GTB – an online genome tolerance browser

Background Accurate methods capable of predicting the impact of single nucleotide variants (SNVs) are assuming ever increasing importance. There exists a plethora of in silico algorithms designed to help identify and prioritize SNVs across the human genome for further investigation. However, no tool...

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Bibliographic Details
Published in:BMC bioinformatics Vol. 18; no. 1; p. 20
Main Authors: Shihab, Hashem A., Rogers, Mark F., Ferlaino, Michael, Campbell, Colin, Gaunt, Tom R.
Format: Journal Article
Language:English
Published: London BioMed Central 06.01.2017
BioMed Central Ltd
Springer Nature B.V
Subjects:
Algorithms
Annotations
Binding sites
Bioinformatics
Biomedical and Life Sciences
Cancer
Comparative genomics
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Databases, Genetic
Funding
Gene mutation
Genome, Human
Genomes
Homeodomain Proteins - genetics
Homeodomain Proteins - metabolism
Humans
Information management
Internet
Life Sciences
Microarrays
Models, Theoretical
Mutation
Neoplasms - diagnosis
Neoplasms - genetics
Receptors, LDL - genetics
Receptors, LDL - metabolism
Software
Visualization
Web Browser
Web browsers
Pathogenicity prediction
Genome browser
Genome tolerance
Mutation
Variant effect prediction
SNVs
Prediction algorithm
ISSN:1471-2105, 1471-2105
Online Access:Get full text
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Summary:Background Accurate methods capable of predicting the impact of single nucleotide variants (SNVs) are assuming ever increasing importance. There exists a plethora of in silico algorithms designed to help identify and prioritize SNVs across the human genome for further investigation. However, no tool exists to visualize the predicted tolerance of the genome to mutation, or the similarities between these methods. Results We present the Genome Tolerance Browser (GTB, http://gtb.biocompute.org.uk ): an online genome browser for visualizing the predicted tolerance of the genome to mutation. The server summarizes several in silico prediction algorithms and conservation scores: including 13 genome-wide prediction algorithms and conservation scores, 12 non-synonymous prediction algorithms and four cancer-specific algorithms. Conclusion The GTB enables users to visualize the similarities and differences between several prediction algorithms and to upload their own data as additional tracks; thereby facilitating the rapid identification of potential regions of interest.
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ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-016-1436-4