Archive for the 'SciLit' Category

Aging increases cell-to-cell transcriptional variability upon immune stimulation | Science

April 21, 2017

#Aging increases cell-to-cell transcriptional variability upon immune stimulation, but just for 225 up-reg. genes http://science.ScienceMag.org/content/355/6332/1433

Impacts of Neanderthal-Introgressed Sequences on the Landscape of Human Gene Expression: Cell

April 16, 2017

"Impacts of Neanderthal-Introgressed Sequences on the Landscape of Human Gene Expression"

(http://www.sciencedirect.com/science/article/pii/S0092867417301289)

#Neanderthal-Introgressed Sequences [&]…Gene Expression http://www.Cell.com/cell/abstract/S0092-8674(17)30128-9?_returnURL=http%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS0092867417301289%3Fshowall%3Dtrue ASE for Hn SNPs shows lower #brain expression vs reference

The International Human Epigenome Consortium: A Blueprint for Scientific Collaboration and Discovery. – PubMed – NCBI

April 16, 2017

#IHEC: A Blueprint for…Collab. & Discovery
http://www.Cell.com/cell/abstract/S0092-8674(16)31528-8?_returnURL=http%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS0092867416315288%3Fshowall%3Dtrue Summary bullets on heterogeneity, disease, rel. to SNPs, comp. tools

NAR Breakthrough Article: denovo-db: a compendium of human de novo variants

April 3, 2017

.@denovodb: a compendium of [initially ~33K] human de novo variants w. phenotype, freely downloadable as a TSV table
https://academic.OUP.com/nar/article-lookup/doi/10.1093/nar/gkw865

QT:{{”
As of July 2016, denovo-db contained 40 different studies and 32,991 de novo variants from 23,098 trios. Database features include basic variant information (chromosome location, change, type); detailed annotation at the transcript and protein levels; severity scores; frequency; validation status; and, most importantly, the phenotype of the individual with the variant.
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denovo-db.gs.washington.edu/denovo-db/
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5210614/

Network analytics in the age of big data | Science

April 2, 2017

#Network analytics in the age of #BigData
http://science.ScienceMag.org/content/353/6295/123.full Emphasizes analyzing connectivity of graph structures (eg motifs) v nodes

QT:{{”
To mine the wiring patterns of networked data and uncover the functional organization, it is not enough to consider only simple descriptors, such as the number of interactions that each entity (node) has with other entities (called node degree), because two networks can be identical in such simple descriptors, but have a very different connectivity structure (see the figure). Instead, Benson et al. use higher-order descriptors called graphlets (e.g., a triangle) that are based on small subnetworks obtained on a subset of nodes in the data that contain all interactions that appear in the data (3). They identify network regions rich in instances of a particular graphlet type, with few of the instances of the particular graphlet crossing the boundaries of the regions. If the graphlet type is specified in advance, the method can uncover the nodes interconnected by it, which enabled Benson et al. to group together 20 neurons in the nematode worm neuronal network that are known to control a particular type of movement. In this way, the method unifies the local wiring patterning with higher-order structural modularity imposed by it, uncovering higher-order functional regions in networked data. “}}

whole genome assembly from Hi-C data

April 2, 2017

De novo assembly of the A aegypti genome using #HiC, by @erezaterez et al http://science.ScienceMag.org/content/early/2017/03/22/science.aal3327.full Works on human too, w. promise for #SVs

De novo assembly of the Aedes aegypti genome using Hi-C yields chromosome-length scaffolds

Olga Dudchenko1,2,3,4,
Sanjit S. Batra1,2,3,*,
Arina D. Omer1,2,3,*,
Sarah K. Nyquist1,3,
Marie Hoeger1,3,
Neva C. Durand1,2,3,
Muhammad S. Shamim1,2,3,
Ido Machol1,2,3,
Eric S. Lander5,6,7,
Aviva Presser Aiden1,2,8,9,
Erez Lieberman Aiden1,2,3,4,5,†

Science 23 Mar 2017:
eaal3327
DOI: 10.1126/science.aal3327

on whole genome assembly from Hi-C reads. There is also some info on chromosomal rearrangement from Hi-C.

Mechanisms underlying structural variant formation in genomic disorders : Nature Reviews Genetics : Nature Publishing Group

April 1, 2017

Mechanisms underlying #SV formation in…disorders
http://www.Nature.com/nrg/journal/v17/n4/abs/nrg.2015.25.html Highlights importance of repeats in creating genomic plasticity

Nat Rev Genet. 2016 Apr;17(4):224-38. doi: 10.1038/nrg.2015.25. Epub 2016 Feb 29.
Mechanisms underlying structural variant formation in genomic disorders. Carvalho CM, Lupski JR

Phys. Rev. E 92, 032810 (2015) – Thermodynamic characterization of networks using graph polynomials

March 31, 2017

Thermodynamic characterization of #networks using graph polynomials https://journals.APS.org/pre/abstract/10.1103/PhysRevE.92.032810 Application to the stockmarket & biological data

Genes, environment, and “bad luck” | Science

March 26, 2017

Genes, environment & bad luck
http://science.ScienceMag.org/content/355/6331/1266 To what degree are #cancer mutations due to replication error (3rd factor), not 1st 2?

discusses R v D correlation

Stem cell divisions, somatic mutations, cancer etiology, and cancer prevention Cristian Tomasetti1,2,*, Lu Li2, Bert Vogelstein3,*
Science 24 Mar 2017:
Vol. 355, Issue 6331, pp. 1330-1334
DOI: 10.1126/science.aaf9011
http://science.sciencemag.org/content/355/6331/1330

Education in Computational Biology Today and Tomorrow

March 25, 2017

Education in #CompBio, by @bffo & @joannealisonfox
http://journals.PLOS.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003391 Keeping up in a rapidly changing field. Will implement some @Yale

QT:{{”
“These initiatives help to extend computational biology beyond the domain of specialized laboratories. Researchers, at all levels, need to keep themselves up-to-date with the quickly changing world of computational biology, and trainees need programs where bioinformatics skills are embedded so they can have comprehensive training. New bioinformatics workflows can be adopted more widely if education efforts keep pace. As previously pointed out , starting early is also very important. There is still room for programs that capture the excitement and enthusiasm of secondary school students and convey the potential of computational biology to the public. We welcome additions to the PLOS Computational Biology “Bioinformatics: Starting Early” collection (www.ploscollections.org/cbstartingearly).

We would like to involve the community in this endeavor. With this editorial, we are calling out to educators and researchers who have experience in teaching, specifically, those keen to raise the expectations and the inquisitiveness of the next generation of biologists. The Education collection will continue to publish leading edge education materials in the form of tutorials that can be used in a “classroom” setting (whatever that may mean nowadays: stated more generically, “the places where people learn”). We will continue to encourage articles set in the context of addressing a particular biological question and, as mentioned above, we welcome new “primers” and “quick guides.” We will also be inviting tutorials from the various computational meetings. A new category of papers that is in the pipeline for the Education collection is the “Quick Tips” format, the first of which was just published . The “Quick Tips” articles address specific tools or databases that are in wide use in the community.
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