yHydra: Deep Learning enables an Ultra Fast Open Search by Jointly Embedding MS/MS Spectra and Peptides of Mass Spectrometry-based Proteomics
This code is an implementation of the open search of yHydra as described in our preprint: https://doi.org/10.1101/2021.12.01.470818
This code-repository implements a GPU-accelerated open search that uses the joint embeddings of yHydra. Note, this repository contains trained models of yHydra. The yHydra training pipeline will be available soon. Since this is ongoing research it may not reflect the final performance of yHydra.
System requirements:
- Linux (tested on Ubuntu 18.04), WSL(tested on 5.10.60.1-microsoft-standard-WSL2)
- Conda installed (e.g. follow documentation at https://conda.io/projects/conda/en/latest/user-guide/install/index.html),
- a GPU machine with latest CUDA installed
- (CPU-only is possible but will result in poor performance)
Note: installation of required packages takes up to several minutes.
Start by cloning this github-repository:
git clone https://github.com/tzom/yHydra
cd yHydraIf running in WSL
sudo apt install dos2unix
dos2unix *To install the required packages create a new conda environment (required packages are automatically installed when using yhydra_env.yml):
conda env create -f yhydra_env.yml
conda activate yhydra_env
bash install_thermorawfileparser.sh
conda install -y pandas lxml
sudo apt install jqThe following commands will download example data (from https://www.ebi.ac.uk/pride/archive/projects/PXD007963) and run the pipeline of yHydra:
mkdir example
wget -nc -i example_data_urls.txt -P example/
gzip example/SynPCC7002_Cbase.fasta
bash run.sh config.yamlTo run yHydra specify the location of input files in ./config.yaml:
# Input - File Locations
FASTA: example/*.fasta.gz
RAWs: example/*.raw
# Output - Results directory
RESULTS_DIR: example/search
# General Parameters
BATCH_SIZE: 64
...then you can run yHydra using specified parameters (./config.yaml):
bash run.sh config.yamlThe search results are dumped as dataframe in .hdf-files (e.g. locations is specified as RESULTS_DIR: example/search in the ./config.yaml), in order to get a glimpse of identfications, you can run this:
python inspect_search.py
which gives you the following output:
(yhydra_env) animeshs@DMED7596:/mnt/f/OneDrive - NTNU/yHydra$ python inspect_search.py
raw_file id is_decoy precursorMZ pepmass ... best_peptide peptide_mass delta_mass q accession
0 qe2_03132014_1WT-1 13677 [False, False, True, True, True, True, False, ... 699.032500 2094.074025 ... ADTAGVHGAALGADEIELTRK 2094.070485 0.003540 0.000000 [SYNPCC7002_A1022]
1 qe2_03132014_13WT-3 17071 [False, False, False, True, False, True, True,... 900.941528 1799.867407 ... DIVTQFHGAEAAVDAEK 1799.868945 -0.001538 0.000000 [SYNPCC7002_A1609]
2 qe2_03132014_1WT-1 20532 [False, False, False, False, True, False, Fals... 925.985352 1849.955053 ... TLIEGLDEISHGGLPSGR 1849.953345 0.001708 0.000000 [SYNPCC7002_A0287]
3 qe2_03132014_5WT-2 18652 [False, False, True, True, False, False, False... 779.409100 2335.203825 ... SIEAEQLKDDLPTIHVGDTVR 2335.201905 0.001920 0.000000 [SYNPCC7002_A1033]
4 qe2_03132014_1WT-1 17209 [False, False, True, True, False, True, False,... 900.942505 1799.869360 ... DIVTQFHGAEAAVDAEK 1799.868945 0.000415 0.000000 [SYNPCC7002_A1609]
... ... ... ... ... ... ... ... ... ... ... ...
23643 qe2_03132014_13WT-3 17280 [True, False, False, True, False, True, True, ... 473.229431 944.443212 ... MFDIFTR 928.447665 15.995548 0.009897 [SYNPCC7002_A2209]
23644 qe2_03132014_5WT-2 3564 [True, False, True, False, True, True, False, ... 329.179352 1312.686107 ... KEESELIDAHGK 1354.672825 -41.986718 0.009980 [SYNPCC7002_A2459]
23645 qe2_03132014_13WT-3 14744 [False, True, False, True, False, False, False... 757.902893 1513.790136 ... AEKNIILSIEDIR 1512.851125 0.939011 0.009980 [SYNPCC7002_F0019]
23646 qe2_03132014_5WT-2 4053 [False, True, False, True, False, False, False... 310.158813 927.452965 ... RGGGGDR 673.325565 254.127401 0.009980 [SYNPCC7002_A0148]
23647 qe2_03132014_13WT-3 4498 [True, False, True, False, True, False, False,... 452.226990 1353.657494 ... TNYVPHVSFTGTK 1449.725215 -96.067721 0.009980 [SYNPCC7002_A2578]
[23648 rows x 19 columns]Tom Altenburg (tzom)
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