Datasets:

Modalities:
Text
Formats:
parquet
Libraries:
Datasets
pandas
License:
Dataset Viewer
Auto-converted to Parquet
Sequences
stringlengths
7
40
Classes
int64
0
3
Proteins
stringlengths
6
10
FLLPFWLQVIFISLLLCLSGMFSGLNLGLMALDPMELR
0
Q9H8M5
SMSAPVIFDR
3
O60749
LPRPICQHVQACPERPQMMGTLER
0
Q9BX69
EFCEWMIQQIGPK
1
P28067
VADEMDLTLGHEVGYSIPQEDCTGPNTLLR
1
Q8TE96
VDLLGEFQSALPK
3
Q9H9L3
CSSVTGVQR
2
O60343
TTVGMDGTLYK
1
Q2TB90
VYFLNSTMASNNMTLFFR
0
Q15762
EKPVTDSNK
0
Q8WTT2
LCPPGIPTPGSGLPPPR
3
P27708
TNLASTVLSLK
3
Q14562
TGDLLEVQQPVDLGALR
3
O14841
TIELIEFEK
3
Q9NW08
AIHSIFK
2
Q9H668
SPQPQLLSNK
3
Q15031
FYEGTFNWESVK
3
Q8N392
CYLLIQQYSEALMALTTMASLR
0
Q9C091
HFQTLYVEPGLCLCDCPGLVMPSFVSTK
2
Q9H089
YIMSDLGPQER
0
Q6ZSZ6
GLVYIQQTDDSLIHFCWK
3
Q16186
ELVVPVAGSCVVDAVPAPGPSPSLYCR
2
P54760
DGEDIEFR
1
A7XYQ1
ADMTASGSPDYGQPHK
2
Q01543
VLCTAPAK
0
O75425
NPEISHMLNNPDIMR
3
Q9UMX0
NGFLLDGFPR
3
P54819
QELIECVANSDEQLGEMFLEEK
3
Q96RP9
LALFGEHVGALR
3
Q9BWH6
VIVQLVTNGK
3
P19838
IPNIYAIGDVVAGPMLAHK
3
P09622
SLLNVSR
3
Q96M27
GHLFCWECLGEAHEPCDCQTWK
2
Q9P2G1
TPDGQGLSTYK
3
Q5VYK3
SPCLLLLWLLLLR
0
Q96JA1
LENNHCHIEESEHVLK
2
Q96JC1
SGTSVLHMNSR
1
Q68D06
DPDELDR
1
P16144
DCYCTVNLDQEEVFR
3
Q14644
GTEETPK
0
Q86W26
IFNNQEFAQLLAQSVNHGFETVYELTK
0
Q15797
DINYVNPVIK
1
P01031
AFPQHCRPR
1
A6NKF1
ELTLDMPK
1
Q9Y6Q1
TPTATVTNEASCWSGPSPEGPVPLTGEELDLR
0
Q9P107
GQTVEQVSNAVGALAK
1
Q9UKX2
SYQITEK
1
O75691
IMGIPEEEQMGLLR
3
P35579
EDDLNSFNATDLK
3
P09960
AVNGETLK
0
Q5SW79
ESPDGNNVACILTLPPYQR
1
Q9H7Z6
VLELEDELQESR
3
Q08378
VTVNTAYGSNGVSVLR
0
Q9Y639
DVQVLFMR
3
O14841
TALMLGCEYGCR
2
Q9BZF9
LEASDCDHQQNSPTLERPGR
2
Q9HAN9
ELGYVTLMCGDGTNDVGALK
3
Q9HD20
HLVMPEHQSR
0
Q96NU1
VPGGFSLLHMLFLHHAFQMDSCLPQPNTLPPQRPK
0
Q6BDS2
LEVAMEEEGLADEEK
1
Q9Y2H1
AGYDNAHSAPSLGMVSNGSTLLNHTSDR
0
P50443
VILVGSDITFCCVSQEK
0
P42702
EICADPK
0
P13500
NHMFLYSVLGSILGQLAVIYIPPLQR
0
O75185
LLLTYADNILR
3
Q96IV0
MALPPQEDATASPPR
3
Q9UQ35
MSFGFYK
0
Q6ICG6
VAAAVDQSAFYSALWGSLLTSPAVR
0
Q5JWR5
LAALNPESNTAGLDIFAK
3
O00299
DASLTLPGLTIQDEGTYICQITTSLYR
0
Q9BX59
YSPENFPYR
2
Q5QJ74
VLSGSEVTA
0
O75387
TSLETLPPGSVLLK
1
O00472
LLSDISAR
2
Q8WYN0
QDCCYDNR
1
Q15646
NHQEVDMNVVR
2
Q01201
EFMGELWPLLLSAQENIAGIPSAFLELK
1
Q8IYB3
TITGSETNLLFFWETHGTK
1
P01583
ALGEEALLR
3
Q92503
LVTTALR
2
Q9ULG6
LLQDISEAR
3
Q63HN8
YNLHPGVTDYMDR
3
Q8N108
NAWNYMLNNYTK
3
Q15800
LVPGGGATEIELAK
3
P50990
PVSAVPPLATNTVSPSLALLANNLSMPTSDLPPGASPR
0
Q9H0E3
TNLGMVLGTLILLHR
1
Q9ULE6
AQVEQELTTLR
3
Q15149
EILTEQDVNGAVLK
2
Q5K651
VAGGAGGGVSKPHAK
2
Q06945
QINLSNIR
3
Q7KZF4
GDMGGAR
0
Q9C0C4
LITKPQNLNDAYGPPSNFLEIDVSNPQTVGVGR
3
O60493
YTDAHYAK
0
O75604
SDHPVGHISGPEVVGSGFQSSVAVR
1
Q5VUA4
LASDDTQK
0
Q0VF96
ELLGPPEAK
2
Q14807
NQAQQPFSLWAR
0
Q9UJX0
LSSDATVLTPNTESSCDLMTK
3
Q63HQ0
SDSALLEGAELVNR
3
P42858
TSSGLFPR
0
Q8N3J3
End of preview. Expand in Data Studio

Detectability - Sinitcyn

This dataset contains bottom-up proteomics data from six different human cell lines (GM12878, HeLa S3, HepG2, hES1, HUVEC, and K562), deep fractioning (24–80 fractions) and three different fragmentation methods (HCD, CAD and ETD). All cell lines were digested with six different proteases (LysC, LysN, AspN, chymotrypsin, GluC and trypsin).

Dataset Details

  • Curated by: Aalborg University - Denmark, in collaboration with Wilhelmlab, TU Munich.
  • License: CC0 1.0 Universal

Dataset Sources

The data is based on the datasets introduced in [1] and available at: https://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD024364

Uses

The dataset is intended to be used for training, fine-tuning, and evaluating detectability prediction models, given a peptide sequence.

References

[1] Sinitcyn, P., Richards, A. L., Weatheritt, R. J., Brademan, D. R., Marx, H., Shishkova, E., ... & Coon, J. J. (2023). Global detection of human variants and isoforms by deep proteome sequencing. Nature biotechnology, 41(12), 1776-1786.

Citation

BibTeX:

@article {Abdul-Khalek2024.10.28.620610,
    author = {Abdul-Khalek, Naim and Picciani, Mario and Wimmer, Reinhard and Overgaard, Michael Toft and Wilhelm, Mathias and Echers, Simon Gregersen},
    title = {To fly, or not to fly, that is the question: A deep learning model for peptide detectability prediction in mass spectrometry},
    elocation-id = {2024.10.28.620610},
    year = {2024},
    doi = {10.1101/2024.10.28.620610},
    publisher = {Cold Spring Harbor Laboratory},
    URL = {https://www.biorxiv.org/content/early/2024/10/31/2024.10.28.620610},
    eprint = {https://www.biorxiv.org/content/early/2024/10/31/2024.10.28.620610.full.pdf},
    journal = {bioRxiv}
}

APA:

Abdul-Khalek, N., Picciani, M., Wimmer, R., Overgaard, M. T., Wilhelm, M., & Gregersen Echers, S. (2024). To fly, or not to fly, that is the question: A deep learning model for peptide detectability prediction in mass spectrometry. bioRxiv, 2024-10.‏

Dataset Card Contact

Simon Gregersen, [email protected], Department of Chemistry and Biosciences, Aalborg University.

Mathias Wilhelm, [email protected], Wilhelmlab, TU Munich, School of Life Sciences, Germany.

Downloads last month
26

Collection including Wilhelmlab/detectability-sinitcyn