Evidence map›Paper›PMID 28985712›Full record

ArticleBMC bioinformatics2017

An integrative approach to predicting the functional effects of small indels in non-coding regions of the human genome.

Michael Ferlaino, Mark F Rogers, Hashem A Shihab, Matthew Mort, David N Cooper, Tom R Gaunt, Colin Campbell

Open access · goldAbstract 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 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed
1.8field-weighted citation impact, top 15% of its field
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

33 citing papers in PubMed, 79 citations in OpenAlex.

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  13. Evaluating the Performance ofCombinatorial chemistry & high throughput screening · 2025
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  20. Predicting pathogenicity from non-coding mutations.Nature biomedical engineering · 2023
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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

7 authors at 3 institutions in 1 country.

Michael FerlainoBig Data Institute, University of Oxford, Oxford, OX3 7LF, UK. michael.ferlaino@bdi.ox.ac.uk.ORCID http://orcid.org/0000-0003-1812-9286
Mark F RogersIntelligent Systems Laboratory, University of Bristol, Bristol, BS8 1UB, UK.
Hashem A ShihabMRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK.
Matthew MortInstitute of Medical Genetics, Cardiff University, Cardiff, CF14 4XN, UK.
David N CooperInstitute of Medical Genetics, Cardiff University, Cardiff, CF14 4XN, UK.
Tom R GauntMRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK.
Colin CampbellIntelligent Systems Laboratory, University of Bristol, Bristol, BS8 1UB, UK.
University of Bristol · GBCardiff University · GBOpen Data Institute · GB

Funding

Medical Research Council G1000427Medical Research Council MC_UU_12013/8Medical Research Council MR/M01326X/1
6 · The paper itself

Abstract

backgroundSmall insertions and deletions (indels) have a significant influence in human disease and, in terms of frequency, they are second only to single nucleotide variants as pathogenic mutations. As the majority of mutations associated with complex traits are located outside the exome, it is crucial to investigate the potential pathogenic impact of indels in non-coding regions of the human genome.

resultsWe present FATHMM-indel, an integrative approach to predict the functional effect, pathogenic or neutral, of indels in non-coding regions of the human genome. Our method exploits various genomic annotations in addition to sequence data. When validated on benchmark data, FATHMM-indel significantly outperforms CADD and GAVIN, state of the art models in assessing the pathogenic impact of non-coding variants. FATHMM-indel is available via a web server at indels.biocompute.org.uk.

conclusionsFATHMM-indel can accurately predict the functional impact and prioritise small indels throughout the whole non-coding genome.

Indexed as

Genome, HumanComputational BiologyDNA, IntergenicGenetics, PopulationHumansINDEL MutationPhenotypeReproducibility of ResultsROC CurveSoftwareDNA, IntergenicIndelsNon-coding genomeSupport vector machinesVariant prioritisation

Identifiers

PMID28985712
PMCPMC5955213
OpenAlexW2762443459

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.