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license: other
license_name: embl-ebi-terms-of-use
license_link: https://www.ebi.ac.uk/about/terms-of-use/
configs:
- config_name: default
data_files:
- split: train
path: dataset-phospho-train-*.parquet
- split: validation
path: dataset-phospho-valid-*.parquet
- split: test
path: dataset-phospho-test-*.parquet
dataset_info:
features:
- name: sequence
dtype: string
- name: precursor_charge
dtype: int64
- name: precursor_mz
dtype: float64
- name: mz_array
sequence: float64
- name: intensity_array
sequence: float64
- name: experiment_name
dtype: string
tags:
- biology
size_categories:
- 1M<n<10M
---
# Dataset Card for InstaNovo-P finetuning data
The dataset used for fine tuning InstaNovo-P is comprised of a collection of reprocessed PRIDE projects in [Scop3P](https://pubs.acs.org/doi/10.1021/acs.jproteome.0c00306). (For a list of the projects, see [Dataset Sources](https://huggingface.co/datasets/InstaDeepAI/InstaNovo-P#dataset-sources)).
- **Curated by:** Jesper Lauridsen, Pathmanaban Ramasamy
- **License:** [EMBL-EBI terms of use](https://www.ebi.ac.uk/about/terms-of-use/)
## Dataset Details
### Dataset Description
The dataset originally contains 4,053,346 PSMs. To only fine-tune on high confidence PSMs, the dataset is filtered at a confidence threshold of 0.80, reducing it to 2,760,939 PSMs, representing 74,686 unique peptide sequences. Most of the data is of human origin, except for [PXD005366](https://www.ebi.ac.uk/pride/archive/projects/PXD005366) and [PXD000218](https://www.ebi.ac.uk/pride/archive/projects/PXD000218), which contain a mix of human and mouse. All PSMs that were used to train the model contained at least one phosphorylated site, while 169, 114 PSMs
( 6%) contained oxidated methionine.
### Dataset Structure
To partition the fine-tuning dataset into training, validation and test, [GraphPart](https://academic.oup.com/nargab/article/5/4/lqad088/7318077), an algorithm for homology partitioning, was applied on the set of unique peptide sequences. GraphPart was set to use [MMseqs2](https://www.nature.com/articles/nbt.3988) with a partitioning threshold of 0.8 and a train-validation-test ratio of 0.7/0.1/0.2 . Of the 74,686 unique sequences, 390 were removed by GraphPart, reducing the total number of PSMs to 2,691,117 in a 2,008,923/232,641/449,553-split, although during training, a random subset of only 2% of the validation set was used in order to reduce computation.
### Dataset Sources
PRIDE Accession codes used for training, validation and test sets:
* [PXD006482](https://www.ebi.ac.uk/pride/archive/projects/PXD006482) (1)
* [PXD005366](https://www.ebi.ac.uk/pride/archive/projects/PXD005366) (2)
* [PXD004447](https://www.ebi.ac.uk/pride/archive/projects/PXD004447) (3)
* [PXD004940](https://www.ebi.ac.uk/pride/archive/projects/PXD004940) (4)
* [PXD004452](https://www.ebi.ac.uk/pride/archive/projects/PXD004452) (5)
* [PXD004415](https://www.ebi.ac.uk/pride/archive/projects/PXD004415) (6)
* [PXD004252](https://www.ebi.ac.uk/pride/archive/projects/PXD004252) (7)
* [PXD003198](https://www.ebi.ac.uk/pride/archive/projects/PXD003198) (8)
* [PXD003657](https://www.ebi.ac.uk/pride/archive/projects/PXD003657) (9)
* [PXD003531](https://www.ebi.ac.uk/pride/archive/projects/PXD003531) (10)
* [PXD003215](https://www.ebi.ac.uk/pride/archive/projects/PXD003215) (11)
* [PXD002394](https://www.ebi.ac.uk/pride/archive/projects/PXD002394) (12)
* [PXD002286](https://www.ebi.ac.uk/pride/archive/projects/PXD002286) (13)
* [PXD002255](https://www.ebi.ac.uk/pride/archive/projects/PXD002255) (14)
* [PXD002057](https://www.ebi.ac.uk/pride/archive/projects/PXD002057) (15)
* [PXD001565](https://www.ebi.ac.uk/pride/archive/projects/PXD001565) (16)
* [PXD001550](https://www.ebi.ac.uk/pride/archive/projects/PXD001550) (17)
* [PXD001546](https://www.ebi.ac.uk/pride/archive/projects/PXD001546) (17)
* [PXD001060](https://www.ebi.ac.uk/pride/archive/projects/PXD001060) (18)
* [PXD001374](https://www.ebi.ac.uk/pride/archive/projects/PXD001374) (19)
* [PXD001333](https://www.ebi.ac.uk/pride/archive/projects/PXD001333) (20)
* [PXD000474](https://www.ebi.ac.uk/pride/archive/projects/PXD000474) (21)
* [PXD000612](https://www.ebi.ac.uk/pride/archive/projects/PXD000612) (22)
* [PXD001170](https://www.ebi.ac.uk/pride/archive/projects/PXD001170) (23)
* [PXD000964](https://www.ebi.ac.uk/pride/archive/projects/PXD000964) (24)
* [PXD000836](https://www.ebi.ac.uk/pride/archive/projects/PXD000836) (25)
* [PXD000674](https://www.ebi.ac.uk/pride/archive/projects/PXD000674) (26)
* [PXD000680](https://www.ebi.ac.uk/pride/archive/projects/PXD000680) (27)
* [PXD000218](https://www.ebi.ac.uk/pride/archive/projects/PXD000218) (28)
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DOI: 10.1021/acs.jproteome.7b00332 (2017).
2. Post, H. et al. Robust, sensitive, and automated phosphopeptide enrichment optimized for low sample amounts applied to
primary hippocampal neurons. J. Proteome Res. 16, 728–737, DOI: 10.1021/acs.jproteome.6b00753 (2016).
3. Tsiatsiani, L. et al. Opposite electron-transfer dissociation and higher-energy collisional dissociation fragmentation
characteristics of proteolytic k/r(x)n and (x)nk/r peptides provide benefits for peptide sequencing in proteomics and
phosphoproteomics. J. Proteome Res. 16, 852–861, DOI: 10.1021/acs.jproteome.6b00825 (2016).
4. Espadas, G., Borràs, E., Chiva, C. & Sabidó, E. Evaluation of different peptide fragmentation types and mass analyzers in
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6. Tran, T. T., Strozynski, M. & Thiede, B. Quantitative phosphoproteome analysis of cisplatin-induced apoptosis in jurkat t
cells. PROTEOMICS 17, DOI: 10.1002/pmic.201600470 (2017).
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13. Drake, J. M. et al. Phosphoproteome integration reveals patient-specific networks in prostate cancer. Cell 166, 1041–1054,
DOI: 10.1016/j.cell.2016.07.007 (2016).
14. Su, N. et al. Special enrichment strategies greatly increase the efficiency of missing proteins identification from regular
proteome samples. J. Proteome Res. 14, 3680–3692 (2015).
15. Creedon, H. et al. Identification of novel pathways linking epithelial-to-mesenchymal transition with resistance to
her2-targeted therapy. Oncotarget 7, 11539–11552, DOI: 10.18632/oncotarget.7317 (2016).
16. van der Mijn, J. C. et al. Evaluation of different phospho-tyrosine antibodies for label-free phosphoproteomics. J.
Proteomics 127, 259–263, DOI: 10.1016/j.jprot.2015.04.006 (2015).
17. Piersma, S. R. et al. Feasibility of label-free phosphoproteomics and application to base-line signaling of colorectal cancer
cell lines. J. Proteomics 127, 247–258, DOI: 10.1016/j.jprot.2015.03.019 (2015).
18. Ruprecht, B. et al. Comprehensive and reproducible phosphopeptide enrichment using iron immobilized metal ion affinity
chromatography (fe-imac) columns. Mol. amp; Cell. Proteomics 14, 205–215, DOI: 10.1074/mcp.m114.043109 (2015).
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ras and cip2a signaling. Sci. Reports 5, DOI: 10.1038/srep13099 (2015).
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17/25
22. Sharma, K. et al. Ultradeep human phosphoproteome reveals a distinct regulatory nature of tyr and ser/thr-based signaling.
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23. Tong, J. et al. Integrated analysis of proteome, phosphotyrosine-proteome, tyrosine-kinome, and tyrosine-phosphatome in
acute myeloid leukemia. PROTEOMICS 17, DOI: 10.1002/pmic.201600361 (2017).
24. Bauer, M. et al. Evaluation of data-dependent and -independent mass spectrometric workflows for sensitive quantification
of proteins and phosphorylation sites. J. Proteome Res. 13, 5973–5988 (2014).
25. Shevchuk, O. et al. HOPE-fixation of lung tissue allows retrospective proteome and phosphoproteome studies. J. Proteome
Res. 13, 5230–5239 (2014).
26. Publication pending
27. Molden, R. C., Goya, J., Khan, Z. & Garcia, B. A. Stable isotope labeling of phosphoproteins for large-scale phosphorylation
rate determination. Mol. amp; Cell. Proteomics 13, 1106–1118, DOI: 10.1074/mcp.o113.036145 (2014).
28. Rajeeve, V., Vendrell, I., Wilkes, E., Torbett, N. & Cutillas, P. R. Cross-species proteomics reveals specific modulation of
signaling in cancer and stromal cells by phosphoinositide 3-kinase (PI3K) inhibitors. Mol. Cell. Proteomics 13, 1457–1470
(2014). |