Key Points
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With the recent explosion in the availability of genome data, gene-finding programs have proliferated. However, the accuracy with which genes can be predicted is still far from satisfactory. This review provides background information and surveys the latest developments in gene-prediction programs. It also highlights the problems that face the gene-prediction field and discusses future research goals.
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The main characteristic of a eukaryotic gene is its organization into exons and introns. The 'exon-definition' model explains how the splicing machinery recognizes exons in a sea of intronic DNA. It indicates that an internal exon is initially recognized by a chain of interacting splicing factors that span it. The binding of these factors to pre-mRNA is responsible for the non-random nucleotide patterns that form the molecular basis of all exon-recognition algorithms.
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Correctly identifying the boundaries of a gene is essential when searching for several genes in a large genomic region. It is relatively easy to find internal exons, but many gene-prediction programs fail to identify gene boundaries. Determining the 3′ end of a gene is easier than determining its 5′ end, mainly because of the difficulty of identifying the promoter and transcriptional start-site sequences, and because the 5′ ends of cDNA sequences are often truncated.
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As current gene-prediction programs are biased towards intron-containing genes, many intronless genes might have been missed by such programs. Many false-positive exon predictions have also been caused by pseudogenes. Developing better and more specialized algorithms to recognize them is becoming increasingly important.
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Hidden Markov model (HMM)-based programs can be used to predict multiple genes, partial genes and genes on both strands, all at the same time. These features are essential when annotating genomes or large chunks of sequence data, such as large contigs, in an automated fashion.
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By comparing the genomes of several closely related species, conserved regulatory regions can be identified easily. For these reasons, making use of comparative genomic data is an important future challenge for the gene-prediction field.
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More functional genomics methods for finding genes are desperately needed to improve gene prediction. Only with sufficient mechanistic data can gene prediction be transformed from being statistical to being biological in nature. The field is working towards the ultimate dynamic model that can identify the consecutive exons of a gene, from its 5′ to its 3′ ends, as if they were being co-transcriptionally recognized and spliced.
Abstract
The human genome sequence is the book of our life. Buried in this large volume are our genes, which are scattered as small DNA fragments throughout the genome and comprise a small percentage of the total text. Finding these indistinct 'needles' in a vast genomic 'haystack' can be extremely challenging. In response to this challenge, computational prediction approaches have proliferated in recent years that predict the location and structure of genes. Here, I discuss these approaches and explain why they have become essential for the analyses of newly sequenced genomes.
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References
Claverie, J.-M. Computational methods for the identification of genes in vertebrate genomic sequences. Hum. Mol. Genet. 6, 1735–1744 (1997).
Burge, C. & Karlin, S. Prediction of complete gene structure in human genomic DNA. J. Mol. Biol. 268, 78–94 (1997).In this paper, the popular Genscan gene-prediction algorithm was first reported.
Milanesi, L. & Rogozin, I. B. in Guide to Human Genome Computing 2nd edn (ed. Bishop, M. J.) 215–260 (Academic, New York, 1998).
Krogh, A. in Guide to Human Genome Computing 2nd edn (ed. Bishop, M. J.) 261–274 (Academic, New York, 1998).
Pavy, N. et al. Evaluation of gene prediction software using a genomic data set: application to Arabidopsis thaliana sequences. Bioinformatics 15, 887–899 (1999).
Rogic, S., Mackworth, A. K. & Ouellette, F. B. F. Evaluation of gene-finding programs on mammalian sequences. Genome Res. 11, 817–832 (2001).
Solovyev, V. V. in Current Topics in Computational Molecular Biology (eds Jiang, T., Xu, Y. & Zhang, M. Q.) 201–248 (MIT Press, Cambridge, Massachusetts, 2002).An up-to-date introduction and review on computational gene-prediction methods.
Brent, M. R. Predicting full-length transcripts. Trends Biotechnol. 20, 273–275 (2002).
Zhang, M. Q. Statistical features of human exons and their flanking regions. Hum. Mol. Genet. 7, 919–932 (1998).
Senapathy, P., Shapiro, M. B. & Harris, N. L. Splice junctions, branch point sites, and exons: sequence statistics, identification and application to genome project. Methods Enzymol. 183, 252–278 (1990).A good introduction to the statistical features of splicing signals and exons.
Chen, T. & Zhang, M. Q. POMBE: a fission yeast gene-finding and exon–intron structure prediction system. Yeast 14, 701–710 (1998).
Lim, L. P. & Burge, C. B. A computational analysis of sequence features involved in recognition of short introns. Proc. Natl Acad. Sci. USA 98, 11193–11198 (2001).A systematic study of the sequence features that might define a short intron.
Robberson, B. L., Cote, G. J. & Berget, S. M. Exon definition may facilitate splice site selection in RNAs with multiple exons. Mol. Cell. Biol. 10, 84–94 (1990).
Ripley, B. D. Pattern Recognition and Neural Networks (Cambridge Univ. Press, Cambridge, UK, 1996).
Solovyev, V. V., Salamov, A. A. & Lawrence, C. B. Predicting internal exons by oligonucleotide composition and discriminant analysis of spliceable open reading frames. Nucleic Acids Res. 22, 248–250 (1994).
Pertea, M., Lin, X. & Salzberg, S. L. GeneSplicer: a new computational method for splice site prediction. Nucleic Acids Res. 29, 1185–1190 (2001).
Fickett, J. W. & Tung, C.-S. Assessment of protein coding measures. Nucleic Acids Res. 20, 6441–6450 (1992).This is a comprehensive assessment of protein-coding measures, which are used in many gene-prediction algorithms.
Salzberg, S. L., Delcher, A. L., Kasif, S. & White, O. Microbial gene identification using interpolated Markov models. Nucleic Acids Res. 26, 544–548 (1998).
Bernardi, G. The human genome: organization and evolutionary history. Annu. Rev. Genet. 29, 445–476 (1995).
Zhang, M. Q. Identification of protein coding regions in the human genome based on quadratic discriminant analysis. Proc. Natl Acad. Sci. USA 94, 565–568 (1997).
Uberbacher, E. C. & Mural, R. J. Locating protein coding segments in human DNA sequences by a multiple sensor-neural network approach. Proc. Natl Acad. Sci. USA 88, 11261–11265 (1991).
Graber, J. H., Cantor, C. R., Mohr, S. C. & Smith, T. F. In silico detection of control signals: mRNA 3′-end-processing sequences in diverse species. Proc. Natl Acad. Sci. USA 96, 14055–14060 (1999).
Tabaska, J. E. & Zhang, M. Q. Detection of polyadenylation signals in human DNA sequences. Gene 231, 77–86 (1999).
Tabaska, J. E., Davuluri, R. V. & Zhang, M. Q. Identifying the 3′-terminal exon in human DNA. Bioinformatics 17, 602–607 (2001).
Schell, T., Kulozik, A. E. & Hentze, M. W. Integration of splicing, transport and translation to achieve mRNA quality control by the nonsense-mediated decay pathway. Genome Biol. 3, ReviewS1006 (2002).
Cartegni, L., Chew, S. L. & Krainer, A. R. Listening to silence and understanding nonsense: exonic mutations that affect splicing. Nature Rev. Genet. 3, 285–298 (2002).
Suzuki, Y. et al. DBTSS: database of human transcriptional start sites and full-length cDNAs. Nucleic Acids Res. 30, 328–331 (2002).
Carey, M. & Smale, S. T. Transcriptional Regulation in Eukaryotes: Concepts, Strategies, and Techniques (Cold Spring Harbor Laboratory Press, New York, 2000).
Fickett, J. W. & Hatzigeorgiou, A. G. Eukaryotic promoter recognition. Genome Res. 7, 861–878 (1997).The first comparison of promoter prediction programs.
Werner, T. Models for prediction and recognition of eukaryotic promoters. Mamm. Genome 23, 168–175 (1999).
Ohler, U. & Niemann, H. Identification and analysis of eukaryotic promoters: recent computational approaches. Trends Genet. 17, 56–60 (2001).
Zhang, M. Q. in Current Topics in Computational Molecular Biology (eds Jiang, T., Xu, Y. & Zhang, M. Q.) 249–268 (MIT Press, Cambridge, Massachusetts, 2002).
Ioshikhes, I. P. & Zhang, M. Q. Large-scale human promoter mapping using CpG islands. Nature Genet. 26, 61–63 (2000).
Zhang, M. Q. Identification of human gene core promoters in silico. Genome Res. 8, 319–326 (1998).
Scherf, M., Klingenhoff, A. & Werner, T. Highly specific localization of promoter regions in large genomic sequences by PromoterInspector: a novel context analysis approach. J. Mol. Biol. 297, 599–606 (2000).
Solovyev, V. & Salamov, A. The Gene-Finder computer tools for analysis of human and model organisms genome sequences. Proc. ISMB 5, 294–302 (1997).
Down, T. A. & Hubbard, T. J. P. Computational detection and location of transcription start sites in mammalian genomic DNA. Genome Res. 12, 458–461 (2002).
Frech, K., Quandt, K. & Werner, T. Muscle actin genes: a first step towards computational classification of tissue specific promoters. In Silico Biol. 1, 29–38 (1998).
Kel, A., Kel-Margoulis, O., Banemko, V. & Wingender, E. Recognition of NFATp/AP-1 composite elements within genes induced upon the activation of immune cells. J. Mol. Biol. 288, 353–376 (1999).
Kozak, M. A progress report on translational control in eukaryotes. SciSTKE 2001, PE1 (2001).
Davuluri, R. V., Grosse, I. & Zhang, M. Q. Computational identification of promoters and first exons in the human genome. Nature Genet. 29, 412–417 (2001).The first report of a first-exon prediction algorithm.
Fickett, J. W. ORFs and genes: how strong a connection? J. Comput. Biol. 2, 117–123 (1995).
Harrison, P. M. et al. Molecular fossils in the human genome: identification and analysis of the pseudogenes in chromosomes 21 and 22. Genome Res. 12, 272–280 (2002).
Gelfand, M. S. & Roytberg, M. A. Prediction of the exon–intron structure by a dynamic programming approach. Biosystems 30, 173–182 (1993).
Snyder, E. E. & Stormo, G. D. Identification of coding regions in genomic DNA sequences: an application of dynamic programming and neural networks. Nucleic Acids Res. 11, 607–613 (1993).
Stormo, G. D. & Haussler, D. Optimally parsing a sequence into different classes based on multiple types of evidence. Proc. Int. Conf. ISMB 2, 369–375 (1994).
Rabiner, L. R. A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77, 257–286 (1989).
Krogh, A. Two methods for improving performance of an HMM and their application for gene finding. Proc. Int. Conf. Intell. Syst. Mol. Biol. 5, 179–186 (1997).
Kulp, D., Haussler, D., Reese, M. G. & Eeckman, F. H. A generalized hidden Markov model for the recognition of human genes in DNA. Proc. Int. Conf. Intell. Syst. Mol. Biol. 4, 134–142 (1996).
Salamov, A. & Solovyev, V. Ab initio gene finding in Drosophila genome DNA. Genome Res. 10, 516–522 (2000).
Hooper, P. M., Zhang, H. & Wishart, D. S. Prediction of genetic structure in eukaryotic DNA using reference point logistic regression and sequence alignment. Bioinformatics 16, 425–438 (2000).
Cox, D. R. & Snell, E. J. Analysis of Binary Data 2nd edn (Chapman & Hall, London, 1989).
Rogic, S., Mackworth, A. K. & Ouellette, F. B. F. Improving gene recognition accuracy by combining predictions from two gene-finding programs. Bioinformatics (in the press).
Lukashin, A. V. & Borodovski, M. GeneMark.hmm: new solutions for gene finding. Nucleic Acids Res. 26, 1107–1115 (1998).
Reese, M. G., Kulp, D., Tammana, H. & Haussler, D. Genie — gene finding in Drosophila melanogaster. Genome Res. 10, 529–538 (2000).
Burset, M. & Guigo, R. Evaluation of gene structure prediction programs. Genomics 34, 353–367 (1996).The first comprehensive evaluation of gene-prediction programs using a common standard training set.
Korf, I., Flicek, P., Duan, D. & Brent, M. R. Integrating genomic homology into gene structure prediction. Bioinformatics 17 (Suppl.), 140–148 (2001).
Frisch, M. et al. In silico prediction of scaffold/matrix attachment regions in large genome sequences. Genome Res. 12, 349–354 (2002).
Zhan, H. C., Liu, D. P. & Liang, C. C. Insulator: from chromatin domain boundary to gene regulation. Hum. Genet. 109, 471–478 (2001).
Gish, W. & States, D. J. Identification of protein coding regions by database similarity search. Nature Genet. 3, 266–272 (1993).
Florea, L. et al. A computer program for aligning a cDNA sequence with a genomic DNA sequence. Genome Res. 8, 967–974 (1998).
Gelfand, M. S., Mironov, A. & Pevner, P. Gene recognition via spliced sequence alignment. Proc. Natl Acad. Sci. USA 93, 9061–9066 (1996).
Kulp, D., Haussler, D., Reese, M. G. & Eeckman, F. H. Integrating database homology in a probabilistic gene structure model. Pacif. Symp. Biocomput. 232–244 (1997).
Xu, Y. & Uberbacher, E. C. Gene prediction by pattern recognition and homology search. Proc. Int. Conf. Intell. Syst. Mol. Biol. 4, 241–251 (1996).
Krogh, A. Using database matches with HMMgene for automated gene detection in Drosophila. Genome Res. 10, 523–528 (2000).
Birney, E. & Durbin, R. Using GeneWise in the Drosophila annotation experiment. Genome Res. 10, 547–548 (2000).
Gotoh, O. Homology-based gene structure prediction: simplified matching algorithm using a translated codon (tron) and improved accuracy by allowing for long gaps. Bioinformatics 16, 190–202 (2000).
Guigo, R. et al. An assessment of gene prediction accuracy in large DNA sequences. Genome Res. 10, 1631–1642 (2000).A comparison of ab initio and alignment-based gene-prediction programs.
Yeh, R. F., Lim, L. P. & Burge, C. B. Computational inference of homologous gene structures in the human genome. Genome Res. 11, 803–816 (2001).
Reese, M. G. et al. Genome annotation assessment in Drosophila melanogaster. Genome Res. 10, 483–501 (2000).
Pennacchio, L. A. & Rubin, E. M. Genomic strategies to identify mammalian regulatory sequences. Nature Rev. Genet. 2, 100–119 (2001).
Mayor, C. et al. VISTA: visualizing global DNA sequence alignment of arbitrary length. Bioinformatics 16, 1046–1047 (2000).
Schwartz, S. et al. PipMaker — a web server for aligning two genomic DNA sequences. Genome Res. 10, 577–586 (2000).
Batzoglou, S. et al. Human and mouse gene structure: comparative analysis and application to exon prediction. Genome Res. 10, 950–958 (2000).
Kent, W. J. & Zahler, A. M. Conservation, regulation, synteny, and introns in a large C. briggsae–C. elegans genomic alignment. Genome Res. 10, 1115–1125 (2000).
Bafna, V. & Huson, D. H. The conserved exon method for gene finding. Proc. Int. Conf. Intell. Syst. Mol. Biol. 8, 3–12 (2000).
Wiehe, T., Gebauer-Jung, S., Mitchell-Olds, T. & Guigo, R. SGP-1: prediction and validation of homologous genes based on sequence alignments. Genome Res. 11, 1574–1583 (2001).
Pachter, L., Alexandersson, M. & Cawley, S. Applications of generalized pair hidden Markov models to alignment and gene finding problems. J. Comput. Biol. 9, 389–399 (2002).
Claverie, J.-M. From bioinformatics to computational biology. Genome Res. 10, 1277–1279 (2000).
Zhang, M. Q. Predicting full-length transcripts. Nature Biotechnol. 20, 275 (2002).
Miyajima, N., Burge, C. B. & Saito, T. Computational and experimental analysis identifies many novel human genes. Biochem. Biophys. Res. Commun. 272, 801–807 (2000).
Shoemaker, D. D. et al. Experimental annotation of the human genome using microarray technology. Nature 409, 922–927 (2001).
Frazer, K. A. et al. Evolutionarily conserved sequences on human chromosome 21. Genome Res. 11, 1651–1659 (2001).
Kapranov, P. et al. Large-scale transcriptional activity in chromosomes 21 and 22. Science 296, 916–919 (2002).
Lee, S. et al. Correct identification of genes from serial analysis of gene expression tag sequences. Genomics 79, 598–602 (2002).
Horak, C. E. & Snyder, M. ChIP-chip: a genomic approach for identifying transcription factor binding sites. Methods Enzymol. 350, 469–483 (2002).
Clark, T. A., Sugnet, C. W. & Ares, M. Jr. Genomewide analysis of mRNA processing in yeast using splicing-specific microarrays. Science 296, 907–910 (2002).
Yeakey, J. M. et al. Profiling alternative splicing on fiber-optic arrays. Nature Biotechnol. 20, 353–358 (2002).
Goldstrohm, A. C., Greenleaf, A. L. & Garcia-Blanco, M. A. Co-transcriptional splicing of pre-messenger RNAs: considerations for the mechanism of alternative splicing. Gene 277, 31–47 (2001).
Proudfoot, N. J., Furger, A. & Dye, M. J. Integrating mRNA processing with transcription. Cell 108, 501–512 (2002).A recent review on the interdependence of transcription and RNA processing.
Acknowledgements
My lab is supported by National Institutes of Health (NIH) grants. I thank L. Pachter and M. Alexandersson for providing their manuscript before publication; and R. Guigo and M. Brent for presenting their recent comparative analysis of human and mouse drafts at the 1% Workshop of NIH/NHGRI in July 2002. I also thank the anonymous reviewers for many helpful suggestions.
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FURTHER INFORMATION
Glossary
- REFSEQ
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The NCBI Reference Sequence project (RefSeq) provides curated gene, mRNA and protein sequences that reflect current knowledge about a sequence and its function, and that are available in the GenBank and NCBI databases.
- TRAINING DATA SET
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The known examples of an object (for example, an exon) that are used to train prediction algorithms, so that they learn the rules for predicting an object. They can be positive training sets (consisting of true objects, such as exons) or negative training sets (consisting of false objects, such as pseudoexons).
- SPLICEOSOME
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A ribonucleoprotein complex that is involved in splicing nuclear pre-mRNA. It is composed of five small nuclear ribonucleoproteins (snRNPs) and more than 50 non-snRNPs, which recognize and assemble on exon–intron boundaries to catalyse intron processing of the pre-mRNA.
- ISOCHORE
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A large region of mammalian genomic DNA sequence in which C+G compositions are relatively uniform.
- LOG-NORMAL DISTRIBUTION
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The distribution of a random variable, the logarithm of which follows a normal distribution. A normal log (length) implies a strong fixed-length selection pressure.
- EXON LENGTH DISTRIBUTION
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A statistical distribution of exon sizes.
- NONSENSE-MEDIATED DECAY
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(NMD). A pathway ensuring that mRNAs that have premature stop codons are eliminated as templates for translation.
- PSEUDOEXON
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A pre-mRNA sequence that resembles an exon, both in its size and in the presence of flanking splice-site sequences, but that is never recognized as an exon by the splicing machinery (the spliceosome).
- KOZAK SEQUENCE
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The consensus sequence for initiation of translation in vertebrates.
- PSEUDOGENE
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A DNA sequence that was derived originally from a functional protein-coding gene that has lost its function, owing to the presence of one or more inactivating mutations.
- BLASTX
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Basic local alignment tool (BLAST) is a computer program for comparing DNA and protein sequences. The BLASTX version compares a nucleotide query sequence that is translated in all reading frames with a protein sequence database.
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Zhang, M. Computational prediction of eukaryotic protein-coding genes. Nat Rev Genet 3, 698–709 (2002). https://doi.org/10.1038/nrg890
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DOI: https://doi.org/10.1038/nrg890
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