linkstream2 microblog

Massively Parallel Pipeline to Clone DNA Variants & Examine…Disease
Mutations http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1004819 CloneSeq leverages NextGen sequencing

With the advance of sequencing technologies, tens of millions of genomic variants have been discovered in the human population. However, there is no available method to date that is capable of determining the functional impact of these variants on a large scale, which has increasingly become a huge bottleneck for the development of population genetics and personal genomics. Clone-seq and comparative interactome-profiling pipeline is a first to address this issue.

Can be coupled to many readouts.

Pooled association tests for rare variants in exon-resequencing http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3032073 Simulation shows advantage of mult. rarity thresholds

Price AL, Kryukov GV, de Bakker PI, Purcell SM, Staples J, Wei LJ,
Sunyaev SR. Pooled association tests for rare variants in
exon-resequencing studies. American Journal of Human Genetics (2010)
86: 832-838.

SUMMARY

Multiple studies indicate strong association between rare variants and
resulting phenotype. This paper describes a population-genetics
simulation framework to study the influence of variant allele
frequency on the corresponding phenotype. In a prior study, causal
relationship between variants and phenotype was resolved by performing
association test on set of variants having allele frequency below a
fixed threshold. However, here it is observed that simulation
frameworks based on a variable allele frequency threshold provide
higher accuracy in association test compared to the fixed allele
frequency model. In addition, inclusion of predicted functional
effects of variants (Polyphen-2 scores) increases the accuracy of the
variable frequency threshold model. Overall, this paper describes a novel methodology, which can be
used to explore the association between rare variants and various
diseases.

http://scratch.mit.edu/

QT:{{”

n 2005, just as Mr. Silver’s referral income from the Weitz firm began to balloon, records show that he directed a state grant worth $250,000 to Dr. Taub for asbestos research, ostensibly related to the Sept. 11, 2001, terrorist attacks. In October 2006, Dr. Taub wrote to Mr. Silver to request another $250,000 grant. A few months later, the money arrived.

Both times, the plans submitted by Dr. Taub’s center said the money would go toward studying the general treatment of mesothelioma, making only passing reference to those who may have been exposed to asbestos after the attacks on the World Trade Center.
…
For his part, Dr. Taub served as an expert witness for the Weitz firm as recently as a 2013 case in federal court in Pennsylvania. Legal records show that his rate for working on the case was $1,750 per hour, plus $7,500 per day for testimony when overnight travel was required.
“}}

http://www.nytimes.com/2015/01/25/nyregion/sheldon-silvers-link-to-a-bonanza-and-a-cancer.html

Bioinformatics (2015) 31 (1): 33-39. doi: 10.1093/bioinformatics/btu615

Making novel proteins from #pseudogenes
http://bioinformatics.oxfordjournals.org/content/31/1/33.short Outcomes in 16 cases where one gets stable & functional translated products

http://bioinformatics.oxfordjournals.org/content/31/1/33.short

Here is a link for some helpful information on the new NIH format for biosketches:

http://grants.nih.gov/grants/policy/faq_biosketches.htm.

Tomasetti & Volgenstein

Science 2 January 2015:
Vol. 347 no. 6217 pp. 78-81
DOI: 10.1126/science.1260825

It’s a correlation between aggressiveness, mutations and cell division http://www.sciencemag.org/content/347/6217/78

The discovery of integrated gene networks for autism and related disorders

Fereydoun Hormozdiari
Osnat Penn
Elhanan Borenstein
Evan E. Eichler

Published in Advance November 5, 2014, doi:10.1101/gr.178855.114 Genome Res. 2015. 25: 142-154

QT:{{”
Motivated by this observation, we have developed a novel method that simultaneously integrates information from both PPI and coexpression networks to identify highly connected modules in both types of networks that are also enriched in mutations in cases and not in controls. We call this method MAGI, short for merging affected genes into integrated networks. MAGI is based on a combinatorial
optimization algorithm that aims to maximize the number of mutations in the modules while accounting for gene length and distribution of putative LoF and missense mutations in cases and controls. MAGI is generic and can be applied to any disease, given a list of de novo mutations in cases and relevant coexpression information. Using neurodevelopmental RNA-seq data from the BrainSpan Atlas
(http://www.brainspan.org/), we have applied it to exome sequence data generated from ASD, ID, epilepsy, and schizophrenia, providing a comprehensive comparison of common and specific gene modules for these diseases.
“}}

On Amazon but reviews indicate that computer interface is a bit problematic

http://www.amazon.com/gp/product/B0002W2A1I

Seems to have a complex computer interface; not available from Amazon

http://spiropd.com/