Gene prediction: Difference between revisions
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Latest revision as of 01:59, 17 February 2025
Gene prediction or gene finding is the process of identifying the regions of genomic DNA that encode genes. This includes protein-coding genes as well as RNA genes, but may also include prediction of other functional elements such as regulatory regions. Gene finding is one of the first and most important steps in understanding the genome of a species once it has been sequenced.
Introduction[edit]
In its earliest days, gene prediction was based on looking for methionine-coding start codons (ATG in DNA, AUG in RNA) and stop codons, and was often done by hand. Today, a variety of computational and experimental methods are used for gene prediction, and gene prediction is a critical first step in bioinformatics pipelines.
Computational methods[edit]
Computational methods for gene prediction use statistics to identify gene-like patterns in the sequence, but the details vary depending on the specifics of the genome.
Ab initio methods[edit]
Ab initio gene prediction methods use statistical models to predict genes based on the genome sequence alone. These methods include Hidden Markov Models, Support Vector Machines, and Artificial Neural Networks.
Comparative methods[edit]
Comparative gene prediction methods compare a genome to the genomes of related species. These methods can identify conserved sequences that are likely to be genes, even if the sequence itself does not have a strong gene-like pattern.
Experimental methods[edit]
Experimental methods for gene prediction involve directly testing the genome for gene products. These methods include Expressed Sequence Tags (ESTs), RNA-Seq, and Protein mass spectrometry.
Challenges[edit]
Despite the many methods available, gene prediction remains a challenging problem. Some of the challenges include differentiating coding from non-coding sequences, predicting the exact boundaries of genes, and dealing with the fact that some genes are only expressed under certain conditions.
