期刊
BIOINFORMATICS
卷 31, 期 10, 页码 1536-1543出版社
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btv009
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资金
- Medical Research Council [MC_UU_12013/8, G1000427/1]
- Engineering and Physical Sciences Research Council [EP/K008250/1]
- Biotechnology and Biological Sciences Research Council (BBSRC) [BB/G022771]
- Biotechnology and Biological Sciences Research Council [BB/L018543/1, BB/G022771/1] Funding Source: researchfish
- Engineering and Physical Sciences Research Council [EP/M01715X/1, EP/K008250/1] Funding Source: researchfish
- Medical Research Council [MC_UU_12013/8] Funding Source: researchfish
- BBSRC [BB/G022771/1, BB/L018543/1] Funding Source: UKRI
- EPSRC [EP/M01715X/1, EP/K008250/1] Funding Source: UKRI
- MRC [MC_UU_12013/8] Funding Source: UKRI
Motivation: Technological advances have enabled the identification of an increasingly large spectrum of single nucleotide variants within the human genome, many of which may be associated with monogenic disease or complex traits. Here, we propose an integrative approach, named FATHMM-MKL, to predict the functional consequences of both coding and non-coding sequence variants. Our method utilizes various genomic annotations, which have recently become available, and learns to weight the significance of each component annotation source. Results: We show that our method outperforms current state-of-the-art algorithms, CADD and GWAVA, when predicting the functional consequences of non-coding variants. In addition, FATHMM-MKL is comparable to the best of these algorithms when predicting the impact of coding variants. The method includes a confidence measure to rank order predictions.
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