DIA-NN

[wty373]

Select Topic Area

Question

GitHub Feature Area

Actions

Body

my DIA-NN (version 2.0 Academia) has been stuck at the step "Predicting spectra and IMs" for a long time (over 28 minutes so far). The parameters I used are as follows:
Input data: 2 mzML files from Astral mass spectrometry
FASTA file: homo2wp.fasta.fasta
Protease: Trypsin/P, missed cleavages = 2
Fixed modification: Carbamidomethyl (C)
Precursor m/z range: 300–1800, charge range: 1–4
Threads: 4
I have enabled "Generate spectral library" and "Deep learning-based spectra, RTs and IMs prediction"
I am wondering if this long runtime is normal, or if there are any potential issues with my parameter settings or input data that could be causing the stall. Could you please share your insights on how to troubleshoot this?

[coderwrexx]

Hello! 28 minutes at the "Predicting spectra and IMS" step is actually quite common given your specific parameter configuration. It is highly likely that DIA-NN is not stalled, but rather processing a massive computational workload.

Here is a breakdown of why this is happening and how you can troubleshoot or speed it up:

1. The Bottleneck: Thread Count vs. Search Space
   You are running deep learning-based predictions for a full human proteome (homo2wp.fasta.fasta) and allowing for 2 missed cleavages. This generates an exponentially large pool of theoretical peptides.

Predicting the spectra, RTs, and IMS for all of these theoretical peptides using only 4 threads is the primary reason for the delay. Deep learning prediction is highly computationally intensive. On a standard 4-thread allocation for a human FASTA, this step can easily take over an hour (or more depending on the specific CPU architecture).

2. RAM Limitations (Swapping)
   Because the search space is so large (due to the missed cleavages and charge ranges), DIA-NN requires a significant amount of RAM to hold the theoretical library in memory during prediction. If your system runs out of physical RAM, your operating system will start "swapping" memory to your storage drive. This will cause the processing speed to drop to a near halt, making it look completely frozen.

Recommended Actions to Troubleshoot:
Check your Resource Manager: Open Task Manager (Windows) or htop/Activity Monitor (Linux/Mac).

If your CPU usage is maxed out across those 4 threads, the program is working perfectly. You just need to be patient.

If your RAM usage is at 99-100%, your system is swapping. You will either need to run this on a machine with more memory or reduce the search space (e.g., dropping missed cleavages to 1).

Increase Threads: If your computer has more physical cores available, stop the run and increase the thread count significantly (e.g., 8, 16, or whatever your hardware supports). This scales very well and will drastically cut down the prediction time.

If your system resources look healthy, just let it run! The in-silico library generation is a one-time heavy lift.

[queenofcorgis]

Welcome!

Thanks for posting in the GitHub Community, @wty373 ! We're happy you're here. You are more likely to get a useful response if you are posting in the applicable category. The Accessibility category is a place for our community to discuss and provide feedback on the digital accessibility of GitHub products. Digital accessibility means that GitHub tools, and technologies, are designed and developed so that people with disabilities can use them.

We’ve moved your post to our Programming Help 🧑‍💻 category, which is more appropriate for this type of discussion.

Please review our guidelines about the Programming Help category for more information.