4. Export data in .csv or tab-delimited .txt format containing at
least one column annotated “Sequence,” “Peptide,” or “pep_seq” with the de novo identified peptides in all caps and a
second column annotated “Scan” including sample and scan
number separated by a colon (e.g., S01:1, sample 1 MS/MS
scan 1). The default export settings in PEAKS will not require
any further modifications.
3.7 Species
Annotation of De Novo
Peptide Identifications
1. Download the most recent version of ProteoClade from
GitHub and uncompress files to the desired location. This
will become the working directory, and it is recommended to
have 500 GB of free disk space available for data generation (see
Subheading 2.6) [15].
2. Create a new folder containing the de novo search results inside
the ProteoClade working folder.
3. Check if Python 3 is installed by executing python (use py in
Windows 10) on the command line. If executed, confirm version and update if required; if not executed, install Python.
4. Navigate to the ProteoClade directory on the command line,
start Python, and load ProteoClade with the following command:
from proteoclade import *
Download all currently available sequence entries in the
UniProt database using the following command:
download_uniprot((“all”,"a”))
Create an in silico protein digest of all the retrieved
sequences (see Note 16):
create_pcdb(“all”)
The default parameters are set for trypsin, and consult
ProteoClades user guide for additional options and setting
details. Database generation time will depend on the available
computer hardware (see Subheading 2.6).
5. Download NCBI’s taxonomy reference for mapping purposes:
download_taxonomy()
The PCTAXA file name is the download day in yearmonth-day format.
6. To annotate de novo spectra, run the following command:
annotate_denovo("denovo_results.csv", "all.pcdb", "date.
pctaxa",
taxon_levels
¼
(’species’,’genus’,’family’,
’order’,’class’,’phylum’,’kingdom’,’superkingdom’))
7. Roll up the annotated results from peptide to gene level using
the following command:
roll_up("annotated_denovo_matched_denovo_results.
csv", unique_taxon ¼ (’phylum’))
(for example of results, see Fig. 2b and c).
Metaproteomics Analysis of Host–Microbiota Interfaces
175
least one column annotated “Sequence,” “Peptide,” or “pep_seq” with the de novo identified peptides in all caps and a
second column annotated “Scan” including sample and scan
number separated by a colon (e.g., S01:1, sample 1 MS/MS
scan 1). The default export settings in PEAKS will not require
any further modifications.
3.7 Species
Annotation of De Novo
Peptide Identifications
1. Download the most recent version of ProteoClade from
GitHub and uncompress files to the desired location. This
will become the working directory, and it is recommended to
have 500 GB of free disk space available for data generation (see
Subheading 2.6) [15].
2. Create a new folder containing the de novo search results inside
the ProteoClade working folder.
3. Check if Python 3 is installed by executing python (use py in
Windows 10) on the command line. If executed, confirm version and update if required; if not executed, install Python.
4. Navigate to the ProteoClade directory on the command line,
start Python, and load ProteoClade with the following command:
from proteoclade import *
Download all currently available sequence entries in the
UniProt database using the following command:
download_uniprot((“all”,"a”))
Create an in silico protein digest of all the retrieved
sequences (see Note 16):
create_pcdb(“all”)
The default parameters are set for trypsin, and consult
ProteoClades user guide for additional options and setting
details. Database generation time will depend on the available
computer hardware (see Subheading 2.6).
5. Download NCBI’s taxonomy reference for mapping purposes:
download_taxonomy()
The PCTAXA file name is the download day in yearmonth-day format.
6. To annotate de novo spectra, run the following command:
annotate_denovo("denovo_results.csv", "all.pcdb", "date.
pctaxa",
taxon_levels
¼
(’species’,’genus’,’family’,
’order’,’class’,’phylum’,’kingdom’,’superkingdom’))
7. Roll up the annotated results from peptide to gene level using
the following command:
roll_up("annotated_denovo_matched_denovo_results.
csv", unique_taxon ¼ (’phylum’))
(for example of results, see Fig. 2b and c).
Metaproteomics Analysis of Host–Microbiota Interfaces
175