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BMC Bioinformatics. 2017 Oct 6;18(1):442. doi: 10.1186/s12859-017-1862-y.

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

Author information

1
Big Data Institute, University of Oxford, Oxford, OX3 7LF, UK. michael.ferlaino@bdi.ox.ac.uk.
2
Nuffield Department of Obstetrics and Gynaecology, University of Oxford, Oxford, OX3 9DU, UK. michael.ferlaino@bdi.ox.ac.uk.
3
Intelligent Systems Laboratory, University of Bristol, Bristol, BS8 1UB, UK.
4
MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK.
5
Institute of Medical Genetics, Cardiff University, Cardiff, CF14 4XN, UK.

Abstract

BACKGROUND:

Small 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.

RESULTS:

We 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.

CONCLUSIONS:

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

KEYWORDS:

Indels; Non-coding genome; Support vector machines; Variant prioritisation

PMID:
28985712
PMCID:
PMC5955213
DOI:
10.1186/s12859-017-1862-y
[Indexed for MEDLINE]
Free PMC Article

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