ganon2 classifies genomic sequences against large sets of references efficiently, with integrated download and update of databases (refseq/genbank), taxonomic profiling (ncbi/gtdb), binning and hierarchical classification, customized reporting and more
--input-target sequence
support with --filter-type hibf
--input-file
parsing with more documentation #282--filter-type ibf
/hibf
on python sideThanks @jfy133 for reporting!
Bug fixes:
Thanks @ksavhughes and @marade for reporting
ganon2 is here (v2.0.0)
ganon2 comprises all features implemented incrementally over the years since version 1.0.0. See the releases page for a full history of changes. ganon2 is faster and uses less memory to index and to classify sequences mainly due to the use of the HIBF from raptor and the use of the amazing SeqAn3 library. ganon2 achieves good results due many factors implemented over time, like the EM-algorithm, data-based optimization of parameters, among many others.
Changes from v1.9.0:
ganon classify --multiple-matches em/lca
to choose the algorithm to solve reads with multiple-matches. --reassign
option was removed, replaced by --multiple-matches em
.one
(before .lca
in ganon classify
and .all
in ganon reassign
)ganon classify
can now better control the integrated report generated at the end with --report-type --ranks --min-count
and --skip-report
ganon build
and build-custom
now generate an HIBF by default with --level species
by default on ganon build
. The boolean --hibf
was removed and --filter-type ibf/hibf
introduced.#260
#258
ganon classify
with --fpr-query
--representative-genomes
in ganon build
--binning
in ganon classify
--skip-genome-size
in ganon build build-custom report
to not generate genome sizes in the dbganon classify
better support for long readsganon build --input-recursive
to find files recursivelyganon build --min-length
to skip small sequencesganon build --input-target sequence
performance improvedganon classify --reassign
or ganon reassign
)--max-fp
or --filter-size
--mode
on ganon build
and ganon build-custom
to better control filter trade-off of speed and size
--mode avg
is used and will balance speed (number of bins created) and size of the final filter--mode faster
creates a slightly larger filter with faster classification speeds--mode fastest
creates a larger filter with even faster classification speeds--mode smaller
creates a slightly smaller filter with slower classification speeds--mode smallest
creates the smallest as possible filter with higher impact on classification speeds--filter-size
is used instead of --max-fp
, smaller/smallest
control to the false positive rate instead of the size