developed a computational method that uses as input paired-end whole genome sequence data from tumor and normal sample aligned against a reference genome and a custom repeat assembly of TE sequences to detect the position and mechanism of TE insertion. The method identified 194 TE insertions (183 L1s, 10 Alus and 1 ERV). The diversity in the frequency of TE insertions in the same cancer type (ranging from 45-60 to 106 events per tumour) suggests the presence of tumour subtypes with respect to TE activity.
By intersecting the 194 TE with genome annotation, the authors found that 64 TE are in known genes (in UTRs and introns), most of which are implicated in tumour suppressor functions. Also, the TE events targeted genes that are frequently/recurrently mutated, suggesting that TE insertions can potentially contribute to cancer development. Gene expression analysis showed that TE insertion results in significantly decreasing the expression levels for the host gene. TE orientation also has an impact on the expression level, with antisense insertion being less disruptive.
Comparing the germline and somatic insertion sites shows notable differences. Germline L1s are significantly more depleted from genes compared to somatic L1s. Somatic L1s are significantly overrepresented within regions of DNA hypomethylation suggesting the DNA
hypomethylation promoted L1 integration.