GTB - an online genome tolerance browser.

Hashem A Shihab, Mark F Rogers, Michael Ferlaino, Colin Campbell, Tom R Gaunt

Abstract read

In one paragraph

Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it

0cells of the map it votes in

3citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

Prediction of driver variants in the cancer genome via machine learning methodologies.Briefings in bioinformatics · 2021
Review
Recognition of the polycistronic nature of human genes is critical to understanding the genotype-phenotype relationship.Genome research · 2018
Review
FATHMM-XF: accurate prediction of pathogenic point mutations via extended features.Bioinformatics (Oxford, England) · 2018
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Hashem A ShihabMRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, BS8 2BN, UK.

Mark F RogersIntelligent Systems Laboratory, University of Bristol, Bristol, BS8 1UB, UK.

Michael FerlainoIntelligent Systems Laboratory, University of Bristol, Bristol, BS8 1UB, UK.

Colin CampbellIntelligent Systems Laboratory, University of Bristol, Bristol, BS8 1UB, UK.

Tom R GauntMRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, BS8 2BN, UK. tom.gaunt@bristol.ac.uk.

Funding

Medical Research Council G1000427Medical Research Council MC_UU_12013/8

6 · The paper itself

Abstract

backgroundAccurate 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.

resultsWe 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.

conclusionThe 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.

Indexed as

Genome, HumanInternetWeb BrowserAlgorithmsDatabases, GeneticHomeodomain ProteinsHumansModels, TheoreticalNeoplasmsReceptors, LDLHomeodomain ProteinsHOXA5 protein, humanLDLR protein, humanReceptors, LDLGenome browserGenome toleranceMutationPathogenicity predictionPrediction algorithmSNVsVariant effect prediction

Identifiers

PMID28061747

DOI10.1186/s12859-016-1436-4

PMCPMC5219737

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LicenceCC BY

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.