Genome Informatics Research Lab

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"An Assessment of Gene Prediction Accuracy in Large DNA Sequences."
R. Guigó, P. Agarwal, J.F. Abril, M. Burset and J.W. Fickett.
Genome Research 10(10):1631-1642 (2000).


One of the first useful products from the human genome will be a set of predicted genes. Besides its intrinsic scientific interest, the accuracy and completeness of this data set is of considerable importance for human health and medicine.

Though progress has been made on computational gene identification in terms of both methods and accuracy evaluation measures, most of the sequence sets in which the programs are tested are short genomic sequences, and there is concern that these accuracy measures may not extrapolate well to larger, more challenging data sets. Given the absence of experimentally verified large genomic data sets, we constructed an semi-artificial test set comprising a number of short single-gene genomic sequences with randomly generated intergenic regions. This test set, which should still present an easier problem than real human genomic sequence, mimics the 200Kbp or so long BACs being sequenced.

In our experiments with these longer genomic sequences, the accuracy of GenScan, likely the most accurate ab initio gene prediction program, dropped significantly, although its sensitivity remained high. The accuracy of similarity-based programs, such as Genewise, Procrustes, and Blastx, on the other hand, was not affected significantly by the presence of random intergenic sequence, but it depended on the strength of the similarity to the protein homolog. As expected, the accuracy dropped if the models were built using more distant homologs, and we are able to quantitatively estimate this decline. However, the specificities of these techniques are still rather good even when the similarity is weak, which is a desirable characteristic for driving expensive follow-up experiments.

Our experiments suggest that though gene prediction will improve with every new protein that is discovered and through improvements in the current set of tools, we still have a long way to go before we can decipher the precise exonic structure of every gene in the human genome using purely computational methodology.

Sequence Test Set

Here you will find the files containing the genomic DNA sequences used in the analysis (fasta format), masked or not for low complexity regions, and the gene features extracted from EMBL v50 (GFF format).

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