Genomic prediction and estimation of marker interaction effects

Genomic prediction and estimation of marker interaction effects

Lars Ronnegard, Xia Shen

From Differentiated Genes to Affected Pathways

From Differentiated Genes to Affected Pathways

Shailesh S. Patil, Bharath Venkatesh, Randeep Singh

Increasing the Efficiency of Genome-wide Association Mapping via Hidden Markov Models

Increasing the Efficiency of Genome-wide Association Mapping via Hidden Markov Models

Hong Gao, Hua Tang, Carlos Bustamante

Efficient Breeding by Genomic Mating

Efficient Breeding by Genomic Mating

Deniz Akdemir, Julio Isidro Sanchez

Deep genome sequencing and variation analysis of 13 inbred mouse strains defines candidate phenotypic alleles, private variation, and homozygous truncating mutations

Deep genome sequencing and variation analysis of 13 inbred mouse strains defines candidate phenotypic alleles, private variation, and homozygous truncating mutations

Thomas Keane, Anthony Doran, David Adams, Kent Hunter, Jonathan Flint, Kim Wong

Novel Method for Comparing RADseq Linkage Maps Reveals Chromosome Evolution in Salmonids

Novel Method for Comparing RADseq Linkage Maps Reveals Chromosome Evolution in Salmonids

Ben J G Sutherland, Thierry Gosselin, Eric Normandeau, Manuel Lamothe, Nathalie Isabel, Celine Audet, Louis Bernatchez

Gene Ontology: Pitfalls, Biases, Remedies

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

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

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.

Translational plasticity facilitates the accumulation of nonsense genetic variants in the human population

Translational plasticity facilitates the accumulation of nonsense genetic variants in the human population

Sujatha Jagannathan, Robert K. Bradley