|Alternative name||SUbsystems Profile by databasE Reduction using FOCUS|
|Interface||Command line interface|
|Restrictions to use||None|
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- person_outline Genivaldo Gueiros Z. Silva <>
Publication for SUbsystems Profile by databasE Reduction using FOCUS
SUPER-FOCUS IN pipelines(3)
[…] with greater sequencing depth. notably, panphlan detected the dominant strains in each kefir sample above 500,000 reads per sample. again, the outputs from functional profiling analysis using super-focus were generally accordant between the platforms at different sequencing depths. finally, and expectedly, metagenome assembly completeness was significantly lower on the miseq than either […]
[…] metagenomics is its ability to characterise the functional potential of metagenomes. again, the results of functional analysis were generally consistent between all three sequencing platforms, but super-focus did detect significant differences in three functions which were present at greater than 1% relative abundance within the kefir metagenome. such discrepancies suggest that results […]
[…] sequencers cannot be reliably compared., above, we described a considerable difference in the compositional profiles determined by different species classifiers. hence, we also compared results from super-focus with those from humann2, which is an alternative tool for functional analysis of metagenomes. we observed a similarly pronounced disparity in the results generated by these methods. […]
Therefore you need the stamp output format, it is created when you run all your samples in the same command. Only thing I disliked is the ".xls" output format which is not xls but csv, had to adjust the R scripts, also the used color palette since I had more samples than colors defined in the superfocus_functions.R script. Maybe this can be improved.
You can screen the stamp output csv for specific Subsystems present in the metagenome using a spreadsheet program or command line (my output files were too big for spreadsheet programs), enabling you to find the relevant biological features of your microbial community.