DIA Transfer Learning for Dimethyl-Labeled Samples

This tutorial was created using AlphaDIA 1.10.1 - please be aware that there might be changes in your version

This guide demonstrates how to use AlphaDIA’s transfer learning capabilities for analyzing samples which require learning the retention time and fragmentation behaviour. We will use replicates of dimethyl-labeled samples with a three step workflow that is fully supported in AlphaDIA 1.10.0 onwards via both GUI and CLI.

The integrated workflow eliminates the need for multiple separate searches and consists of:

1. Transfer Learning step: Generates a custom PeptDeep model fine-tuned to your specific samples, predicting retention times, fragmentation patterns, and charge state of your dimethyl-labeled peptides.

2. First search step: Builds a spectral library using the fine-tuned model containing peptide information with accurate mass, retention time, and fragmentation predictions.

3. Second search step: Uses the library created in the previous step for cross-run quantification (Match Between Runs), providing the most accurate and comprehensive results.

The entire process is fully automated, with data seamlessly transferred between steps and optimal parameters applied at each phase. You configure the search workflow in the settings tab, and AlphaDIA automatically handles information passing between search steps and activates required settings for transfer learning and MBR.

Prerequisites

This guide requires AlphaDIA 1.10.0 or higher.

Before starting, ensure you have:

• A machine with at least 64 gigabytes of memory

• Test data available for download here (replicates of dimethyl-labeled tryptic HeLa digests with light isotope)

• Valid AlphaDIA installation with GUI (one-click installer recommended)

• The BundledExecutionEngine selected as your execution engine

Make sure you have a project folder set up with the raw data, fasta file and an output folder.

Setting Up Your Project

1. Configure Input/Output

Launch AlphaDIA and configure your inputs. Set your output folder to a location of your choice where results will be stored. Select all raw files and add them to the file list. You’ll also need to add the FASTA file which will be used for library prediction. Set your output folder to a location of your choice where results will be stored.

2. Configure the Multistep Search

1. Method Settings

In the GUI, navigate to settings and activate “Transfer Learning step” and “Match Between Runs” second step. These options enable the complete multistep workflow.

All settings which we will configure will by default affect all three search steps. On top of these settings, AlphaDIA will make smart choices in the transfer learning and MBR search to make sure the right modules are switched on.

Set the general.thread_count parameter to match the number of logical cores available on your system. This will optimize performance by utilizing your available computing resources.

Enable library prediction from FASTA withg library_prediction.enabled, which is essential for the workflow. This allows AlphaDIA to generate a theoretical spectral library from your protein database.

The library_prediction.precursor_mz range should be set to match your dataset characteristics. For the example data, the range 380-980 is appropriate as it covers the mass range of the peptides in the sample. Setting this correctly helps focus the search on relevant precursors.

For dimethyl-labeled samples, you must specify the following fixed modifications by setting library_prediction.fixed_modifications to Dimethyl@Any_N-term and Dimethyl@K. These account for the chemical modifications added during sample preparation.

Add appropriate variable modifications to library_prediction.variable_modifications which may be present in your samples. For typical samples, Oxidation@M and Acetyl@Protein_N-term are common. Variable modifications account for potential post-translational or chemical modifications that may or may not be present on certain residues.

The tolerance settings are critical for search accuracy. Set search.target_ms1_tolerance to 4 ppm and search.target_ms2_tolerance to 7 ppm if you’re using Orbitrap Astral or similar high-resolution instruments. For lower-resolution instruments, values of 10-15 ppm might be more appropriate. These values define how closely measured masses must match theoretical masses.

2. Transfer Learning Settings

Warning

As the settings apply to all steps, do not activate the transfer library and transfer learning module for all steps. AlphaDIA will activate this automatically for the search step if this step is part of the multistep workflow.

If you want to have more control over the transfer learning step, we can use the advanced parameters in the “Transfer Learning” and “Transfer library” section. Have a look here for a more detailed description.

Transfer Library For library creation we can configure which fragments, not yet annotated in the library, should be quantified in the training data. We can set the fragment types, including labile modification losses or neutral losses. We can also define filtering criteria to limit the library to high quality precursors.

Transfer Learning For transfer learning we can set the hyperparameters for training as well as the annotation of the model. Usually no changes are needed as an automated learning rate schedule for optimal performance without overfitting is used.

3. Start the Workflow

With your settings configured, click the “Run Workflow” button to start the integrated multistep search process. The workflow executes in three automated phases.

Results

1. Output Folder

After the workflow completes, your results will be organized as follows:

The final analysis results will be located in the root of your project folder. These are the primary files you’ll use for your research.

Intermediate results from earlier steps are stored in subfolders named transfer and library, allowing you to examine the results of individual stages if needed.

For downstream analysis and interpretation, you’ll primarily work with the results stored in the root of the output folder:

The precursor-level file precursors.tsv, which contains detailed information about all identified peptide precursors, including retention times, intensities, and confidence metrics.

The protein matrix file pg.matrix.tsv, which summarizes protein-level quantification across all your samples, making it ideal for comparative analyses.

If you wish to use the custom PeptDeep models developed during your analysis for future projects, you can find them in transfer/peptdeep.transfer. These models can be valuable for analyzing similar samples in the future.

2. Search Performance

The stat.tsv file provides a quick overview of the number of precursors and protein groups identified across your samples, along with other relevant performance metrics. This is useful for quickly assessing the success of your analysis.

Run	Precursors (Transfer)	Precursors (First Search)	Precursors (Final)
20240408_OA1_Evo12_31min_TiHe_SA_H032_E32_F-40_B3	39,425	57,757	68,195
20240408_OA1_Evo12_31min_TiHe_SA_H032_E32_F-40_B2	39,062	58,165	68,192
20240408_OA1_Evo12_31min_TiHe_SA_H032_E32_F-40_B1	34,720	60,075	69,072

These numbers demonstrate how each step of the workflow contributes to increasing the number of identified precursors, with the final result identifying significantly more peptides than the initial transfer learning step alone.

The individual performance metrics of the peptdeep model are stored in transfer/stats.transfer.tsv. The Evaluation guide here explains how to analyze and visualize the learned peptdeep model.