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An Overview of Gene Identification: Approaches, Strategies, and Considerations

Andreas D. Baxevanis¹

¹National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland

Publication Name:

Current Protocols in Bioinformatics

Unit Number:

Unit 4.1

DOI:

10.1002/0471250953.bi0401s6

Online Posting Date:

September, 2004

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Abstract

Modern biology is on the verge of officially ushering in a new era in science with the completion of the sequencing of the human genome in April 2003. While often erroneously called the “post-genome era”, this will actually truly mark the beginning of the “genome era,” a time in which the availability of sequence data for many genomes will have a significant effect on how science is performed in the 21st century. This unit offers an overview of many of the gene prediction methods that are currently available and offers a general assessment of how well the methods work for various problems.

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Unit Introduction
Remembering Biology in Deducing Gene Structure
Categorizing the Methods
How Well do the Methods Work?
Strategies and Considerations
Future Directions
Acknowledgments
Literature Cited
Figures
Tables

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Figures

Figure 4.1.1

The central dogma of molecular biology. Proceeding from the DNA through the RNA to the protein level, various sequence features and modifications can be identified that can be used in the computational deduction of gene structure. These include the presence of promoter and regulatory regions, intron-exon boundaries, and both start and stop signals. Unfortunately, these signals are not always present, and when present may not always be in the same form or context. The reader is referred to the text for greater detail.

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Figure 4.1.2

Sensitivity vs. specificity. In the upper portion of the figure, the four possible outcomes of a prediction are shown: a true positive (TP), a true negative (TN), a false positive (FP), and a false negative (FN). The matrix at the bottom of the figure shows how both sensitivity and specificity are determined from these four possible outcomes, giving a tangible measure of the effectiveness of any gene prediction method. (Figure adapted from Burset and Guigó, 1996 and Snyder and Stormo, 1997.)

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Figure 4.1.3

Annotated output from GeneMachine showing the results of multiple gene prediction program runs. NCBI Sequin is used as the viewer. At the top of the output are shown the results from various BLAST runs (BLASTN vs. dbEST, BLASTN vs. nr, and BLASTX vs. SWISS-PROT). Towards the bottom of the window are shown the results from the predictive methods (FGENES, GENSCAN, MZEF, and GRAIL 2). Annotations indicating the strength of the prediction are preserved and shown wherever possible within the viewer. Putative regions of high interest would be areas where hits from the BLAST runs line up with exon predictions from the gene prediction programs.

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Literature Cited

Literature Cited
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