images
https://use.jetstream-cloud.org/application/images
Thursday, June 29, 2017
The 2-micron plasmid as a nonselectable, stable, high copy number yeast vector
https://www.ncbi.nlm.nih.gov/pubmed/1857755
Plasmid. 1991 Mar;25(2):81-95.
The 2-micron plasmid as a nonselectable, stable, high copy number yeast vector.
Abstract
- PMID:
- 1857755
Wednesday, June 28, 2017
yeast DIP analysis
using the 2017 data sets. Median interactions per gene: 4, average is 8.8 for all genes.
list.files()## [1] "_explore_dip.html" "_explore_dip.Rmd" "Scere20170205.csv"
## [4] "Scere20170205.txt" "Scere20170205.xlsx"#library(xlsx)
tb = read.table("Scere20170205.txt", header=T, sep="\t", row.names=NULL)
#tb = read.xlsx("Scere20170205.xlsx", 1)
Visual check show that there are interactions between yeast proteins and non-yeast proteins (such as human and flys) Some the column names are move by one-column.
big2small = function(char1, char2) {
if ( char1 > char2) {
return( c( char1, char2) )
} else {
return( c(char2, char1) )
}
}for( i in 1:length(tb[,1])) {
#for( i in 1:19) {
pair = big2small(as.character(tb[i, 1]), as.character(tb[i, 2]))
tb$pairID[i] = paste( pair[1], pair[2], sep = "::")
}
How many ExE interactions?
unique_EEpairs = unique(tb$pairID)
all_names = c();
for( pair in unique_EEpairs) {
all_names = c(all_names, unlist( strsplit(pair, split="::") ))
}degree = table(all_names)
str(degree)## 'table' int [1:5176(1d)] 3 68 7 6 9 9 56 5 5 1 ...
## - attr(*, "dimnames")=List of 1
## ..$ all_names: chr [1:5176] "DIP-1000N|refseq:NP_014991|uniprotkb:P12689" "DIP-1001N|refseq:NP_010241|uniprotkb:Q07350" "DIP-1002N|refseq:NP_010206|uniprotkb:Q07468" "DIP-1003N|refseq:NP_010131|uniprotkb:P25441" ...mean(degree)## [1] 8.878284median(degree)## [1] 4order char in R
_order_ORF
H Qin
6/28/2017
big2small = function(char1, char2) {
if ( char1 > char2) {
return( c( char1, char2) )
} else {
return( c(char2, char1) )
}
}name1 = c("apple", "banana", "dog", "cat")
name2 = c("banana", "apple", "cat", "dog")
tb = cbind(name1, name2)for( i in 1:4){
return= big2small(tb[i, 1], tb[i, 2])
print(return)
}## name2 name1
## "banana" "apple"
## name1 name2
## "banana" "apple"
## name1 name2
## "dog" "cat"
## name2 name1
## "dog" "cat"Tuesday, June 27, 2017
yeast aging, protein biogenesis, translation control
[Janssens+Al:2015] Janssens, Georges E; Meinema, Anne C; Gonzalez, Javier; Wolters, Justina C; Schmidt, Alexander; Guryev, Victor; Bischoff, Rainer; Wit, Ernst C; Veenhoff, Liesbeth M; & Heinemann, Matthias (2015). 'Protein biogenesis machinery is a driver of replicative aging in yeast.' eLife. 4, pp. e08527.
[Blank+Al:2017] Blank, Heidi M; Perez, Ricardo; He, Chong; Maitra, Nairita; Metz, Richard; Hill, Joshua; Lin, Yuhong; Johnson, Charles D; Bankaitis, Vytas A; Kennedy, Brian K; Aramayo, Rodolfo; & Polymenis, Michael (2017). '**Translational control of lipogenic enzymes in the cell cycle of synchronous, growing yeast cells.**' *The EMBO journal*.
Antibiotic could protect against neurodegenerative diseases during aging
Translation attenuation by minocycline enhances longevity and proteostasis in old post-stress-responsive organisms, https://elifesciences.org/articles/40314
Calico elife paperGoogle Drive install, Ubuntu Virtual machine (error)
Inside of Ubuntu virtual machine:
https://askubuntu.com/questions/544646/how-to-install-google-drive-on-ubuntu-14-04
https://askubuntu.com/questions/544646/how-to-install-google-drive-on-ubuntu-14-04
sudo add-apt-repository ppa:nilarimogard/webupd8
sudo apt-get update
sudo apt-get install grive hqin@qin2-VirtualBox:~$ sudo /usr/bin/grive
Please run grive with the "-a" option if this is the first time you're accessing your Google Drive!
hqin@qin2-VirtualBox:~$ sudo /usr/bin/grive -a
Then copy-paste a link, loginto GoogleAcccount to get an authentication code.
It then hangs.
Monday, June 26, 2017
Yeast genetic map, thecellmap.org
http://thecellmap.org/costanzo2016/
Three zip files
-rw-r--r--@ 1 hqin staff 497M Jun 26 09:58 Raw genetic interaction datasets- Pair-wise interaction format.zip
-rw-r--r--@ 1 hqin staff 34M Jun 26 09:58 Raw genetic interaction datasets- Matrix format.zip
-rw-r--r--@ 1 hqin staff 147M Jun 26 09:59 Genetic interaction profile similarity matrices.zip
Expand to three folders
drwxr-xr-x@ 7 hqin staff 238B Dec 6 2016 Data File S1. Raw genetic interaction datasets: Pair-wise interaction format
drwxr-xr-x@ 18 hqin staff 612B Dec 6 2016 Data File S2. Raw genetic interaction datasets: Matrix format
drwxr-xr-x@ 5 hqin staff 170B Oct 20 2016 Data File S3. Genetic interaction profile similarity matrices
The global interaction dataset is based on the construction and analysis of ~23 million double mutants which identified 550,000 negative and 350,000 positive genetic interactions and covers ~90% of all yeast genes as either array and/or query mutants. The global genetic interaction dataset includes three different genetic interaction maps. First, 3,589 nonessential deletion query mutant strains were screened against the deletion mutant array covering 3,892 nonessential genes to generate a nonessential x nonessential (NxN) network. Second, 1,162 TS query mutant strains representing 804 essential genes were also screened against the nonessential deletion mutant array to generate an essential x nonessential (ExN) network. Finally, 2,241 nonessential deletion mutant query strains and 1,108 TS query mutant strains, corresponding to 795 essential genes, were crossed to an array of 792 TS strains, spanning 561 unique essential genes, to generate an expanded ExN network and an essential x essential (ExE) network. The data can be downloaded from the links below. Note that we continue to map genetic interactions for remaining gene pairs not represented in this dataset and we will update the data and networks as new interactions are generated.
Reference
http://hongqinlab.blogspot.com/2013/06/notes-costanzo-sga-2009.html
20180131Wed
No self-interactions have been found in the cellmap network (using the stringent criteria). Therefore, to prepare the networks for permutation, I ordered all gene pairs alphabetically (i.e., both [A,B] and [B,A] will be changed to [A,B]), and then removed the redundant pairs (i.e., only one [A,B] was left).
However, after that I found that there are 878,704 overall interactions (both positive and negative), 540,396 negative interactions and 353,117 positive interactions.
Now the problem is that, neg + pos - all ~ 100k ... i.e., there are ~100k interactions have been found in both negative and positive sets.
Sunday, June 25, 2017
alternative to ODrive
multiple cloud storage management
http://www.tpsort.com/similar-to/3020-top-15-odrive-alternative-and-similar-softwares
http://www.tpsort.com/similar-to/3020-top-15-odrive-alternative-and-similar-softwares
Saturday, June 24, 2017
Thursday, June 22, 2017
Tuesday, June 20, 2017
AWS Amazon education and research grant
I used my UTC email. An verification code was sent to verify my application to AWS Educate.
https://aws.amazon.com/grants/
https://youtu.be/6QOjfvefP60
AWS Cloud Credits for Research
https://aws.amazon.com/grants/
https://youtu.be/6QOjfvefP60
AWS Cloud Credits for Research
The AWS Cloud Credits for Research program (formerly AWS Research Grants) supports researchers who seek to:
- Build cloud-hosted publicly available science-as-a-service applications, software, or tools to facilitate their future research and the research of their community.
- Perform proof of concept or benchmark tests evaluating the efficacy of moving research workloads or open data sets to the cloud.
- Train a broader community on the usage of cloud for research workloads via workshops or tutorials.
AWS Educate is Amazon’s global initiative to provide students and educators with the resources needed to greatly accelerate cloud-related learning endeavors and to help power the entrepreneurs, workforce, and researchers of tomorrow.
UTC advising 2017-2018,
Undergraduate catalogue
College of Engineering and Computer Science
http://catalog.utc.edu/content.php?catoid=21&navoid=725Computer Science and Engineering
Go to information for this department.Programs
Bachelor
- • Computer Science: Computer Engineering, B.S.
- • Computer Science: Cyber Security, B.S.
- • Computer Science: Data Science, B.S.
- • Computer Science: Software Systems, B.S.
- • Computer Science: STEM Education, B.S.
Minor
The incoming freshmen have been pre-registered in May by Laura Bass for a Fall schedule. There are several versions of a fall schedule which is based on the student’s ACT scores for their Math placement.
The ideal schedule:
MATH 1950 – 4 hours
CPSC 1100 – 4 hours
ENGL 1010 – 3 hours
General Education – 3-6 Hours
The above combination may vary for students who do not have an ACT score of 28 (and/or AP credits, joint enrolled HS credits, etc. for math courses that allow them to start in MATH 1950/Calc. I).
Students who may have an ACT score below 19 will have all Gen Ed courses scheduled for now but are being encouraged to either take the Math Dept’s Step Ahead Summer program in August for an opportunity to exit developmental Math or retake the MATH ACT residual for a higher score. If they do not exit developmental Math before classes start they will be required to take developmental Math in Fall at Chatt State or they will be very behind.
current date in R markdown
date: "May 4 2017 - `r format(Sys.time(), '%d %B, %Y')`"
See
https://stackoverflow.com/questions/23449319/yaml-current-date-in-rmarkdown
Tuesday, June 13, 2017
mediator effect ion t0 between GFlex and RFlex
Update on July 13, 2018:
ACME: average causal mediation effects
ADE: average direct effects
So, proportion of mediation = ACME / (ACME + ADE)
I use G and R estimated using Flexsurv package because there independently estimated from t0.
===========
ACME: average causal mediation effects
ADE: average direct effects
So, proportion of mediation = ACME / (ACME + ADE)
I use G and R estimated using Flexsurv package because there independently estimated from t0.
===========
Mediation test on natural isolates. short version
h qin
June 13, 2017
rm(list=ls())
#setwd("~/github/0.network.aging.prj.bmc/0a.rls.fitting")
setwd("~/github/bmc_netwk_aging_manuscript/R1/0.nat.rls.fitting")
library('flexsurv')## Loading required package: survivalsource("../lifespan.r")Parse strains from files
files = list.files(path="../qinlab_rls/", pattern="rls.tab")
tmp1 = gsub("\\d{6}.", "", files)
redundant_strains = gsub(".rls.tab", "", tmp1)
strains = sort( unique( redundant_strains ))
strains## [1] "101S" "BY4716" "BY4741" "BY4742"
## [5] "BY4743" "JSBY4741" "M1-2" "M13"
## [9] "M14" "M2-8" "M22" "M32"
## [13] "M34" "M5" "M8" "RM112N"
## [17] "S288c" "SGU57" "sir2D.4741a" "sir2D.4742"
## [21] "sir2DSIR2.4742" "SK1" "W303" "YPS128"
## [25] "YPS163"
Take files from natural isolates
my.strains=c("101S", "M1-2","M13","M14","M2-8","M22","M32","M34","M5","M8","RM112N","S288c","SGU57", "YPS128","YPS163")
files2=c();
for( i in 1:length(my.strains)){
files2 = c( files2, files[grep(my.strains[i], files)]);
}
report = data.frame(cbind(my.strains))
report$samplesize = NA; report$R=NA; report$t0=NA; report$n=NA; report$G=NA; report$longfilename=NA;
files = files2;
strains = my.strains; Now, fit all RLS data sets by strains
for( i in 1:length(report[,1])){
#for( i in 3:4){
my.files = files[grep(strains[i], files)]
report$longfilename[i] = paste(my.files, collapse = "::");
tb = read.table( paste("../qinlab_rls/",my.files[1],sep=''), sep="\t")
if( length(my.files)> 1){
for( fi in 2:length(my.files)) {
tmp.tb = read.table( paste("../qinlab_rls/",my.files[fi],sep=''), sep="\t")
tb = rbind( tb, tmp.tb)
}
}
report$samplesize[i] = length(tb[,1])
GompFlex = flexsurvreg(formula = Surv(tb[,1]) ~ 1, dist = 'gompertz')
WeibFlex = flexsurvreg(formula = Surv(tb[,1]) ~ 1, dist = 'weibull')
report$avgLS[i] = mean(tb[,1])
report$stdLS[i] = sd(tb[,1])
report$CV[i] = report$stdLS[i] / report$avgLS[i]
report$GompGFlex[i] = GompFlex$res[1,1]
report$GompRFlex[i] = GompFlex$res[2,1]
report$GompLogLikFlex[i] = round(GompFlex$loglik, 1)
report$GompAICFlex[i] = round(GompFlex$AIC)
report$WeibShapeFlex[i] = WeibFlex$res[1,1]
report$WeibRateFlex[i] = WeibFlex$res[2,1]
report$WeibLogLikFlex[i] = round(WeibFlex$loglik, 1)
report$WeibAICFlex[i] = round(WeibFlex$AIC)
#set initial values
Rhat = report$GompRFlex[i]; # 'i' was missing. a bug costed HQ a whole afternoon.
Ghat = report$GompGFlex[i];
nhat = 6;
t0= (nhat-1)/Ghat;
fitBinom = optim ( c(Rhat, t0, nhat), llh.binomialMortality.single.run,
lifespan=tb[,1],
#method='SANN') #SANN needs control
method="L-BFGS-B",
lower=c(1E-10, 1, 4), upper=c(1,200,20) );
report[i, c("R", "t0", "n")] = fitBinom$par[1:3]
report$G[i] = (report$n[i] - 1)/report$t0[i]
}
report2 = report; Mediation test on Gflex <–t0 <– RFlex
Hong thinks the results indicate the t0 is the mediator from Flex to GFlex, but not sure.
library(mediation)## Loading required package: MASS## Loading required package: Matrix## Loading required package: mvtnorm## Loading required package: sandwich## mediation: Causal Mediation Analysis
## Version: 4.4.5set.seed(20170801)
report2$log10GompRFlex = log10(report2$GompRFlex)
med.fit = lm(t0 ~ log10GompRFlex, data=report2)
summary(med.fit)##
## Call:
## lm(formula = t0 ~ log10GompRFlex, data = report2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.2238 -7.6956 -0.6106 1.5195 22.5871
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 99.564 20.269 4.912 0.000284 ***
## log10GompRFlex 19.967 7.429 2.688 0.018617 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.928 on 13 degrees of freedom
## Multiple R-squared: 0.3572, Adjusted R-squared: 0.3078
## F-statistic: 7.225 on 1 and 13 DF, p-value: 0.01862out.fit = lm(GompGFlex ~ t0 + log10GompRFlex, data=report2)
summary(out.fit)##
## Call:
## lm(formula = GompGFlex ~ t0 + log10GompRFlex, data = report2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.008037 -0.004385 -0.001282 0.001773 0.012763
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1818701 0.0236582 7.687 5.64e-06 ***
## t0 -0.0020169 0.0001916 -10.529 2.05e-07 ***
## log10GompRFlex -0.0095046 0.0063994 -1.485 0.163
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.006857 on 12 degrees of freedom
## Multiple R-squared: 0.9447, Adjusted R-squared: 0.9355
## F-statistic: 102.5 on 2 and 12 DF, p-value: 2.861e-08med.out <- mediate(med.fit, out.fit, treat = "log10GompRFlex", mediator = "t0", robustSE = TRUE, sims = 100)
summary(med.out)##
## Causal Mediation Analysis
##
## Quasi-Bayesian Confidence Intervals
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME -0.04383 -0.08096 -0.01 <2e-16 ***
## ADE -0.00718 -0.02469 0.01 0.46
## Total Effect -0.05101 -0.08276 -0.02 <2e-16 ***
## Prop. Mediated 0.86767 0.46191 1.23 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sample Size Used: 15
##
##
## Simulations: 100Mediation test 2 on Rflex <–t0 <– GFlex
Hong thinks this is negative result, which means t0 works only in one direction.
med.fit = lm(t0 ~ GompGFlex, data=report2)
summary(med.fit)##
## Call:
## lm(formula = t0 ~ GompGFlex, data = report2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.6401 -1.9424 -0.8670 -0.0658 8.1513
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 94.999 3.723 25.52 1.72e-12 ***
## GompGFlex -427.325 31.369 -13.62 4.50e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.168 on 13 degrees of freedom
## Multiple R-squared: 0.9345, Adjusted R-squared: 0.9295
## F-statistic: 185.6 on 1 and 13 DF, p-value: 4.503e-09out.fit = lm(log10GompRFlex ~ t0 + GompGFlex, data=report2)
summary(out.fit)##
## Call:
## lm(formula = log10GompRFlex ~ t0 + GompGFlex, data = report2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.52342 -0.24509 0.04331 0.22083 0.34492
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.002954 2.387477 -0.001 0.999
## t0 -0.017838 0.024884 -0.717 0.487
## GompGFlex -16.337520 10.999937 -1.485 0.163
##
## Residual standard error: 0.2843 on 12 degrees of freedom
## Multiple R-squared: 0.457, Adjusted R-squared: 0.3666
## F-statistic: 5.051 on 2 and 12 DF, p-value: 0.02562med.out <- mediate(med.fit, out.fit, treat = "GompGFlex", mediator = "t0", robustSE = TRUE, sims = 100)
summary(med.out)##
## Causal Mediation Analysis
##
## Quasi-Bayesian Confidence Intervals
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME 6.454 -14.468 31.46 0.74
## ADE -15.052 -45.810 8.83 0.18
## Total Effect -8.598 -18.553 -1.06 0.04 *
## Prop. Mediated -0.526 -9.794 3.54 0.74
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sample Size Used: 15
##
##
## Simulations: 100
Subscribe to:
Posts (Atom)