3 Works

Data from: Fitness of Arabidopsis thaliana mutation accumulation lines whose spontaneous mutations are known

Charles B. Fenster, Matthew Thomas Rutter, Angela J. Roles, Jeffrey K. Conner, Ruth G. Shaw, Frank Holcomb Shaw, Korbinian Schneeberger, Stephan Ossowski & Detlef Weigel
Despite the fundamental importance of mutation to the evolutionary process, we have little knowledge of the direct consequences of specific spontaneous mutations to the fitness of the organism. Combining results of whole-genome sequencing with repeated field assays of survival and reproduction, we quantify the combined effects on fitness of spontaneous mutations identified in Arabidopsis thaliana. We demonstrate that the effects are beneficial, deleterious or neutral depending on the environmental context. Some lines, bearing mutations disrupting...

Data from: Independent FLC mutations as causes of flowering time variation in Arabidopsis thaliana and Capsella rubella

Ya-Long Guo, Marco Todesco, Jörg Hagmann, Sandip Das & Detlef Weigel
Capsella rubella is an inbreeding annual forb closely related to Arabidopsis thaliana, a model species widely used for studying natural variation in adaptive traits such as flowering time. Although mutations in dozens of genes can affect flowering of A. thaliana in the laboratory, only a handful of such genes vary in natural populations. Chief among these are FRIGIDA (FRI) and FLOWERING LOCUS C (FLC). Common and rare FRI mutations along with rare FLC mutations explain...

Data from: Challenges and strategies in transcriptome assembly and differential gene expression quantification. A comprehensive in silico assessment of RNA-seq experiments.

Nagarjun Vijay, Jelmer W. Poelstra, Axel Künstner & Jochen B. W. Wolf
Transcriptome Shotgun Sequencing (RNA-seq) has been readily embraced by geneticists and molecular ecologists alike. As with all high-throughput technologies, it is critical to understand which analytic strategies are best suited and which parameters may bias the interpretation of the data. Here we use a comprehensive simulation approach to explore how various features of the transcriptome (complexity, degree of polymorphism π, alternative splicing), technological processing (sequencing error ε, library normalization) and bioinformatic workflow (de novo vs....

Registration Year

  • 2012

Resource Types

  • Dataset


  • Max Planck Institute for Developmental Biology
  • University of Minnesota
  • Chinese Academy of Sciences
  • University of Maryland, College Park
  • Uppsala University
  • Oberlin College
  • Michigan State University