Computer programs for eukaryotic gene prediction

Makarov, Vladimir

doi:10.1093/bib/3.2.195

Cited by 26 publications

(11 citation statements)

References 10 publications

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“…Also, such information is useful for tuning up the current gene prediction programs. Although remarkable improvement has been made, the currently used gene prediction programs are basically the coding-exon prediction programs and they are still unable to detect the exact ends of the boundaries, which are flanked by the 5'-end or the 3'-end untranslated regions of the transcripts [57]. If the exact boundaries of the transcripts are identified, it would be extremely helpful to complement the current gene prediction methods.…”

Section: For Further Enriching the 5'-end Data~ Construction Of The Tmentioning

confidence: 99%

Functional Analyses of the Human Genome Based on Large-Scale Fulllength cDNA Resources

Suzuki¹,

Sugano²

2005

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Newly developed full-length cDNA library technologies have enabled us to generate an unprecedented scale of cDNA resources with respect to both the forms of the physical cDNA clones and cDNA sequence information. Detailed annotations were attached to each of the cDNAs both computationally and manually and several integrated databases on the cDNA information were launched in a publicly accessible manner. Now, taking advantage of the physical cDNA clone resources, which are thought to cover most of the entire protein-coding genes in humans and mice non-redundantly, efficient high-throughput approaches, collectively called functional genomics approaches, are underway for characterizing biological functions of the encoded proteins from various points of view. In addition, it has become clear that the fulllength cDNA resources are also useful for determining the precise genomic positions of the transcriptional start sites. The positional information of the TSSs allowed us to identify and analyze the adjacent promoter regions as putative transcriptional regulatory regions. Moreover, several very recently developed methods, combining full-length cDNA technologies with SAGE technologies, have enabled further high-throughput identification of the TSSs. Based on further expansion of the full-length cDNA data, attempts have been started towards a comprehensive understanding of what genomic elements, including genic regions and non-genic regions such as promoters, would bring about what biological consequences in what cellular contexts. Rapid compilation of genomic sequence data as well as multifaceted use of the full-length cDNA resources will shortly lay a firm foundation for a global understanding of the complex molecular biological systems which convert the information in the genomic DNA into a living cell.

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Section: For Further Enriching the 5'-end Data~ Construction Of The Tmentioning

confidence: 99%

Functional Analyses of the Human Genome Based on Large-Scale Fulllength cDNA Resources

Suzuki¹,

Sugano²

2005

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“…The multi-gene model, which describes the complex gene structure of any eukaryotic DNA sequence containing many genes, is con-S structed by combing the models of single-exon gene, multi-exon gene and intergenic region. In GeneKey(1.0), the multi-gene model S Genome is defined as (6) Genome…”

Section: Multilevel Modeling Of Gene Structuresmentioning

confidence: 99%

Prediction of eukaryotic gene structures based on multilevel optimization

Zhou¹

2004

Chinese Sci Bull

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Computational gene structure prediction, which is valuable for finding new genes and understanding the composition of genomes, plays a very important role in various kinds of genome projects. For eukaryotic gene structures, however, the prediction accuracy of existing methods is still limited. This paper presents a method of predicting eukaryotic gene structures based on multilevel optimization. The complicated problem of predicting gene structure in eukaryotic DNA sequence containing multiple genes can be decomposed into a series of sub-problems at several levels with decreasing complexity, including the gene level (single-exon gene, multi-exon gene), the element level (exon, intron, etc.), and the feature level (functional site signals, codon usage preference, etc.). On the basis of this decomposition, a multilevel model for the prediction of complex gene structures is created by a multilevel optimization process, in which the models dealing with sub-problems at low complexity level are first optimized respectively, and then optimally combined together to form models for those sub-problems at higher complexity level. Based on the multilevel model, a dynamic programming algorithm is designed to search for optimal gene structures from DNA sequences, and a new program GeneKey (1.0) for the prediction of eukaryotic gene structures is developed. Testing results with widely used datasets demonstrate that the prediction accuracies of GeneKey (1.0) at the nucleotide level, exon level and gene level are all higher than that of the well known program GENSCAN. A web server of GeneKey(1.0) is available at http://infosci.hust.edu.cn

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“…Long stretches of intergenic DNA are present in eukaryotic genomes and their coding sequences are interrupted by non-coding introns. Although computational gene prediction methods help in seeking partial knowledge of real genes [2,3], gene finding programs are still unable to provide gene discovery with desired correctness [4]. The problem lies at the core of program development which is essentially based on experimentally verified intron-exon boundary dataset of particular organism and developed using specific patterns of matching nucleotides correlated with aligned protein coding regions.…”

Section: Introductionmentioning

confidence: 99%

A Method for Construction, Cloning and Expression of Intron-Less Gene from Unannotated Genomic DNA

et al. 2008

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The present century has witnessed an unprecedented rise in genome sequences owing to various genome-sequencing programs. However, the same has not been replicated with cDNA or expressed sequence tags (ESTs). Hence, prediction of protein coding sequence of genes from this enormous collection of genomic sequences presents a significant challenge. While robust high throughput methods of cloning and expression could be used to meet protein requirements, lack of intron information creates a bottleneck. Computational programs designed for recognizing intron-exon boundaries for a particular organism or group of organisms have their own limitations. Keeping this in view, we describe here a method for construction of intron-less gene from genomic DNA in the absence of cDNA/EST information and organism-specific gene prediction program. The method outlined is a sequential application of bioinformatics to predict correct intron-exon boundaries and splicing by overlap extension PCR for spliced gene synthesis. The gene construct so obtained can then be cloned for protein expression. The method is simple and can be used for any eukaryotic gene expression.

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Computer programs for eukaryotic gene prediction

Cited by 26 publications

References 10 publications

Functional Analyses of the Human Genome Based on Large-Scale Fulllength cDNA Resources

Functional Analyses of the Human Genome Based on Large-Scale Fulllength cDNA Resources

Prediction of eukaryotic gene structures based on multilevel optimization

A Method for Construction, Cloning and Expression of Intron-Less Gene from Unannotated Genomic DNA

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