Improved annotations allow for better understanding and interpretation of genomic and transcriptomic experiments, including analyzing the functional implications of insertions, deletions, and mutations, inferring the function of understudied genes, and determining functional changes resulting from differential expression studies.
Thus, a functionally characterized lncRNA (reference) can be used to infer the function of others that are functionally unknown (target), based on shared structural motifs.
Thus, a functionally characterized lncRNA (here called reference) can be used to infer the function of other lncRNAs that are functionally unknown (here called target), based on shared structural motifs.
The most straightforward approach of computational functional annotation is to infer the function of a gene based on the functions of genes returned by a database search for similar genes.
Most functional annotation is predicted using sequence homology-based methods that infer the function of uncharacterized protein sequences based on their similarity to characterized templates.
Focus on co-expressed modules may actually improve the feasibility of inferring the function of co-expressed genes based on observed expression patterns together with standard functional information inferred from the subset of conserved annotated genes with identifiable orthologs from model systems.
To elucidate functional divergence among the Gsdma-related genes in mice, and to infer the function of the human and rat orthologs, we examined in vivo function of mouse Gsdma by generating Gsdma knockout mice and transgenic mice that overexpress wild-type Gsdma or Gsdma harboring a point mutation (Alanine339Threonine).
The researchers then extrapolated this variant annotation from BRCA1 to infer the function of mutations in two related cancer genes.
Combined with advanced data analysis and mathematical modeling, such temporal datasets are an invaluable source of information for inferring the function of individual proteins in molecular assemblies.
Watching for changes in the memory registers the researchers call this "buffer deconstruction"– allowed them to derive the structure of the botnet communications and infer the function of the various components of each command.
