Back

EGGS: Empirical Genotype Generalizer for Samples

Smith, T. Q.; Rahman, A.; Szpiech, Z. A.

2025-10-17 bioinformatics
10.1101/2025.10.16.682896 bioRxiv
Show abstract

SummaryWe introduce Empirical Genotype Generalizer for Samples (EGGS) which accepts empirical genotypes with missing data and replicates the distribution of missing genotypes along the empirical segment in other replicates. The empirical segment must have a number of sites less than the replicate. In addition, EGGS can remove phase, remove polarization, simulate deamination, simulate sequencing error, create pseudohaploids, and convert between Variant Call Format (VCF), ms-style replicates, and EIGENSTRAT. When producing VCF files, EGGS assumes all samples are diploid. Availability and ImplementationEGGS is written in the C programming language. Precompiled executables, source code, and the manual are available at https://github.com/TQ-Smith/EGGS

Published in Bioinformatics Advances (predicted rank #5) · training set

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

1
Bioinformatics
◐ 1204 papers in training set
Top 0.8%
26.0%
Similar papers in this journal
2
Genome Biology
○ 637 papers in training set
Top 0.4%
18.2%
Similar papers in this journal
3
PLOS Computational Biology
● 1863 papers in training set
Top 3%
11.7%
Similar papers in this journal
50% of probability mass above
4
BMC Bioinformatics
○ 457 papers in training set
Top 0.8%
8.7%
Similar papers in this journal
Bioinformatics Advances · published here
◐ 203 papers in training set
Top 1%
5.4%
Similar papers in this journal
6
GigaScience
◐ 212 papers in training set
Top 0.9%
4.0%
7
BMC Genomics
○ 406 papers in training set
Top 3%
2.3%
8
Nucleic Acids Research
◐ 1281 papers in training set
Top 9%
1.7%
9
PLOS ONE
● 5266 papers in training set
Top 47%
1.7%
10
Molecular Biology and Evolution
◐ 542 papers in training set
Top 3%
1.7%
11
Genome Research
● 468 papers in training set
Top 4%
1.7%
12
PLOS Genetics
● 862 papers in training set
Top 8%
1.5%
13
Molecular Ecology Resources
○ 171 papers in training set
Top 1%
1.3%
14
Nature Communications
○ 5641 papers in training set
Top 51%
1.1%
15
Briefings in Bioinformatics
◐ 354 papers in training set
Top 6%
1.1%
16
Nature Methods
○ 385 papers in training set
Top 6%
1.0%
17
GENETICS
◐ 483 papers in training set
Top 4%
1.0%
18
Algorithms for Molecular Biology
○ 17 papers in training set
Top 0.2%
0.8%
19
G3: Genes, Genomes, Genetics
◐ 252 papers in training set
Top 5%
0.6%
20
Nature Computational Science
○ 55 papers in training set
Top 2%
0.6%

"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.