Assessment of the predictive accuracy of five in silico prediction tools, alone or in combination, and two metaservers to classify long QT syndrome gene mutations

Table 1 In silico prediction tools and metaservers used in the current study

Program	Type	Incorporated programs (Metaserver only)	Type (Metaserver only)	URL
PolyPhen-2, version 2.2.2 [17]	Protein sequence and structure			http://genetics.bwh.harvard.edu/pph2/
SNPs&GO [18]	Supervised learning (support vector machine)			http://snps.biofold.org/snps-and-go/snps-and-go.html
SIFT, version 5.2.0 [14,15]	Sequence and evolutionary conservation			http://siftdna.org/www/Extended_SIFT_chr_coords_submit.html
PROVEAN, version 1.1 [16]	Sequence and evolutionary conservation			http://provean.jcvi.org/index.php
SNAP [19]	Supervised learning (neural networks)			https://www.rostlab.org/services/SNAP/
Meta-SNP [30]	Metaserver	PANTHER	Sequence and evolutionary conservation	http://snps.biofold.org/meta-snp/
		PhD-SNP	Supervised-learning (support vector machines)
		SIFT	Sequence and evolutionary conservation
		SNAP	Supervised learning (neural networks)
PredictSNP [31]	Metaserver	MAPP	Sequence and evolutionary conservation	http://loschmidt.chemi.muni.cz/predictsnp/
		PhD-SNP	Supervised-learning (support vector machines)
		PolyPhen-1	Protein sequence and structure
		PolyPhen-2	Protein sequence and structure
		SIFT	Sequence and evolutionary conservation
		SNAP	Supervised learning (neural networks)

ISSN: 1471-2350