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Computational prediction of eukaryotic protein-coding genes

Key Points

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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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Figure 1: The central dogma of gene expression.
Figure 2: Exon classification.
Figure 3: Exon-definition model.
Figure 4: Different states and transitions in the Genscan hidden Markov model.
Figure 5: A generalized pair hidden Markov model.

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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

Ensembl mouse genome server

Entrez genome

Mammalian Gene Collection

RIKEN

University of Santa Cruz Genome Bioinformatics site

Glossary

REFSEQ

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

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

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

A large region of mammalian genomic DNA sequence in which C+G compositions are relatively uniform.

LOG-NORMAL DISTRIBUTION

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

A statistical distribution of exon sizes.

NONSENSE-MEDIATED DECAY

(NMD). A pathway ensuring that mRNAs that have premature stop codons are eliminated as templates for translation.

PSEUDOEXON

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

The consensus sequence for initiation of translation in vertebrates.

PSEUDOGENE

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

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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