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README.md

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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)
+
+1. Peng, X. et al. Identification of missing proteins in the phosphoproteome of kidney cancer. J. Proteome Res. 16, 4364–4373,
+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
+data-dependent methods using an orbitrap fusion lumos tribrid mass spectrometer. PROTEOMICS 17, DOI: 10.1002/pmic.
+201600416 (2017).
+5. Bekker-Jensen, D. B. et al. An optimized shotgun strategy for the rapid generation of comprehensive human proteomes.
+Cell Syst. 4, 587–599.e4, DOI: 10.1016/j.cels.2017.05.009 (2017).
+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).
+7. Liu, Z., Wang, F., Chen, J., Zhou, Y. & Zou, H. Modulating the selectivity of affinity absorbents to multi-phosphopeptides
+by a competitive substitution strategy. J. Chromatogr. A 1461, 35–41, DOI: 10.1016/j.chroma.2016.07.042 (2016).
+8. Humphrey, E. S. et al. Resolution of novel pancreatic ductal adenocarcinoma subtypes by global phosphotyrosine profiling.
+Mol. Cell. Proteomics 15, 2671–2685 (2016).
+9. Picariello, G. et al. Antibody-independent identification of bovine milk-derived peptides in breast-milk. Food Funct. 7,
+3402–3409 (2016).
+10. Francavilla, C. et al. Phosphoproteomics of primary cells reveals druggable kinase signatures in ovarian cancer. Cell
+Reports 18, 3242–3256, DOI: 10.1016/j.celrep.2017.03.015 (2017).
+11. Lyon, S. M. et al. A method for whole protein isolation from human cranial bone. Anal. Biochem. 515, 33–39, DOI:
+10.1016/j.ab.2016.09.021 (2016).
+12. Nguyen, E. V. et al. Hyper-phosphorylation of sequestosome-1 distinguishes resistance to cisplatin in patient derived high
+grade serous ovarian cancer cells. Mol. amp; Cell. Proteomics 16, 1377–1392, DOI: 10.1074/mcp.m116.058321 (2017).
+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).
+19. Kauko, O. et al. Label-free quantitative phosphoproteomics with novel pairwise abundance normalization reveals synergistic
+ras and cip2a signaling. Sci. Reports 5, DOI: 10.1038/srep13099 (2015).
+20. Alpert, A. J., Hudecz, O. & Mechtler, K. Anion-exchange chromatography of phosphopeptides: weak anion exchange
+versus strong anion exchange and anion-exchange chromatography versus electrostatic repulsion-hydrophilic interaction
+chromatography. Anal. Chem. 87, 4704–4711 (2015).
+21. Suni, V., Imanishi, S. Y., Maiolica, A., Aebersold, R. & Corthals, G. L. Confident site localization using a simulated
+phosphopeptide spectral library. J. Proteome Res. 14, 2348–2359 (2015).
+17/25
+22. Sharma, K. et al. Ultradeep human phosphoproteome reveals a distinct regulatory nature of tyr and ser/thr-based signaling.
+Cell Reports 8, 1583–1594, DOI: 10.1016/j.celrep.2014.07.036 (2014).
+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).

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