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ePlatypus: an ecosystem for computational analysis of immunogenomics data

Kreiner, V.; Agrafiotis, A.; Cotet, T.-S.; Kuhn, R.; Shlesinger, D.; Manero-Carranza, M.; Khodaverdi, K.; Masserey, S.; Guerci, L.; Hong, K.-L.; Han, J.; Stiklioraitis, K.; Martinolli, V.; Dizerens, R.; Kilchenmann, S.; Stalder, L.; Nissen, L.; Vogelsanger, B.; Anzböck, S.; Laslo, D.; Kondorosy, M.; Venerito, M.; Garcia, A. S.; Feller, I.; Oxenius, A.; Reddy, S. T.; Yermanos, A.

2022-09-28 immunology
10.1101/2022.09.28.509709 bioRxiv
Show abstract

The maturation of systems immunology methodologies requires novel and transparent computational frameworks capable of integrating diverse data modalities in a reproducible manner. Here, we present the ePlatypus computational immunology ecosystem for immunogenomics data analysis, with a focus on adaptive immune repertoires and single-cell sequencing. ePlatypus is a web-based platform and provides programming tutorials and an integrative database that elucidates selection patterns of adaptive immunity. Furthermore, the ecosystem links novel and established bioinformatics pipelines relevant for single-cell immune repertoires and other aspects of computational immunology such as predicting ligand-receptor interactions, structural modeling, simulations, machine learning, graph theory, pseudotime, spatial transcriptomics and phylogenetics. The ePlatypus ecosystem helps extract deeper insight in computational immunology and immunogenomics and promote open science. Accessibilityhttps://alexyermanos.github.io/Platypus/index.html

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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.