GENSCAN also was able to simultaneously predict multiple genes on both DNA strands, thus avoiding a common deficiency in predicting overlapping genes. GENSCAN also incorporated exon-specific length distributions, allowing exon scores to correlate with a biologically observed distribution of exon lengths ( 55), as opposed to the geometric distribution of scores that decays with exon size usually generated by a hidden Markov model approach. GENSCAN incorporated a number of methods for feature detection, including a first-order weight-array model (WAM) ( 169) for predicting splice acceptors, a second-order WAM to identify the splice branch point, a novel maximal dependence decomposition (MDD) method for the splice donor site, and a fifth-order hidden Markov model for other features. In 1997 GENSCAN was published ( 26) and showed a significant improvement in gene prediction over the existing methods, using a generalized hidden Markov model to generate the gene structures. In 2004, Eddy ( 40) presented a review of the utility of hidden Markov models in DNA sequence analysis. Although both Grail and Geneparser use dynamic programming, Genie was the first to implement a generalized hidden Markov model to generate gene structures, an approach now heavily exploited in the gene prediction field for both feature detection and gene structure generation. Geneparser, Genie, and Grail implement a neural network approach trained on known examples to identify gene features. For example, Genefinder uses log-likelihood scores from sequence conservation matrices to detect gene features and a dynamic programming approach to join these to create the gene prediction. Second, the programs will attempt to optimally combine these features to form the final gene prediction. First, a signal sensor detects gene features such as ribosome attachment sites, intron donor and acceptor splice sites, initiation codons, codon biases, and open reading frames. Typically, gene prediction employs two phases. Green, unpublished), FGENESH ( 135), GeneID ( 111) and Grail I and II ( 165), GeneMark.hmm ( 94), Genie ( 124), MZEF ( 167), and Morgan ( 136), among others. The most heavily used gene prediction approaches have been Genefinder (P. The object of this section is not to provide an exhaustive discussion of the original gene prediction algorithms, as these have been addressed in reviews elsewhere ( 21, 97, 168), but to introduce the underlying methods utilized and recent directions within the field. Such ability will be important in the next phase of biology, where synthetic biology approaches will be employed to design and produce novel functional gene constructs. Nonetheless, computational modeling of gene structure represents a key way in which we can understand the underlying biological process and identify the salient genomic signals and features that are employed. Despite this effort in the creation of computational tools, the gold standard in gene structure determination is still through biochemical confirmation and relatively large efforts have been set up to expedite the experimental determination of gene structures in numerous vertebrates including mouse and human ( 28, 50, 70, 108, 150). Although the task of identifying the open reading frames of genes is somewhat simplified in prokaryotes and lower eukaryotes such as Saccharomyces cerevisiae, the problem remains largely unsolved in metazoan genomes where coding elements are substantially punctuated with introns.ĭetermining methods for ab initio gene detection has been an active area of research and a number of gene prediction algorithms have been developed. GENE PREDICTIONįrom the beginning of genomic sequencing an established goal has been the accurate ab initio prediction of genes, that is, the identification of gene structure using only information inherent in the DNA sequence. Concentrating on methods for metazoan and particularly mammalian genomes, this review provides an overview of the approaches possible for functional element prediction on a genome-wide scale, including the more recent developments and an assessment of the state-of-the-art methods in their ability to provide reliable results. For many genomes the only annotation available will be derived from ab initio algorithmic predictions or through computational methods that can transfer knowledge from related organisms. However, the rate at which genomes, including mammalian genomes, are being sequenced currently far outpaces the capacity for any systematic biochemical analysis. Computational methods for the prediction of genomic features rarely supplant molecular biology–based experiments, but instead provide a powerful starting point for further studies.
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