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Vertebrate gene predictions and the problem of large genes

Nature Reviews Genetics volume 4, pages 741–749 (2003)Cite this article

Abstract

To find unknown protein-coding genes, annotation pipelines use a combination of ab initio gene prediction and similarity to experimentally confirmed genes or proteins. Here, we show that although the ab initio predictions have an intrinsically high false-positive rate, they also have a consistently low false-negative rate. The incorporation of similarity information is meant to reduce the false-positive rate, but in doing so it increases the false-negative rate. The crucial variable is gene size (including introns) — genes of the most extreme sizes, especially very large genes, are most likely to be incorrectly predicted.

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Figure 1: Actual versus predicted exons in a known gene: TEA domain family member 1 (SV40 transcriptional enhancer factor on human chromosome 11).
Figure 2: Correlation between gene size and intron size.
Figure 3: Size dependencies for false-positive and false-negative rates.
Figure 4: Size dependency for gene fragmentation problem.
Figure 5: Detection of erroneous predictions using gene size.
Figure 6: Size dependency in tissue-specific expression.
Figure 7: Size independence of the over-prediction problem.
Figure 8: Complete and partial failure to detect a gene.

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Acknowledgements

We thank E. Eyras at the Sanger Center, UK, for explaining the details of the Ensembl procedures to us. This work was sponsored by the Chinese Academy of Sciences, Commission for Economy Planning, Ministry of Science and Technology, National Natural Science Foundation of China, Beijing Municipal Government, Zhejiang Provincial Government and Hangzhou Municipal Government. Some of this work was also supported by the National Human Genome Research Institute.

Author information

Author notes

  1. Jun Wang, ShengTing Li and Yong Zhang: J. W., S. T. L. and Y. Z. contributed equally to this work.

Authors and Affiliations

  1. Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 101300, China

    Jun Wang, ShengTing Li, Yong Zhang, HongKun Zheng, Zhao Xu, Jia Ye, Jun Yu & Gane Ka-Shu Wong

  2. James D. Watson Institute of Zhejiang University, Hangzhou Genomics Institute, Key Laboratory of Bioinformatics of Zhejiang Province, Hangzhou, 310007, China

    Jun Wang, Jun Yu & Gane Ka-Shu Wong

  3. College of Life Sciences, Peking University, Beijing, 100871, China

    Yong Zhang

  4. Department of Medicine, UW Genome Center, University of Washington, Seattle, 98195, Washington, USA

    Jun Yu & Gane Ka-Shu Wong

Authors
  1. Jun Wang
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Correspondence to Gane Ka-Shu Wong.

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FURTHER INFORMATION

BLAST

BLAT

Ensembl

FANTOM

FgeneSH

Gene Ontology

GeneMark

GenScan

RefSeq

SGP2

TwinScan

UCSC Human Genome Browser

Glossary

AB INITIO GENE PREDICTION

The identification of protein-coding genes in genomic sequence, using no prior knowledge other than the signal and content terms.

AMYGDALA

An almond-shaped neurostructure that is involved in the production and response to non-verbal signs of anger, avoidance, defensiveness and fear.

ANNOTATION PIPELINES

A series of computer procedures that is used to identify the biological contents of a sequenced genome. Gene finding is only the first of many steps. Subsequent steps might include the identification of homologous genes, the assignment of biological function and so on.

CDS SIZE

The size of the spliced transcript, excluding introns. As gene-prediction programs do not detect untranslated regions, we do not include them in this definition.

COMPLETE MISS

(CM). The probability that less than 100 bp of the protein-coding sequence of a gene is correctly predicted.

CONTENT TERMS

Patterns of codon usage, which are unique to each species, that allow protein-coding sequences to be distinguished from surrounding non-coding sequence.

FALSE DESERT

(FD). A fraction of a sequence of a gene, including its introns which is not covered by any of the gene predictions.

FALSE NEGATIVE

(FN). The probability that a segment that is known to code for protein is not correctly predicted to be coding, specified as a per-base pair or per-amino acid rate.

FALSE POSITIVE

(FP). The probability that a segment that is predicted to code for protein is not in fact known to be coding, given as a per-base pair or per-amino acid rate. Note that we only count those exons that have some overlap to the region of the genome that is defined by the cDNA alignment. Exons that lie outside this region are relegated to the over-predictions.

GENE SIZE

The size of the unspliced transcript, including introns. As gene-prediction programs do not detect untranslated regions, we do not include them in this definition.

OUTLIER GENES

Genes the sequence characteristics of which are sufficiently outside the normal range to create problems for ab initio gene prediction.

OVER-PREDICTION

Predicted exons that lie entirely outside the region of the genome that is defined by the complementary DNA alignment, but which are part of a prediction that has some overlap with this region. Note the distinction between this and false positives.

PER-AMINO ACID RATE

(Per-aa rate). In computing FPs and FNs, this is the method in which we also insist that the correct amino acids are predicted, which requires that the reading frame is correctly assigned.

PER-BASE PAIR RATE

(Per-bp rate). In computing FPs and FNs, this is the method in which we only ask that the correct nucleotides are predicted, without checking if the reading frame is correctly assigned.

REFSEQ

The division of GenBank that is devoted to full-length reference sequences for experimentally confirmed genes.

SENSITIVITY

A measure of prediction that is equivalent to one minus the false-negative rate.

SERIAL ANALYSIS OF GENE EXPRESSION

(SAGE). A quantitative expression assay that is based on tags that are 10–20 bp in length, which are derived from mRNAs.

SIGNAL TERMS

Short sequence motifs, such as splice sites, branch points, polypyrimidine tracts, start codons and stop codons, that are used to detect exon boundaries.

SPECIFICITY

A measure of prediction that is equivalent to one minus the false-positive rate.

TRAINING SET

A set of known protein-coding sequences that is used to teach the ab initio gene-prediction program what the codon-usage patterns look like for a given species.

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Wang, J., Li, S., Zhang, Y. et al. Vertebrate gene predictions and the problem of large genes. Nat Rev Genet 4, 741–749 (2003). https://doi.org/10.1038/nrg1160

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