xTracer
Parallel Accumulation with Mobility Aligned Fragmentation (PAMAF) achieves near-complete ion utilization and high spectral specificity by fragmenting all mobility-separated precursors without quadrupole isolation. Leveraging the ultrahigh mobility resolution of SLIM, this quadrupole-free strategy maximizes ion sampling efficiency and offers a promising approach in mass spectrometry–based proteomics, particularly for low-abundance peptides or low-input samples. However, the unique data structure of PAMAF—where precursor–fragment relationships are encoded along the mobility dimension—renders it incompatible with existing peptide identification tools. Here, we present xTracer, the first untargeted peptide identification algorithm developed specifically for PAMAF data. xTracer integrates correlations across both chromatographic and mobility dimensions to associate precursor and fragment ions, reconstruct pseudo-spectra, and enable database searching using well-established DDA search engines. Applied to datasets with varying sample loads and acquisition throughputs, xTracer consistently achieved robust and reproducible peptide identifications, outperforming single-domain correlation strategies. Overall, xTracer provides a versatile and high-efficiency computational framework for reconstructing pseudo-spectra from quadrupole-free, mobility-aligned fragmentation data, enhancing the analytical power of high-resolution ion mobility–based proteomics.
Contents
Datasets
Installation
Usage
Output
Datasets
Varying sample load dataset and varying throughput dataset by PAMAF acquisition can be downloaded from MSV000099577
Installation
We recommend using Conda to create a Python environment for using xTracer on Windows.
-
Create a Python environment with version 3.12.11 to consistent with the SDK environment.
conda create -n xtracer_env python=3.12.11 conda activate xtracer_env
-
Install xTracer
pip install xtracer-pamaf
-
SDK access
Please send an SDK request email to Mobilion Inc., and then copy the file _mbisdk.pyd, MBI_SDK.dll and mbisdk.py into the sdk folder under the xTracer installation directory.
Usage
xtracer -ws_in "the folder that contains .mbi files" -xic -xim
All params are list below by entering xtracer -h:
optional arguments for users:
-h, --help Show this help message and exit.
-ws_in WS_IN Specify the folder that contains .mbi files.
-out_name OUT_NAME Specify the folder name that contains .mgf files. Default: mgf_xtracer
-xic Using XIC-based method to calculate PCC
-xim Using XIM-based method to calculate PCC
-write_pcc Specify whether to write PCC values to MS/MS files. Default: False
-pr_mz_min PR_MZ_MIN Specify the minimum m/z value of precursors. Default: 200
-charge_min CHARGE_MIN Specify the minimum charge of precursors. Default: 1
-charge_max CHARGE_MAX Specify the maximum charge of precursors. Default: 4
-at_min AT_MIN Specify the minimum arrival time (at) value of signals. Default: 90 ms
-tol_at_area TOL_AT_AREA Specify the millisecond tolerance of signal in at dimension. Default: 2.0
-tol_at_shift TOL_AT_SHIFT Specify the millisecond tolerance when considering signal related. Default: 1
-tol_ppm TOL_PPM Specify the ppm tolerance of signal in m/z dimension. Default: 30
-tol_iso_num TOL_ISO_NUM Specify how many isotopes should have to be a precursor. Default: 2, i.e. M, M+1H, M+2H
-tol_pcc TOL_PCC Specify the PCC tolerance when two signal are related. Default: 0.4
-tol_point_num TOL_POINT_NUM Specify the point num tolerance that a signal should have. Default: 5
-tol_fg_num TOL_FG_NUM Specify the fragment ions num tolerance that a spectrum should have. Default: 10
-xim_across_cycle_num Specify the odd XIM cycle span when summing frames. Default: 3
-xic_across_cycle_num Specify the odd XIC cycle span when extracting XIC. Default: 7
Output
For each .mbi file, xTracer produces a corresponding .mgf DDA-like file that can be analyzed by DDA engines for identification.
Troubleshooting
- Please create a GitHub issue and we will respond as soon as possible.
- Email: jian.song.2025@outlook.com
Release files for xtracer-pamaf 1.3.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| xtracer_pamaf-1.3.0.tar.gz | 12.9 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| xtracer_pamaf-1.3.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 28.4 kB
Release files / xtracer_pamaf-1.3.0.tar.gz
| Download URL | xtracer_pamaf-1.3.0.tar.gz |
|---|---|
| Size | 12.9 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
67cb4906648357c45c94cf840739c89cf35d8cb8c0f05a534e98fb30887b1a92
|
|
BLAKE2b-256 checksum How to use checksums |
4af258660144fd6821edddd062e783cb73d60e1c3239e1adf9c0e33c945f929f
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.9.25
|
Release files / xtracer_pamaf-1.3.0-py3-none-any.whl
| Download URL | xtracer_pamaf-1.3.0-py3-none-any.whl |
|---|---|
| Size | 15.5 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
3689e59518dde650441b69a139109c4814221a35b42f71ed0cff48e493bd9c4a
|
|
BLAKE2b-256 checksum How to use checksums |
4757237a82d6fadc6489b93d8e16751d420108ef5b830201b3802a91c20b4d91
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.9.25
|