HIPred: An integrative approach to predicting haploinsufficient genes

Bioinformatics

Volumn 33, Issue 12, 2017, Pages 1751-1757

  Shihab, Hashem A ^a   Rogers, Mark F ^a   Campbell, Colin ^a   Gaunt, Tom R ^a  

^a UNIVERSITY OF BRISTOL   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

HISTONE;

CHROMATIN; DNA SEQUENCE; GENETIC EPIGENESIS; GENETICS; GENOMICS; HAPLOINSUFFICIENCY; HUMAN; HUMAN GENOME; MACHINE LEARNING; METABOLISM; PROCEDURES; PROTEIN PROTEIN INTERACTION; SEQUENCE ANALYSIS;

CHROMATIN; EPIGENESIS, GENETIC; GENOME, HUMAN; GENOMICS; HAPLOINSUFFICIENCY; HISTONES; HUMANS; MACHINE LEARNING; PROTEIN INTERACTION MAPS; SEQUENCE ANALYSIS, DNA; SEQUENCE ANALYSIS, RNA;

EID: 85021353830     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btx028     Document Type: Article

References (28)

1. Campbell, C. and Ying, Y. (2011) Learning with support vector machines. Synth. Lect. Artif. Intell. Mach. Learn., 5, 1-95
2. Chen, T. and Guestrin, C. (2016). Xgboost: A scalable tree boosting system. CoRR. http://arxiv.org/abs/1603.02754
3. Choi, Y. et al. (2012) Predicting the functional effect of amino acid substitutions and indels. PLoS One, 7, e46688
4. Dang, V.T. et al. (2008) Identification of human haploinsufficient genes and their genomic proximity to segmental duplications. Eur. J. Hum. Genet., 16, 1350-1357
5. Huang, N. et al. (2010) Characterising and predicting haploinsufficiency in the human genome. PLoS Genet., 6, e1001154
6. Iossifov, I. et al. (2012) De novo gene disruptions in children on the autistic spectrum. Neuron, 74, 285-299
7. Khurana, E. et al. (2013) Interpretation of genomic variants using a unified biological network approach. PLoS Comput. Biol., 9, e1002886
8. Kircher, M. et al. (2014) A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet., 46, 310-315
9. Lek, M. et al. (2016). Analysis of protein-coding genetic variation in 60, 706 humans. Nature, 536, 285-291
10. MacArthur, D.G. et al. (2012) A systematic survey of loss-of-function variants in human protein-coding genes. Science, 335, 823-828
11. Neale, B.M. et al. (2012) Patterns and rates of exonic de novo mutations in autism spectrum disorders. Nature, 485, 242-245
12. Ng, P.C. et al. (2008) Genetic variation in an individual human exome. PLoS Genet., 4, e1000160
13. O'Roak, B.J. et al. (2012) Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations. Nature, 485, 246-250
14. Pedregosa, F. et al. (2011) Scikit-learn: machine learning in python. J. Mach. Learn. Res., 12, 2825-2830
15. Pelak, K. et al. (2010) The characterization of twenty sequenced human genomes. PLoS Genet., 6, e1001111
16. Petrovski, S. et al. (2013) Genic intolerance to functional variation and the interpretation of personal genomes. PLoS Genet., 9, e1003709
17. Rackham, O.J.L. et al. (2014) EvoTol: a protein-sequence based evolutionary intolerance framework for disease-gene prioritization. Nucleic Acids Res., 43, e33
18. Ritchie, G.R.S. et al. (2014) Functional annotation of noncoding sequence variants. Nat. Methods, 11, 294-296
19. Roadmap Epigenomics Consortium. et al. (2015) Integrative analysis of 111 reference human epigenomes. Nature, 518, 317-330
20. Rogers, M. et al. (2015). Sequential data selection for predicting the pathogenic effects of sequence variation. In: IEEE International Conference on Bioinformatics and Biomedicine, pp. 639-644
21. Sanders, S.J. et al. (2012) De novo mutations revealed by whole-exome sequencing are strongly associated with autism. Nature, 485, 237-241
22. Shihab, H.A. et al. (2013) Predicting the functional, molecular, and phenotypic consequences of amino acid substitutions using Hidden Markov Models. Hum. Mutat., 34, 57-65
23. Shihab, H.A. et al. (2015) An integrative approach to predicting the functional effects of non-coding and coding sequence variation. Bioinformatics, 31, 1536-1543
24. Sonnenburg, S. et al. (2010) The SHOGUN machine learning toolbox. J. Mach. Learn. Res., 11, 1799-1802
25. Steinberg, J. et al. (2015) Haploinsufficiency predictions without study bias. Nucleic Acids Res., 43, e101
26. The 1000 Genomes Project Consortium. (2012) An integrated map of genetic variation from 1, 092 human genomes. Nature, 491, 56-65
27. The ENCODE Project Consortium. (2012) An integrated encyclopedia of DNA elements in the human genome. Nature, 489, 57-74
28. Veitia, R.A. and Birchler, J.A. (2010) Dominance and gene dosage balance in health and disease: why levels matter!. J. Pathol., 220, 174-185