mw55309

esearch -db bioproject -query "ENTREZ QUERY HERE" | efetch -format docsum | xtract -pattern DocumentSummary -element Project_Acc Project_Title Project_Target_Material Project_MethodType Project_Description Sequencing_Status 

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# get the data as per https://www.cedricscherer.com/2019/05/17/the-evolution-of-a-ggplot-ep.-1/#code
library(tidyverse)
devtools::source_gist("https://gist.github.com/Z3tt/301bb0c7e3565111770121af2bd60c11")

# convert to data.frame, attach and sort region
df <- as.data.frame(df_ratios)
attach(df)

# get rid of NAs
df <- df[ ! is.na(student_ratio),]

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Output:

```
run  at 2021-05-13 20:37:49.539609
run  at 2021-05-13 20:37:51.455154
run  at 2021-05-13 20:37:53.464305
run  at 2021-05-13 20:37:55.362489
run  at 2021-05-13 20:37:57.314623
wait at 2021-05-13 20:37:57.565849
wait at 2021-05-13 20:37:58.586121

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Output:

```
run  at 2021-05-13 17:32:33.076136
run  at 2021-05-13 17:32:35.094340
run  at 2021-05-13 17:32:36.977748
run  at 2021-05-13 17:32:38.854865
run  at 2021-05-13 17:32:40.755098
wait at 2021-05-13 17:32:41.120666
wait at 2021-05-13 17:32:42.447928

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Output:

```
run  at 2021-05-13 17:27:28.334900
run  at 2021-05-13 17:27:30.287149
run  at 2021-05-13 17:27:32.254218
run  at 2021-05-13 17:27:34.109459
run  at 2021-05-13 17:27:36.062005
wait at 2021-05-13 17:27:36.433094
wait at 2021-05-13 17:27:37.513581

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d <- data.frame(frac=seq(0.1,1.0,by=0.1), total=c(116,175,215,246,271,291,308,323,336,348))

plot(d$frac, d$total, pch=16, xlim=c(0,3), ylim=c(0,400))

fit <- lm(formula = total ~ I(1/frac), data=d)

nd <- data.frame(frac=seq(0.1,3.0,by=0.1))

nd$pred <- predict(fit, newdata=nd)

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x <- 0:100
y <- 100/x
plot(x,y, xlim=c(0,100), ylim=c(0,100), type="l", xlab="health", ylab="wealth", xaxt="n", yaxt="n", bty="n")

#axis(side=1, labels=FALSE, tick=NA, lty=1, lwd=1)

u <- par("usr")

# Use arrows() to draw the axis lines, adding the arrowheads
# Use the par("usr") values to specify the end points of the lines

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query:
>contig_1648_86 # 93536 # 94054 # 1 # ID=21_86;partial=00;start_type=ATG;rbs_motif=AGGAG/GGAGG;rbs_spacer=11-12bp;gc_cont=0.696
MMTLLHLISAVALLVWGTHIVRTGVLRVYGARLRRWLGGRLNRPLAFAAGTGVTALVQSS
NATALLASGFVADRLMPLATATREVLRMGDIVEGMLAAILEALRNERAGSARDLGDQDDE
LDELYTGIKLYLELIGDMKRLNSLFCAVAFSTVEEEDGDGPGLSTLSRRSRP*

subject:
>tr|A0A2E9RF79|A0A2E9RF79_9GAMM PhoU domain-containing protein OS=Alcanivorax sp. OX=1872427 GN=CL543_00625 PE=4 SV=1
MMTLLHLXSAVALLVWGTHIVRTGVLRVYGARLRRWLGGRLNRPLAFAAGTGVTALVQSS
NATALLASGFVADRLMPLATALAILLGADLGTALMARVLTLDLSWLWPLLVIAGVPLFLS

mw55309

Download Miniconda

Main env (python 3.5):
	- snakemake
	- Ete3?
	- CheckM
	- HMMER
	- pplacer
	- FastTree
	- Prodigal

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## functions

plot.histograms <- function(cname="", flip=FALSE, xlim) {

	dse <- density(setosa[, cname])
	dve <- density(versicolor[, cname])
	dvi <- density(virginica[, cname])

	ymax <- max(c(dse$y, dve$y, dvi$y))