linkstream2 microblog

Make #precisionmedicine work for cancer http://www.nature.com/news/health-make-precision-medicine-work-for-cancer-care-1.17301 @MarkARubin1: >90% of…patients carry a mutation that may be drug-responsive

QT:{{"
“Hugely complicated genomic reports are rarely available in electronic form and are seldom tied to basic information about the patient. Whole-genome sequencing on tumour samples from nearly 14,000 people by the International Cancer Genome Consortium (ICGC), for instance, has revealed nearly 13 million mutations across the genome.

…
Since 2013, working with a team of computational biologists from Weill Cornell and the Centre for Integrative Biology at the University of Trento in Italy, my colleagues and I have conducted a pilot programme to determine the feasibility of tying genomic to clinical data in real time. So far, we have created easy-to-read reports for 250 people with cancer.
…
We have discovered that more than

"more than 90% of our patients carry a mutation that may be responsive to a known drug — although less than 10% of the patients may be eligible for a clinical trial either for logistical reasons or because there is insufficient evidence to warrant trying a non-approved drug.”
"}}

BRAF #Pseudogene Functions as a Competitive Endogenous RNA; [Shows it] induces Lymphoma [after alteration, in mice]
http://www.cell.com/cell/abstract/S0092-8674(15)00244-5

Cell. 2015 Apr 9;161(2):319-32. doi: 10.1016/j.cell.2015.02.043. Epub 2015 Apr 2.

Karreth FA1, Reschke M1, Ruocco A1, Ng C1, Chapuy B2, Léopold V1, Sjoberg M3, Keane TM3, Verma A4, Ala U1, Tay Y1, Wu D5, Seitzer N1, Velasco-Herrera Mdel C3, Bothmer A1, Fung J1, Langellotto F6, Rodig SJ7, Elemento O4, Shipp MA2, Adams DJ3, Chiarle R8, Pandolfi PP9.

Abstract
Research over the past decade has suggested important roles for pseudogenes in physiology and disease. In vitro experiments
demonstrated that pseudogenes contribute….

In Search of #Bayesian Inference
http://cacm.acm.org/magazines/2015/1/181628-in-search-of-bayesian-inference/fulltext Nice intuition on priors in recovering air-crash wreckage & analyzing mammographs

QT:{{”

In its most basic form, Bayes’ Law is a simple method for updating beliefs in the light of new evidence. Suppose there is some statement A that you initially believe has a probability P(A) of being correct (what Bayesians call the “prior” probability). If a new piece of evidence, B, comes along, then the probability that A is true given that B has happened (what Bayesians call the “posterior” probability) is given by

P(A|B)=P(B|A) P(A) / P(B)

where P(B|A) is the likelihood that B would occur if A is true, and P (B) is the likelihood that B would occur under any circumstances.

Consider an example described in Silver’s book The Signal and the Noise: A woman in her forties has a positive mammogram, and wants to know the probability she has breast cancer. Bayes’ Law says that to answer this question, we need to know three things: the probability that a woman in her forties will have breast cancer (about 1.4%); the probability that if a woman has breast cancer, the mammogram will detect it (about 75%); and the probability that any random woman in her forties will have a positive mammogram (about 11%). Putting these figures together, Bayes’ Law—named after the Reverend Thomas Bayes, whose manuscript on the subject was published posthumously in 1763—says the probability the woman has cancer, given her positive mammogram result, is just under 10%; in other words, about 9 out of 10 such mammogram results are false positives.

In this simple setting, it is clear how to construct the prior, since there is plenty of data available on cancer rates. In such cases, the use of Bayes’ Law is uncontroversial, and essentially a tautology—it simply says the woman’s probability of having cancer, in light of her positive mammogram result, is given by the proportion of positive mammograms that are true positives. Things get murkier when
statisticians use Bayes’ rule to try to reason about one-time events, or other situations in which there is no clear consensus about what the prior probabilities are. For example, large passenger airplanes do not crash into the ocean very often, and when they do, the
circumstances vary widely. In such cases, the very notion of prior probability is inherently subjective; it represents our best belief, based on previous experiences, about what is likely to be true in this particular case. If this initial belief is way off, we are likely to get bad inferences.

“}}

Changes in [DHS] #regulatory element activity…[over 3 primates] associated w/ altered…expression & pos. selection
http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1002789

DHS across 3 primates finds species specific sites associated with differential expression & positive selection

Shibata Y, Sheffield NC, Fedrigo O, Babbitt CC, Wortham M, Tewari AK, London D, Song L, Lee BK, Iyer VR, Parker SC, Margulies EH, Wray GA, Furey TS, Crawford GE*. Extensive evolutionary changes in regulatory element activity during human origins are
associated with altered gene expression and positive selection. PLoS Genet. 2012 Jun; 8(6):e1002789. doi: 10.1371/journal.pgen.1002789. Epub 2012 Jun 28. PubMed PMID: 22761590; PubMed Central PMCID: PMC3386175

SUMMARY (from csds):

The study is focused on analyzing genotype-phenotype correlation by looking at the evolution of DHS sites across three primate genomes: human, chimp and macaque. By comparing the data they were able to identify common DHS sites across the three species (sites that show similar DHS levels) and also species-specific sites. All the assays were supported by ChiP experiments. The study identified >2000 regulatory elements that were gained/lost since the divergence of
human and chimp. Looking at DNase and RNAseq data the authors show that the enrichment of regulatory elements next to genes with species-specific expression, suggests that the gain or loss of DHS sites impacts transcript abundance. The human DHS sites were enhanced for chromatin marks predictive of enhancers, while common regions were preferentially associated with promoters and insulators. By looking at species specificity, they found that species-specific DHS gains are cell type specific while both species specific DHS gains and losses are subject to positive selection. The common DHS sites are conserved and are suggested to have roles involving transcription and general housekeeping.

Circadian #Enhancers Coordinate… Rhythmic… Transcription http://www.cell.com/cell/abstract/S0092-8674%2814%2901310-5
Periodicity helps find #eRNAs/enhancers & their targets

A large set [1171] of rare complete human knockouts
http://www.nature.com/ng/journal/vaop/ncurrent/full/ng.3243.html ~8% Icelanders have one; from a larger set of ~5K genes w/ #LOFs

Also:

In Iceland’s DNA, New Clues to Disease-Causing Genes – NYTimes.com
http://www.nytimes.com/2015/03/26/science/in-icelands-dna-clues-to-what-genes-may-cause-disease.html

by Carl Zimmer
QT:{{”
The Decode researchers looked for human knockouts in Iceland — and found a lot of them. Nearly 8 percent of Icelanders lack a working version of a gene. All told, the Decode team identified 1,171 genes shut down in Icelandic knockouts.
In a 2012 study, Dr. MacArthur and his colleagues were able to identify just 253 genes knocked out in humans.
“}}

GM microbes…that can’t escape the lab
http://www.nature.com/news/gm-microbes-created-that-can-t-escape-the-lab-1.16758 Repurposing stops to synthetic amino acids; useful for biofuels & communities

Church & Isaacs

http://www.nature.com/ng/journal/vaop/ncurrent/full/ng.3222.html

Nat Genet. 2015 Mar 2. doi: 10.1038/ng.3222. [Epub ahead of print]

Analyses of allele-specific gene expression in highly divergent mouse crosses identifies pervasive allelic imbalance.

Crowley JJ1, Zhabotynsky V1, Sun W2, Huang S3, Pakatci IK3, Kim Y1, Wang JR3, Morgan AP4, Calaway JD4, Aylor DL1, Yun Z1, Bell TA4, Buus RJ4, Calaway ME4, Didion JP4, Gooch TJ4, Hansen SD4, Robinson NN4, Shaw GD4, Spence JS1, Quackenbush CR1, Barrick CJ1, Nonneman RJ1, Kim K5, Xenakis J5, Xie Y1,Valdar W6, Lenarcic AB1, Wang W3, Welsh CE3, Fu CP3, Zhang Z3, Holt J3, Guo Z3, Threadgill DW7, Tarantino LM8, Miller DR4, Zou F5, McMillan L3, Sullivan PF9, Pardo-Manuel de Villena F4.

http://www.nature.com/ng/journal/v47/n4/full/ng.3222.html

Carefully controlled allele expt – hets midway in expr betw homs for parents for 76pct (additive model) but 25pct hets diff. from parents

ASE in…divergent mouse crosses identifies pervasive allelic imbalance, 80%, w/ 60% closely following additive model
http://www.nature.com/ng/journal/vaop/ncurrent/full/ng.3222.html

Scientists & cheesemakers gather for (microbial) culture
http://www.nature.com/news/scientists-and-cheesemakers-gather-for-microbial-culture-1.15776 #Cheese is big for #microbiome differences one can taste

Also: http://www.sciencedirect.com/science/article/pii/S0092867414007454

Microbiologists are on a quest to catalogue and control the bacteria that make each raw-milk cheese unique.
Ewen Callaway
27 August 2014

Syntrophic exchange in synthetic #microbial communities [& their evolution] http://www.pnas.org/content/111/20/E2149.abstract Trading metabolically costly amino acids