Abstract The standard models for genomic prediction assume additive polygenic marker effects. For epistatic models including marker interaction effects, the number of effects to be fitted becomes large, which require computational tools tailored specifically for such models. Here, we extend the methods implemented in the R package bigRR so that marker interaction effects can be computed. Simulation results based on marker data from Arabidopsis thaliana show that the inclusion of interaction effects between markers can give a small but significant improvement in genomic predictions. The methods were implemented in the R package EPISbigRR available in the bigRR project on R-Forge. The package includes an introductory vignette to the functions available in EPISbigRR.
Gene Ontology: Pitfalls, Biases, Remedies
Pascale Gaudet, Christophe Dessimoz
The Gene Ontology (GO) is a formidable resource but there are several considerations about it that are essential to understand the data and interpret it correctly. The GO is sufficiently simple that it can be used without deep understanding of its structure or how it is developed, which is both a strength and a weakness. In this chapter, we discuss some common misinterpretations of the ontology and the annotations. A better understanding of the pitfalls and the biases in the GO should help users make the most of this very rich resource. We also review some of the misconceptions and misleading assumptions commonly made about GO, including the effect of data incompleteness, the importance of annotation qualifiers, and the transitivity or lack thereof associated with different ontology relations. We also discuss several biases that can confound aggregate analyses such as gene enrichment analyses. For each of these pitfalls and biases, we suggest remedies and best practices.
Primer on the Gene Ontology
Pascale Gaudet, Nives Škunca, James C. Hu, Christophe Dessimoz
The Gene Ontology (GO) project is the largest resource for cataloguing gene function. The combination of solid conceptual underpinnings and a practical set of features have made the GO a widely adopted resource in the research community and an essential resource for data analysis. In this chapter, we provide a concise primer for all users of the GO. We briefly introduce the structure of the ontology and explain how to interpret annotations associated with the GO.
On Determining if Tree-based Networks Contain Fixed Trees
Maria Anaya, Olga Anipchenko-Ulaj, Aisha Ashfaq, Joyce Chiu, Mahedi Kaiser, Max Shoji Ohsawa, Megan Owen, Ella Pavlechko, Katherine St. John, Shivam Suleria, Keith Thompson, Corrine Yap
We address an open question of Francis and Steel about phylogenetic networks and trees. They give a polynomial time algorithm to decide if a phylogenetic network, N, is tree-based and pose the problem: given a fixed tree T and network N, is N based on T? We show that it is NP-hard to decide, by reduction from 3-Dimensional Matching (3DM), and further, that the problem is fixed parameter tractable.