Genviewer: A Computing Tool for Protein-Coding Regions Prediction in Nucleotide Sequences

“…This is the approach taken, in the particular case of single gene structures, in the GenelD (Guigó et al, 1992), GenViewer (Milanesi et al, 1993), GAP3 (Xu et al, 1994), and FGENEH (Solovyev et al, 1994) programs. In all these cases, gene assembly is separated from exon construction, and the goal is explicitly stated of assembling the gene maximizing a function of the scores of the assembled exons.…”

Section: Discussionmentioning

confidence: 99%

Assembling Genes from Predicted Exons in Linear Time with Dynamic Programming

Guigó¹

1998

Journal of Computational Biology

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“…Hexamer frequency is higher in protein coding regions than in non-protein coding regions and is the most discriminative feature between coding and non-coding regions. It is one of the main features used in SORFIND [12], Geneview2 [23] and MZEF [24]. Statistical models such as HMM and ANN can be used to derive intrinsic features.…”

Section: Fig 1 Gene Finding Approachs [4]mentioning

confidence: 99%

Computational Approaches for Gene Prediction: A Comparative Survey

Al-Turaiki

Mathkour

Touir

et al. 2011

Communications in Computer and Information Science

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Abstract. Accurate gene structure prediction plays a fundamental role in functional annotation of genes. The main focus of gene prediction methods is to find patterns in long DNA sequences that indicate the presence of genes. The problem of gene prediction is an important problem in the field of bioinformatics. With the explosive growth of genomic information there is a need for computational approaches that facilitate gene location, structure and functional prediction. In this paper, we survey various computational approaches for gene predictions.

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Genviewer: A Computing Tool for Protein-Coding Regions Prediction in Nucleotide Sequences

Cited by 18 publications

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The gene identification problem: An overview for developers

The gene identification problem: An overview for developers

Assembling Genes from Predicted Exons in Linear Time with Dynamic Programming

Computational Approaches for Gene Prediction: A Comparative Survey

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