EasiGP
Shunichiro Tomura
This code is used for "Ensemble AnalySis with Interpretable Genomic Prediction (EasiGP): Computational Tool for Interpreting Ensembles of Genomic Prediction Models". (https://doi.org/10.1002/tpg2.70138)
EasiGP analyses the ensemble of multiple diverse genomic prediction models at the genome level in crop breeding programs. Circos plots are then constructed using the effect of each genomic marker region and the interactions of these genomic marker regions for a target trait. With a circos plot view, we can visually compare the inferred trait genetic architecture of each genomic prediction model to deepen the understanding of the predictive behaviour of each genomic prediction model at the genome level. The comparison of the inferred genomic marker effects with known key genome regions also enables the discovery of potential new genome regions that have not been well-investigated in previous studies.
Ensemble
Shunichiro Tomura
The shared code is used for "Improvements in Prediction Performance of Ensemble Approaches for Genomic Prediction in Crop Breeding" (https://doi.org/10.1093/g3journal/jkaf048).
Workflow:
- Your target data should be preprocessed first. Imputation and pruning were applied to this experiment.
- Install required libraries and packages (check the "environment" folder for the details).
- Each individual genomic prediction model should return their genomic prediction results. These are used as input for an ensemble.
RWR Toolkit
David Kainer
A set of command-line and R tools for performing Random-walk with Restart analyses on multiplex networks in any species.
cpam: Changepoint Additive Models for Time Series Omics Data
Luke Yates, Michael Charleston, Jazmine Humphreys, Steven Smith
Provides a comprehensive framework for time series omics analysis, integrating changepoint detection, smooth and shape-constrained trends, and uncertainty quantification. It supports gene- and transcript-level inferences, p-value aggregation for improved power, and both case-only and case-control designs. It includes an interactive 'shiny' interface. The methods are described in Yates et al. (2024) (doi:10.1101/2024.12.22.630003)
PSoup
Nicole Fortuna
A package that models pea hormone interactions, and predicts branching phenotypes.
LLMannotator
David Kainer
An AI driven auto-annotation workflow for assigning ontology terms to text.
APSIM Initiative
