NDLI: Positional characterisation of false positives from computational prediction of human splice sites

Content Provider	PubMed Central
Author	Thanaraj, T. A.
Copyright Year	2000
Abstract	The performance of computational tools that can predict human splice sites are reviewed using a test set of EST-confirmed splice sites. The programs (namely HMMgene, NetGene2, HSPL, NNSPLICE, SpliceView and GeneID-3) differ from one another in the degree of discriminatory information used for prediction. The results indicate that, as expected, HMMgene and NetGene2 (which use global as well as local coding information and splice signals) followed by HSPL (which uses local coding information and splice signals) performed better than the other three programs (which use only splice signals). For the former three programs, one in every three false positive splice sites was predicted in the vicinity of true splice sites while only one in every 12 was expected to occur in such a region by chance. The persistence of this observation for programs (namely FEXH, GRAIL2, MZEF, GeneID-3, HMMgene and GENSCAN) that can predict all the potential exons (including optimal and sub-optimal) was assessed. In a high proportion (>50%) of the partially correct predicted exons, the incorrect exon ends were located in the vicinity of the real splice sites. Analysis of the distribution of proximal false positives indicated that the splice signals used by the algorithms are not strong enough to discriminate particularly those false predictions that occur within ± 25 nt around the real sites. It is therefore suggested that specialised statistics that can discriminate real splice sites from proximal false positives be incorporated in gene prediction programs.
Starting Page	744
File Format	PDF
ISSN	13624962
e-ISSN	13624962
Journal	Nucleic Acids Research
Issue Number	3
Volume Number	28
Language	English
Publisher	Oxford University Press
Publisher Date	2000-02-01
Access Restriction	Open
Rights Holder	Oxford University Press
Subject Keyword	Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Genetics

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