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| Good Practices | |
| 1) It uniquely identifies the source of the sample (the location, the biological individual), or at least can be traced back to this information. | |
| 2) It is clear and cannot be confused with any other sample in the same project, lab, department, university, universe. | |
| 3) It is impossible to mis-write or mis-read. | |
| 4) It is short. | |
| -Study (2-3 letter) or code as a study number | |
| CF | |
| CAR | |
| IH |
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| library(ape) | |
| library(vegan) | |
| library(adegenet) #Might as well load if you are doing phyo stuff | |
| setwd("/Users/../floder") | |
| Alignment <- read.dna("mfa.fasta",format = "fasta") #Importing alignment in fasta format | |
| fasta.seq.labels <- as.data.frame(labels(Alignment)) #Obtaining ordered taxa | |
| colnames(fasta.seq.labels) <- "taxa" | |
| clades <- as.data.frame(read_delim("~/clade_assignments_for_tax.txt")) #importing clade assigns for each isolate (in the future this could be automated using PCA) |
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| ##GENERAL TEXT OR FILE MANIPULATION | |
| #Find lines in a list (e.g. file names) that are not present in another list | |
| #This is good for checking whether downstream files are present (i.e. pipeline ran susscessfully) | |
| comm -23 <(sort All.txt) <(sort Finished.txt) | |
| #Looping over anything | |
| for f in $(cat names.txt); do whatever to ${f}; done | |
| #renaming file extensions using bash code |