Using the Python API

This tutorial shows how to use the MS²Rescore Python API for each step of the rescoring process individually. This is useful if you want to customize rescoring for your own Python workflow or if you want to understand how MS²Rescore works.

Note that the full MS²Rescore workflow is also available from Python with the single function call ms2rescore.rescore().

[1]:

import logging
import plotly.io

logging.basicConfig(level=logging.INFO)
plotly.io.renderers.default = "plotly_mimetype+notebook"

Reading and parsing peptide-spectrum matches

[2]:

from psm_utils.io import read_file

from ms2rescore.report.charts import score_histogram

INFO:numexpr.utils:Note: NumExpr detected 32 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 8.
INFO:numexpr.utils:NumExpr defaulting to 8 threads.

Reading the PSM file

MS²Rescore is fully centered around the use of a psm_utils PSMList. This is a unified data representation of PSMs and their various attributes. Internally, it is simply a list of Pydantic data classes which represent PSMs. With the submodule psm_utils.io, we can read PSMs from a variety of file formats. Here, we will read a PSM file in the MaxQuant msms.txt format.

Importantly, for rescoring, the PSM file must contain all target and decoy PSMs, including PSMs that did not pass the FDR threshold. Most search engines must be specifically configured to return all PSMs without FDR filtering.

[3]:

psm_list = read_file("../../../examples/id/msms.txt", filetype="msms")
psm_list["spectrum_id"] = [str(spec_id) for spec_id in psm_list["spectrum_id"]]

For a quick inspection, we can format the PSM list as a Pandas dataframe and display the first few rows:

[4]:

psm_list.to_dataframe().head()

[4]:

	peptidoform	spectrum_id	run	collection	spectrum	is_decoy	score	qvalue	pep	precursor_mz	retention_time	ion_mobility	protein_list	rank	source	provenance_data	metadata	rescoring_features
0	AAAAAAALQAK/2	4703	20161213_NGHF_DBJ_SA_Exp3A_HeLa_1ug_7min_15000_02	None	None	False	107.660	None	0.001517	478.77982	5.2007	None	[P36578, H3BM89, H3BU31]	None	msms	{'msms_filename': '..\..\..\examples\id\msms.t...	{'Scan index': '3698', 'Sequence': 'AAAAAAALQA...	{}
1	[ac]-AAAAAEQQQFYLLLGNLLSPDNVVR/3	13572	20161213_NGHF_DBJ_SA_Exp3A_HeLa_1ug_7min_15000_02	None	None	False	107.740	None	0.004931	915.15197	11.8470	None	[O00410, E7ETV3, E7EQT5, C9JZD8]	None	msms	{'msms_filename': '..\..\..\examples\id\msms.t...	{'Scan index': '11885', 'Sequence': 'AAAAAEQQQ...	{}
2	[ac]-AAAAAEQQQFYLLLGNLLSPDNVVRK/3	13366	20161213_NGHF_DBJ_SA_Exp3A_HeLa_1ug_7min_15000_02	None	None	False	137.890	None	0.000493	957.85029	11.6900	None	[O00410, E7ETV3, E7EQT5, C9JZD8]	None	msms	{'msms_filename': '..\..\..\examples\id\msms.t...	{'Scan index': '11695', 'Sequence': 'AAAAAEQQQ...	{}
3	AAAAAQGGGGGEPR/2	505	20161213_NGHF_DBJ_SA_Exp3A_HeLa_1ug_7min_15000_02	None	None	False	22.641	None	0.142020	585.28653	0.5178	None	[E9PJF0, E9PQW4, P27361]	None	msms	{'msms_filename': '..\..\..\examples\id\msms.t...	{'Scan index': '419', 'Sequence': 'AAAAAQGGGGG...	{}
4	AAAAAWEEPSSGN[de]GTAR/2	6589	20161213_NGHF_DBJ_SA_Exp3A_HeLa_1ug_7min_15000_02	None	None	False	89.403	None	0.046504	823.87389	6.6105	None	[Q9P258]	None	msms	{'msms_filename': '..\..\..\examples\id\msms.t...	{'Scan index': '5439', 'Sequence': 'AAAAAWEEPS...	{}

We can also directly plot the current PSM score distributions:

[5]:

score_histogram(psm_list)