Thursday, June 29, 2017

jet stream cloud images

images
https://use.jetstream-cloud.org/application/images

The 2-micron plasmid as a nonselectable, stable, high copy number yeast vector



https://www.ncbi.nlm.nih.gov/pubmed/1857755


 1991 Mar;25(2):81-95.

The 2-micron plasmid as a nonselectable, stable, high copy number yeast vector.

Abstract

The endogenous 2-microns plasmid of Saccharomyces cerevisiae has been used extensively for the construction of yeast cloning and expression plasmids because it is a native yeast plasmid that is able to be maintained stably in cells at high copy number. Almost invariably, these plasmid constructs, containing some or all 2-microns sequences, exhibit copy number levels lower than 2-microns and are maintained stably only under selective conditions. We were interested in determining if there was a means by which 2-microns could be utilized for vector construction, without forfeiting either copy number or nonselective stability. We identified sites in the 2-microns plasmid that could be used for the insertion of genetic sequences without disrupting 2-microns coding elements and then assessed subsequent plasmid constructs for stability and copy number in vivo. We demonstrate the utility of a previously described 2-microns recombination chimera, pBH-2L, for the manipulation and transformation of 2-microns as a pure yeast plasmid vector. We show that the HpaI site near the STB element in the 2-microns plasmid can be utilized to clone yeast DNA of at least 3.9 kb with no loss of plasmid stability. Additionally, the copy number of these constructs is as high as levels reported for the endogenous 2-microns.
PMID:
 
1857755

MultiNet has lower density than CellMap genetic networks

So, MultiNet need to be re-visited.

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] 8.878284
median(degree)
## [1] 4

order char in R

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"

set root.dir in knitr


https://philmikejones.wordpress.com/2015/05/20/set-root-directory-knitr/

Tuesday, June 27, 2017

AWS research grant application

AWS research grant application, receipt

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*.

blackboard import packages

move course contents to another course

https://youtu.be/XhMbLoKb2Bs

UTC Course merge request form

UTC Course merge request form

http://www.utc.edu/learn/instructor-resources/utclearn-merge.php

Google 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

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.


Correction: e should be epsilon.  abs(epsilon)>0.08 should be used for intermediate criteria. 


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.

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
The AWS Cloud Credits for Research program (formerly AWS Research Grants) supports researchers who seek to:
  1. Build cloud-hosted publicly available science-as-a-service applications, software, or tools to facilitate their future research and the research of their community.
  2. Perform proof of concept or benchmark tests evaluating the efficacy of moving research workloads or open data sets to the cloud.
  3. Train a broader community on the usage of cloud for research workloads via workshops or tutorials.


header_aws-grants
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.




aws-educate_marketing-banner

kayroplot in R,


plot along chromosome in R

https://bernatgel.github.io/karyoploter_tutorial/

UTC advising 2017-2018,

Undergraduate catalogue

College of Engineering and Computer Science

http://catalog.utc.edu/content.php?catoid=21&navoid=725

Computer Science and Engineering

Go to information for this department.

Programs

Bachelor
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.







































video editing, Python



https://pypi.python.org/pypi/moviepy

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.



===========

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: survival
source("../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: 4.4.5
set.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: 0.01862
out.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-08
med.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: 100

Mediation 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-09
out.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: 0.02562
med.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