Chapter 3 Exporting data

It is useful to be able to export our ASV sequences and a matrix table of counts from DADA2/R as we might want to visualise or analyse these using other software.
Our ASV sequences and counts per sample are stored in the object seqtab.nochim
. The ASVs are not named, so first let's name them (ASV_1, ASV_2, etc.).
# The column names of seqtab.nochim are actually the ASV sequences,
# so extract these and assign them to `mifish_seqs`
mifish_seqs <- colnames(seqtab.nochim)
# Make a new variable for ASV names, `mifish_headers`,
#with length equal to the number of ASVs
mifish_headers <- vector(dim(seqtab.nochim)[2], mode="character")
# Fill the vector with names formatted for a fasta header (>ASV_1, >ASV_2, etc.)
for (i in 1:dim(seqtab.nochim)[2]) {
mifish_headers[i] <- paste(">ASV", i, sep="_")
}
3.1 Fasta file
Now we have our sequences and names as variables we can join them and make a fasta file.
You should now have this fasta file in your working directory on the server.
3.2 Sequence count matrix
Next make a table of sequence counts for each sample and ASV.
# First transpose the `seqtab.nochim` and assign this to the variable `mifish_tab`
mifish_tab <- t(seqtab.nochim)
# Name each row with the ASV name, omitting the '>' used in the fasta file
row.names(mifish_tab) <- sub(">", "", mifish_headers)
write.table(mifish_tab, "MiFish_ASV_counts.tsv", sep="\t", quote=F, col.names=NA)
You should now have an ASV by sample matrix with sequence counts in a tab-separated value (tsv) file in your working directory.
3.3 Table of taxon names
Lastly, if we've used dada2 to assign taxonomy we can make a table of taxon names for each ASV.
# Replace the row names in `taxa` with the ASV names,
# omitting the '>' used for the fasta file.
rownames(taxa) <- gsub(pattern=">", replacement="", x=mifish_headers)
write.table(taxa, "MiFish_ASV_taxonomy.tsv", sep = "\t", quote=F, col.names=NA)
You should now have a tsv file of taxonomic assignments for each ASV in your working directory.