Ferlaino Michael, Rogers Mark F, Shihab Hashem A, Mort Matthew, Cooper David N, Gaunt Tom R, Campbell Colin
Big Data Institute, University of Oxford, Oxford, OX3 7LF, UK.
Nuffield Department of Obstetrics and Gynaecology, University of Oxford, Oxford, OX3 9DU, UK.
BMC Bioinformatics. 2017 Oct 6;18(1):442. doi: 10.1186/s12859-017-1862-y.
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.
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.
FATHMM-indel can accurately predict the functional impact and prioritise small indels throughout the whole non-coding genome.
小片段插入和缺失(indels)对人类疾病有重大影响,就发生频率而言,它们作为致病突变仅次于单核苷酸变异。由于与复杂性状相关的大多数突变位于外显子组之外,因此研究人类基因组非编码区域中indels的潜在致病影响至关重要。
我们提出了FATHMM-indel,这是一种综合方法,用于预测人类基因组非编码区域中indels的功能效应(致病或中性)。我们的方法除了利用序列数据外,还利用了各种基因组注释。在基准数据上进行验证时,FATHMM-indel显著优于CADD和GAVIN,这两种是评估非编码变异致病影响的最先进模型。可通过indels.biocompute.org.uk的网络服务器获取FATHMM-indel。
FATHMM-indel可以准确预测功能影响,并对整个非编码基因组中的小indels进行优先级排序。