Skip to main content

Converting PAMAF .mbi data to pseudo-MS/MS files by untargeted methods.

Project description

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.

  1. 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
    
  2. Install xTracer

    pip install xtracer-pamaf
    
  3. 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
  -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: 2.5
  -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: 0.3
  -tol_point_num TOL_POINT_NUM   Specify the point num tolerance that a signal should have. Default: 7
  -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


Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

xtracer_pamaf-1.2.1.tar.gz (12.8 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

xtracer_pamaf-1.2.1-py3-none-any.whl (15.5 kB view details)

Uploaded Python 3

File details

Details for the file xtracer_pamaf-1.2.1.tar.gz.

File metadata

  • Download URL: xtracer_pamaf-1.2.1.tar.gz
  • Upload date:
  • Size: 12.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.9.25

File hashes

Hashes for xtracer_pamaf-1.2.1.tar.gz
Algorithm Hash digest
SHA256 0310deac1acca0082bfa78dcff4c4916f596f7b6cffd34782d7b5e6c7c1be0de
MD5 179feb59b9b22c6ca5fb68dac3efaa24
BLAKE2b-256 bac12ad86550783b4db8e95e566e7d94adc0ff0967f90275c516e43e06147403

See more details on using hashes here.

File details

Details for the file xtracer_pamaf-1.2.1-py3-none-any.whl.

File metadata

  • Download URL: xtracer_pamaf-1.2.1-py3-none-any.whl
  • Upload date:
  • Size: 15.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.9.25

File hashes

Hashes for xtracer_pamaf-1.2.1-py3-none-any.whl
Algorithm Hash digest
SHA256 cbfe247f30eec8931ec5166fcb56d27d017caf75f72804ccbf148853b8b1a5c9
MD5 d97784da7e6798d20db160dc551d5aed
BLAKE2b-256 02c8e8aae6086b29a67f9be47ad2778de25571555a171c59a52cab5aed438860

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page