GTB – An Online Genome Tolerance Browser

Hashem Shihab; Mark Rogers; Michael Ferlaino; Colin Campbell; Tom Gaunt

doi:10.1186/s12859-016-1436-4

GTB – An Online Genome Tolerance Browser

Hashem Shihab, Mark Rogers, Michael Ferlaino, Colin Campbell, Tom Gaunt

Research output: Contribution to journal › Article (Academic Journal) › peer-review

4 Citations (Scopus)

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Abstract

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): 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 4 cancerspecific 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.

Original language	English
Number of pages	6
Journal	BMC Bioinformatics
Volume	18
Issue number	20
DOIs	https://doi.org/10.1186/s12859-016-1436-4
Publication status	Published - 6 Jan 2017

Keywords

SNVs
Mutation
Pathogenicity Prediction
Prediction Algorithm
Variant Effect Prediction
Genome Browser
Genome Tolerance

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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Novel Methodology for Predicting the Functional Effects of Genetic Variation
Campbell, I. C. G. (Principal Investigator)
1/06/15 → 31/05/18
Project: Research
An Active Learning Approach to Network Inference
Campbell, I. C. G. (Principal Investigator)
1/07/13 → 30/06/16
Project: Research
IEU Theme 2
Flach, P. A. (Principal Investigator), Gaunt, T. R. (Principal Investigator) & Gaunt, T. R. (Principal Investigator)
1/06/13 → 31/03/18
Project: Research

Cite this

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title = "GTB – An Online Genome Tolerance Browser",

abstract = "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): 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 4 cancerspecific 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.",

keywords = "SNVs, Mutation, Pathogenicity Prediction, Prediction Algorithm, Variant Effect Prediction, Genome Browser, Genome Tolerance",

author = "Hashem Shihab and Mark Rogers and Michael Ferlaino and Colin Campbell and Tom Gaunt",

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month = jan,

day = "6",

doi = "10.1186/s12859-016-1436-4",

language = "English",

volume = "18",

journal = "BMC Bioinformatics",

issn = "1471-2105",

publisher = "BioMed Central",

number = "20",

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T1 - GTB – An Online Genome Tolerance Browser

AU - Shihab, Hashem

AU - Rogers, Mark

AU - Ferlaino, Michael

AU - Campbell, Colin

AU - Gaunt, Tom

PY - 2017/1/6

Y1 - 2017/1/6

N2 - 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): 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 4 cancerspecific 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.

AB - 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): 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 4 cancerspecific 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.

KW - SNVs

KW - Mutation

KW - Pathogenicity Prediction

KW - Prediction Algorithm

KW - Variant Effect Prediction

KW - Genome Browser

KW - Genome Tolerance

U2 - 10.1186/s12859-016-1436-4

DO - 10.1186/s12859-016-1436-4

M3 - Article (Academic Journal)

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SN - 1471-2105

VL - 18

JO - BMC Bioinformatics

JF - BMC Bioinformatics

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ER -

GTB – An Online Genome Tolerance Browser

Abstract

Keywords

UN SDGs

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Projects

Novel Methodology for Predicting the Functional Effects of Genetic Variation

An Active Learning Approach to Network Inference

IEU Theme 2

Cite this