Along the way I had to replace all of the barcodes in a fastq file with new barcodes, and after flailing at an attempt to code this in bash, I punted and wrote it in R:

setwd('&;/Dropbox/Meadow_to_qiimedb/Dust/sequences/2') # copy so old version is saved system("cp Kembel_etal_Dust2_INDEX.fastq index.fastq") # read both in as vector bcin <- read.table('old.txt', head=FALSE) bcout <- read.table('new.txt', head=FALSE) # format to vector bci <- as.character(bcin$V1) bco <- as.character(bcout$V1) n <- length(bci) # loop through with system sed command for(i in 1:n) { system(paste("sed s/", bci[i], "/", bco[i], "/ index.fastq > index2.fastq", sep='')) system("cp index2.fastq index.fastq") print(i) } # output index.fastq has all new barcodes

Of course R is not exactly the elegant way to do this, but it did work in the moment and was really quick to code. But this made me realize that I should really be more familiar with python - that is certainly a better tool for this sort of job. So I spent this weekend brushing up on python and came up with this script to do the same job:

#!/bin/python # # script to replace dna barcodes in a big file: # this was written and tested on a small test file, but # also worked on sequence files. # the test requires 3 files in the same directory # "roses.txt" <sequence file> that contains these 3 lines: # # roses are red, # violets are blue # roses are also white # # and "old.txt" <old barcodes> with these 3 lines: # # roses # violets # white # # and "new.txt" <new barcodes> with these 3 lines # # socks # shoes # stinky # # Caution! # since this was written for an index read # (following Caporaso 2012 methods) # it does not require that barcodes be at the beginning!! # import sys def Rep(filename): # new function to hold it all f = open(filename, 'rU') # open filename that is passed and read only seq = f.read() # read it and hold all in memory as one string o = open('old.txt', 'rU') # open old words n = open('new.txt', 'rU') # open new replace words out = open('rosesnew.txt', 'w') # create new file that will get filled old = o.read() # read in old words new = n.read() # read in new words old = old.split('\n') # split them both at line break new = new.split('\n') # otherwise appears in list for i in range(3): # loop through using numbers seq = seq.replace(old[i], new[i]) # so that 'i' can index 2 things out.write(seq) # write new output to file created above def main(): # boilerplate Rep(sys.argv[1]) if __name__ == '__main__': main()

Discussed VOCs and health with Josh and Gwynne - we're trying to figure out exactly what data to collect in that context:

lots known about bad effects of VOCs but very little about benefits

well known what VOCs you might experience based on land cover and proximity to given sources (like plants vs industrial vs roads etc)

assumed that most come from plants - assumed that little comes from plant microbes and some comes from soils, with some individual VOCs known and well tracked

But likely little known regarding small scale spatial dynamics in urban environment with regards to beneficials.

Some research on benefits - mostly forest bathing and essential oils research.

So we are considering collecting VOCs from urban gradient (but determining the appropriate scale) for predictive model since we have lots of good info about health effects for bad VOCs and some on beneficials.

That is happening in RStudio with the following general steps:

I spent most of the day finishing up our big submission to the QIIME database (lillis dust, air, and surfaces). All metadata are now in order for all 3 projects, and they are working on entering data so that sequences can be input before our Berkeley Indoor Evolution meta-analysis meeting.

Additionally I worked for part of the day with Adam on the lab notebook website, and we think it is about ready to launch after we test it for the next couple of days.