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classify_sequences(), which has
confidence score filtering built-in as an option and is multithreaded by
default. Can be run by giving the function the name of a data frame and
the columns corresponding to the sequence id and sequences or as a
{strollur} object and function will be output results as
appropriateclassify_sequences(), filter_taxonomy(),
print_taxonomy()build_kmer_database() now includes
reference_data argumentfilter_taxonomy() to take a data frame rather
than a listprint_taxonomy() to take a data frame rather
than a list and then output mothur-like taxonomy string to user
specified depth. If specified depth is shorter than observed, string
gets truncated. If depth is longer than specified it adds
“_unclassified”. User can turn off confidence scoresrdp_trainset9_pds and rdp_trainset9 now
include reference data as a data frame in the reference_data slot; will
be incorporated in databases provided by
{phylotyprrefdata}. These were renamed from trainset9_rdp
and trainset9_pdsid to sequence_name across
functionsset.seed() to be within call to
classify_sequences()build_kmer_database() and
classify_sequences() for performance making both
significantly fasterclassify_sequence()This is a patch release to… * update the citation * change the license to GPL v3 * make a small change to the behavior of the code for when a k-mer shows up more than once in a sequence
This is the first release version of phylotypr! 🎉
read_fasta()read_taxonomy()build_kmer_database()classify_sequence()filter_taxonomy()print_taxonomy()trainset9_rdptrainset9_pdsThese binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.