title: Using bioinformatics to predict the functional impact of SNVs.
abstract: BACKGROUND
  The past decade has seen the introduction of fast and relatively inexpensive methods to detect genetic variation across the genome and exponential growth in the number of known single nucleotide variants (SNVs). There is increasing interest in bioinformatics approaches to identify variants that are functionally important from millions of candidate variants. Here, we describe the essential components of bioinformatics tools that predict functional SNVs.
  
  
  RESULTS
  Bioinformatics tools have great potential to identify functional SNVs, but the black box nature of many tools can be a pitfall for researchers. Understanding the underlying methods, assumptions and biases of these tools is essential to their intelligent application.
authors.affiliation: Department of Molecular Cell and Developmental Biology, University of California, Santa Cruz, CA, USA.
authors.forename: Melissa S
authors.initials: MS
authors.lastname: Cline
authors.forename: Rachel
authors.initials: R
authors.lastname: Karchin
mesh_terms: D000465:Algorithms; D019943:Amino Acid Substitution; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D019143:Evolution, Molecular; D035683:MicroRNAs / Q000235:genetics / Q000378:metabolism; D020641:Polymorphism, Single Nucleotide; D011499:Protein Processing, Post-Translational; D012326:RNA Splicing; D020539:Sequence Analysis, Protein; D013329:Structure-Activity Relationship; D014158:Transcription, Genetic
chemical_list: D035683:MicroRNAs