AMP density tutorial

In this tutorial, we will show how to use the expected.percontigs output produced in the contigs and reads mode to obtain the density of AMPs per species.

Consider the expected results in the folder tests/contigs/expected.percontigs as one of the inputs used in this tutorial, and the table for taxonomy of contigs available in the example_seqs/example_taxonomy_contigs.tsv.gz.

First you will need to load tables into your system:

import pandas as pd

percontigs = pd.read_table('tests/contigs/expected.percontigs', comment='#')
taxonomy = pd.read_table('example_seqs/example_taxonomy_contigs.tsv.gz')

Then, you will need to merge these tables.

percontigs = percontigs.merge(on='contig',
                              right=taxonomy,
                              how='outer')

Now, we will group results and sum values:

percontigs = percontigs.dropna()
percontigs = percontigs.drop('contig', axis=1)
percontigs = percontigs.groupby('taxonomy').agg('sum')

By now, you should have a table with the species and the total of assembled base pairs per species as well as their total number of ORFs, smORFs and AMPs. Now you just calculate the density as follows:

percontigs['AMP_density'] = percontigs.AMPs * 1e6 / percontigs.length
percontigs.to_csv('expected.density',
                  sep='\t',
                  header=True,
                  index=True)

Note that taxonomy is the index of the grouped table, so we write with index=True to keep it in the output.

The resulting expected.density table should be as follows:

taxonomy	length	ORFs	smORFs	AMPs	AMP_density
speciesA	4208	11	10	1	237.643
speciesB	11876	15	8	2	168.407
speciesC	5679	8	6	0	0.000

This per-species density is different from the sample-wide density that contigs and reads modes already report on their own (the # Macrel calculated for the sample a density of … AMPs / Mbp. comment line at the top of the *.percontigs.gz output, also printed to the console). Here we break that figure down by species instead of computing a single value for the whole sample.