1.
Sauer, Lena M.; Cánovas, Rodrigo; Roche, Daniel Barry; Shams-Eldin, Hosam; Ravel, Patrice; Colinge, Jacques; Schwarz, Ralph T.; Mamoun, Choukri Ben; Rivals, Eric; Cornillot, Emmanuel
In: Malaria Journal, vol. 22, no. 1, pp. 27-46, 2023, ISBN: 1475-2875.
Abstract | Links | BibTeX | Tags: Misc
@article{Sauer2023,
title = {FT-GPI, a highly sensitive and accurate predictor of GPI-anchored proteins, reveals the composition and evolution of the GPI proteome in Plasmodium species},
author = {Lena M. Sauer and Rodrigo Cánovas and Daniel Barry Roche and Hosam Shams-Eldin and Patrice Ravel and Jacques Colinge and Ralph T. Schwarz and Choukri Ben Mamoun and Eric Rivals and Emmanuel Cornillot},
doi = {10.1186/s12936-022-04430-0},
isbn = {1475-2875},
year = {2023},
date = {2023-12-01},
urldate = {2023-12-01},
journal = {Malaria Journal},
volume = {22},
number = {1},
pages = {27-46},
publisher = {BioMed Central},
abstract = {Protozoan parasites are known to attach specific and diverse group of proteins to their plasma membrane via a GPI anchor. In malaria parasites, GPI-anchored proteins (GPI-APs) have been shown to play an important role in host–pathogen interactions and a key function in host cell invasion and immune evasion. Because of their immunogenic properties, some of these proteins have been considered as malaria vaccine candidates. However, identification of all possible GPI-APs encoded by these parasites remains challenging due to their sequence diversity and limitations of the tools used for their characterization.},
keywords = {Misc},
pubstate = {published},
tppubtype = {article}
}
Protozoan parasites are known to attach specific and diverse group of proteins to their plasma membrane via a GPI anchor. In malaria parasites, GPI-anchored proteins (GPI-APs) have been shown to play an important role in host–pathogen interactions and a key function in host cell invasion and immune evasion. Because of their immunogenic properties, some of these proteins have been considered as malaria vaccine candidates. However, identification of all possible GPI-APs encoded by these parasites remains challenging due to their sequence diversity and limitations of the tools used for their characterization.
2.
Petescia, Alessia; Nebohacova, Martina; Nosek, Jozef; Brejova, Brona; Vinar, Tomas
Contaminant Detection using Differentiating k-mers Presentation
Poster Presentation at ECCB 2022, 12.09.2022.
@misc{Petescia-presentation,
title = {Contaminant Detection using Differentiating k-mers},
author = {Alessia Petescia and Martina Nebohacova and Jozef Nosek and Brona Brejova and Tomas Vinar},
year = {2022},
date = {2022-09-12},
urldate = {2022-09-12},
issue = {ECCB 2022},
howpublished = {Poster Presentation at ECCB 2022},
keywords = {Misc},
pubstate = {published},
tppubtype = {presentation}
}
3.
Sládeček, T.; Gažiová, M.; Pös, O.; Pös, Z.; Budiš, J.; Radvánsky, J.; Szemes, T.
Combination of expert decision systems with artificial intelligence leads to superior accuracy of automated prediction of clinical effect of copy number variation Presentation
Poster Presentation at ESHG, 01.06.2022.
BibTeX | Tags: Misc, WP3: Translational CPG
@misc{Sladecek2022,
title = {Combination of expert decision systems with artificial intelligence leads to superior accuracy of automated prediction of clinical effect of copy number variation},
author = {T. Sládeček and M. Gažiová and O. Pös and Z. Pös and J. Budiš and J. Radvánsky and T. Szemes},
year = {2022},
date = {2022-06-01},
howpublished = {Poster Presentation at ESHG},
keywords = {Misc, WP3: Translational CPG},
pubstate = {published},
tppubtype = {presentation}
}
4.
Gažiová, M.; Sládeček, T.; Pös, O.; Števko, M.; Krampl, W.; Pös, Z.; Hekel, R.; Hlavačka, M.; Kucharík, M.; Radvánszky, J.; Budiš, J.; Szemes, T.
Automated prediction of the clinical impact of structural copy number variations Journal Article
In: Scientific Reports, vol. 12, no. 1, pp. 555, 2022, ISSN: 2045-2322.
Abstract | Links | BibTeX | Tags: Misc, WP3: Translational CPG
@article{Gaziova2022,
title = {Automated prediction of the clinical impact of structural copy number variations},
author = {M. Gažiová and T. Sládeček and O. Pös and M. Števko and W. Krampl and Z. Pös and R. Hekel and M. Hlavačka and M. Kucharík and J. Radvánszky and J. Budiš and T. Szemes},
doi = {10.1038/s41598-021-04505-z},
issn = {2045-2322},
year = {2022},
date = {2022-01-11},
urldate = {2022-01-11},
journal = {Scientific Reports},
volume = {12},
number = {1},
pages = {555},
publisher = {Springer Science and Business Media LLC},
abstract = {Copy number variants (CNVs) play an important role in many biological processes, including the development of genetic diseases, making them attractive targets for genetic analyses. The interpretation of the effect of these structural variants is a challenging problem due to highly variable numbers of gene, regulatory, or other genomic elements affected by the CNV. This led to the demand for the interpretation tools that would relieve researchers, laboratory diagnosticians, genetic counselors, and clinical geneticists from the laborious process of annotation and classification of CNVs. We designed and validated a prediction method (ISV; Interpretation of Structural Variants) that is based on boosted trees which takes into account annotations of CNVs from several publicly available databases. The presented approach achieved more than 98{%} prediction accuracy on both copy number loss and copy number gain variants while also allowing CNVs being assigned “uncertain”significance in predictions. We believe that ISV’s prediction capability and explainability have a great potential to guide users to more precise interpretations and classifications of CNVs.},
keywords = {Misc, WP3: Translational CPG},
pubstate = {published},
tppubtype = {article}
}
Copy number variants (CNVs) play an important role in many biological processes, including the development of genetic diseases, making them attractive targets for genetic analyses. The interpretation of the effect of these structural variants is a challenging problem due to highly variable numbers of gene, regulatory, or other genomic elements affected by the CNV. This led to the demand for the interpretation tools that would relieve researchers, laboratory diagnosticians, genetic counselors, and clinical geneticists from the laborious process of annotation and classification of CNVs. We designed and validated a prediction method (ISV; Interpretation of Structural Variants) that is based on boosted trees which takes into account annotations of CNVs from several publicly available databases. The presented approach achieved more than 98{%} prediction accuracy on both copy number loss and copy number gain variants while also allowing CNVs being assigned “uncertain”significance in predictions. We believe that ISV’s prediction capability and explainability have a great potential to guide users to more precise interpretations and classifications of CNVs.