Journal
BIOINFORMATICS
Volume 27, Issue 4, Pages 441-448Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btq695
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Funding
- National Human Genome Research Institute [5P41HG002371-09, 5U41HG004568-02]
- Muscular Dystrophy Association [135140]
- National Science Foundation [DBI 0845275]
- Direct For Biological Sciences
- Div Of Biological Infrastructure [0845275] Funding Source: National Science Foundation
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Motivation: 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 bionformatics 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.
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