Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Algorithms (1)
- Alleles (1)
- Bioinformatics (1)
- Chemistry (1)
- Computational biology (1)
-
- Computer appl. in life sciences (1)
- Data mining and knowledge discovery (1)
- Enzymology (1)
- Fungal (1)
- Fungal proteins (1)
- Gene expression regulation (1)
- Gene knockout techniques (1)
- Genes (1)
- Genetics (1)
- Metabolism (1)
- Mutation (1)
- Neurospora crassa (1)
- P21-activated kinases (1)
- Physiology (1)
- Protein-serine-threonine kinases (1)
- Signal transduction (1)
Articles 1 - 2 of 2
Full-Text Articles in Biology
Global Analysis Of Serine-Threonine Protein Kinase Genes In Neurospora Crassa, Gyungsoon Park, Jacqueline A. Servin, Gloria E. Turner, Lorena Altamirano
Global Analysis Of Serine-Threonine Protein Kinase Genes In Neurospora Crassa, Gyungsoon Park, Jacqueline A. Servin, Gloria E. Turner, Lorena Altamirano
Dartmouth Scholarship
Serine/threonine (S/T) protein kinases are crucial components of diverse signaling pathways in eukaryotes, including the model filamentous fungus Neurospora crassa. In order to assess the importance of S/T kinases to Neurospora biology, we embarked on a global analysis of 86 S/T kinase genes in Neurospora. We were able to isolate viable mutants for 77 of the 86 kinase genes. Of these, 57% exhibited at least one growth or developmental phenotype, with a relatively large fraction (40%) possessing a defect in more than one trait. S/T kinase knockouts were subjected to chemical screening using a panel of eight chemical treatments, with …
Evolving Hard Problems: Generating Human Genetics Datasets With A Complex Etiology, Daniel S Himmelstein, Casey S Greene, Jason H Moore
Evolving Hard Problems: Generating Human Genetics Datasets With A Complex Etiology, Daniel S Himmelstein, Casey S Greene, Jason H Moore
Dartmouth Scholarship
BackgroundA goal of human genetics is to discover genetic factors that influence individuals' susceptibility to common diseases. Most common diseases are thought to result from the joint failure of two or more interacting components instead of single component failures. This greatly complicates both the task of selecting informative genetic variants and the task of modeling interactions between them. We and others have previously developed algorithms to detect and model the relationships between these genetic factors and disease. Previously these methods have been evaluated with datasets simulated according to pre-defined genetic models.