Finding Our Way through Phenotypes

By A. Deans, Suzanna Lewis, E. Huala, Salvatore S. Anzaldo, Michael Ashburner, James P. Balhoff, D. Blackburn, Judith A. Blake, J. Burleigh, B. Chanet and 64 more. PLoS Biology, 2015

303 citations recorded by the source corpus.

Imagine if we could compute across phenotype data as easily as genomic data; this article calls for efforts to realize this vision and discusses the potential benefits.

Subfields: Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/9948f24223e2382e9e0e3cfe4b0ce59cd72ae0bf

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In Molecular Biology

1. Daniela Sciaky
2. Robin J. Johnson
3. Thomas C. Wiegers
4. Carolyn J. Mattingly
5. Allan Peter Davis
6. Richard J. Ladle
7. Francesco de Bello
8. A. Jiménez‐Valverde

Citations in the corpus

• The Comparative Toxicogenomics Database: update 2019 (2018, methodology): “The Inference Network automatically computes a list of genes that have both a directly curated chemical–gene interaction in CTD and are independently annotated to the same GO term by external databases (36,37).”
• The Genetic Basis of Mendelian Phenotypes: Discoveries, Challenges, and Opportunities. (2015, unknown): “Therefore, to completely enumerate ‘‘all’’ human Mendelian phenotypes, it will be necessary to consider a more comprehensive span of environmental conditions and develop more-sophisticated tools to evaluate phenotype.(49) Studies in mice with engineered loss-of-function (LOF) mutations suggest that ”
• Seven Shortfalls that Beset Large-Scale Knowledge of Biodiversity (2015, background): “Automatic analysis of phenotypes (Deans et al. 2015) and next-generation sequencing combined with bioinformatics (Taberlet et al. 2012) have the potential to identify species from large samples, documenting patterns of trait variation and linking genetic data with particular phenotypes.”