Epistatic Filtrations Calculator

This is an online client for computing higher-order epistatic interactions as detailed in the articles [1] and [2]. It was implemented as polymake extension and can be found online on GitHub. If you found this useful for your scientific work, please cite our paper [1].

The input is a sequence of genotype-phenotype maps, where several phenotypes for the same genotype are considered as independent measurements, thus giving rise to a distribution of phenotypes. Genotypes are 0/1-vectors (i.e., here we are treating the biallelic case only), and phenotypes are real numbers. The entire dataset is supposed to be contained in a single file of type csv (ASCII text, comma separated values). Such files can be exported from standard spreadsheet software.

Upload csv file
The input csv file must be in the precise format shown in the exemplary screenshot on the right hand side:
  • The genotypes are placed in the first data row. Their coordinates are separated by vertical bars, e.g. 0|0|1|0.
  • Right below the genotypes, the measured data is placed accordingly. The columns are allowed to be of varying size.

Please upload a file.

Download result file

After roughly five minutes, in order to download the results for a previously submitted csv data sheet, please enter your id here:

Data sets examined in our papers [1] and [2]
Explanation of the output

Your input will be rejected

Once passed this check, the epistatic filtration of the input data is computed and the following reports are given back: If the given genotypes describe the vertices of the complete n-dimensional cube, various parallel transports are performed, see Section 6.6. and 6.7 of [1]. For each locus i in {1,...,n} the parallel information **0***→**1*** and **1***→**0*** is computed where the digits 0 and 1 are in the i-th place. The results can be found in the folders parallel/locus〈i〉/0+TO+1 (first case) and parallel/locus〈i〉/1+TO+0 (second case):

  1. Holger Eble, Michael Joswig, Lisa Lamberti, and William B. Ludington: Cluster partitions and fitness landscapes of the Drosophila fly microbiome, J. Math. Biology (2019), no. 79, 861–899, doi: 10.1007/s00285-019-01381-0.
  2. Holger Eble, Michael Joswig, Lisa Lamberti, and William B. Ludington: Higher-order interactions in fitness landscapes are sparse, preprint, ArXiv:2009.12277.