beta-dia 0.1.0

A cool project that does something awesome on diaPASEF data.

Beta-DIA

Beta-DIA is a partially open-source, free-to-use Python software that provides comprehensive peptide/protein identification and accurate quantification results for single-shot diaPASEF data.

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Contents

Installation
Usage
Output

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Installation

We recommend using Conda to create a Python environment for using Beta-DIA, whether on Windows or Linux.

1. Create a Python environment with version 3.9.18.

   conda create -n beta_env python=3.9.18, numpy<2.0.0
   conda activate beta_env
   

2. Install the corresponding PyTorch and CuPy packages based on your CUDA version (which can be checked using the nvidia-smi command). Beta-DIA will fail on computers without a GPU.

• CUDA-12

  pip install torch==2.3.1 --index-url https://download.pytorch.org/whl/cu121
  pip install cupy-cuda12x
  conda install cudatoolkit
  

• CUDA-11

  pip install torch==2.3.1 --index-url https://download.pytorch.org/whl/cu118
  pip install cupy-cuda11x
  conda install cudatoolkit
  

3. Install Beta-DIA

   pip install beta_dia
   

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Usage

beta_dia -lib "Absolute path of the spectral library" -ws "Absolute path of the .d folder or a folder containing multiple .d folders"

(Please note that the path needs to be enclosed in quotes.)

• -lib
  This parameter is used to specify the absolute path of the spectral library. Beta-DIA currently supports the spectral library with the suffix .speclib predicted by DIA-NN (>=v1.9). It supports oxygen modifications on methionine (M) but does not include modifications such as phosphorylation or acetylation. Refer to this for instructions on how to generate prediction spectral libraries using DIA-NN. Beta-DIA will develop its own predictor capable of forecasting the peptide retention time, ion mobility, and fragmentation pattern. It may also be compatible with other formats of spectral libraries based on requests.

• -ws
  This parameter is used to specify the .d folder or the folder containing multiple .d folders that need to be analyzed.

Output

Beta-DIA will generate beta_dia/report_beta.log.txt and beta_dia/report_beta.tsv in each .d folder. The report_beta.tsv contains precursor and protein IDs, as well as plenty of associated information. Most column names are consistent with DIA-NN and are self-explanatory.

• Protein.Group - inferred proteins. Beta-DIA uses IDPicker algorithm to infer proteins.
• Protein.Ids - all proteins matched to the precursor in the library.
• Protein.Names names (UniProt names) of the proteins in the Protein.Group.
• PG.Quantity quantity of the Protein.Group.
• Precursor.Id peptide seq + precursor charge.
• Precursor.Charge the charge of precursor.
• Q.Value run-specific precursor q-value.
• Protein.Q.Value run-specific q-value for the unique protein, that is protein identified with proteotypic (=specific to it) peptides.
• PG.Q.Value run-specific q-value for the protein group.
• Proteotypic indicates the peptide is specific to a protein.
• Precursor.Quantity quantity of the precursor.
• RT the retention time of the precursor.
• IM the ion mobility of the precursor.
• CScore the final precursor score calculated by Beta-DIA after merging all sub-scores.
• CScore.PG the final protein group score calculated by Beta-DIA after merging multiple peptide scores.

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Troubleshooting

• Please create a GitHub issue and I will respond as soon as possible.
• Email to me: [e-mail]

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Citing Beta-DIA

Check out: Beta-DIA: Beta-DIA: Integrating learning-based and function-based feature scores to optimize the proteome profiling of diaPASEF mass spectrometry data

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Changelog

0.1.0

• FEAT: first commit on GitHub.