4.7 Article

FATHMM-XF: accurate prediction of pathogenic point mutations via extended features

Journal

BIOINFORMATICS
Volume 34, Issue 3, Pages 511-513

Publisher

OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btx536

Keywords

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Funding

  1. Engineering and Physical Sciences Research Council (EPSRC) [EP/M01715X/1, EP/K008250/1]
  2. Medical Research Council Integrative Epidemiology Unit (MRC IEU) [MC UU 12013/8]
  3. Qiagen Inc.
  4. Cardiff University
  5. EPSRC [EP/K008250/1, EP/M01715X/1] Funding Source: UKRI
  6. MRC [G1000427, MC_UU_12013/8, MC_UU_00011/4] Funding Source: UKRI
  7. Engineering and Physical Sciences Research Council [EP/K008250/1, EP/M01715X/1] Funding Source: researchfish
  8. Medical Research Council [MC_UU_00011/4, G1000427, MC_UU_12013/8] Funding Source: researchfish

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aSummary: We present FATHMM-XF, a method for predicting pathogenic point mutations in the human genome. Drawing on an extensive feature set, FATHMM-XF outperforms competitors on benchmark tests, particularly in non-coding regions where the majority of pathogenic mutations are likely to be found. Availability and implementation: The FATHMM-XF web server is available at http://fathmm.biocompute.org.uk/fathmm-xf/, and as tracks on the Genome Tolerance Browser: http://gtb.biocompute.org.uk. Predictions are provided for human genome version GRCh37/hg19. The data used for this project can be downloaded from: http://fathmm.biocompute.org.uk/fathmm-xf/ Contact: mark. rogers@bristol.ac.uk or c.campbell@bristol.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

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