Dataset Information

A method of precise mRNA/DNA homology-based gene structure prediction.

ABSTRACT:

Background

Accurate and automatic gene finding and structural prediction is a common problem in bioinformatics, and applications need to be capable of handling non-canonical splice sites, micro-exons and partial gene structure predictions that span across several genomic clones.

Results

We present a mRNA/DNA homology based gene structure prediction tool, GIGOgene. We use a new affine gap penalty splice-enhanced global alignment algorithm running in linear memory for a high quality annotation of splice sites. Our tool includes a novel algorithm to assemble partial gene structure predictions using interval graphs. GIGOgene exhibited a sensitivity of 99.08% and a specificity of 99.98% on the Genie learning set, and demonstrated a higher quality of gene structural prediction when compared to Sim4, est2genome, Spidey, Galahad and BLAT, including when genes contained micro-exons and non-canonical splice sites. GIGOgene showed an acceptable loss of prediction quality when confronted with a noisy Genie learning set simulating ESTs.

Conclusion

GIGOgene shows a higher quality of gene structure prediction for mRNA/DNA spliced alignment when compared to other available tools.

SUBMITTER: Churbanov A

PROVIDER: S-EPMC1274302 | biostudies-literature | 2005 Oct

REPOSITORIES: biostudies-literature

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Publications

A method of precise mRNA/DNA homology-based gene structure prediction.

Churbanov Alexander A Pauley Mark M Quest Daniel D Ali Hesham H

BMC bioinformatics 20051021

<h4>Background</h4>Accurate and automatic gene finding and structural prediction is a common problem in bioinformatics, and applications need to be capable of handling non-canonical splice sites, micro-exons and partial gene structure predictions that span across several genomic clones.<h4>Results</h4>We present a mRNA/DNA homology based gene structure prediction tool, GIGOgene. We use a new affine gap penalty splice-enhanced global alignment algorithm running in linear memory for a high quality ...[more]

PMID: 16242044

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Project description:BackgroundPrediction of protein structural classes (alpha, beta, alpha + beta and alpha/beta) from amino acid sequences is of great importance, as it is beneficial to study protein function, regulation and interactions. Many methods have been developed for high-homology protein sequences, and the prediction accuracies can achieve up to 90%. However, for low-homology sequences whose average pairwise sequence identity lies between 20% and 40%, they perform relatively poorly, yielding the prediction accuracy often below 60%.ResultsWe propose a new method to predict protein structural classes on the basis of features extracted from the predicted secondary structures of proteins rather than directly from their amino acid sequences. It first uses PSIPRED to predict the secondary structure for each protein sequence. Then, the chaos game representation is employed to represent the predicted secondary structure as two time series, from which we generate a comprehensive set of 24 features using recurrence quantification analysis, K-string based information entropy and segment-based analysis. The resulting feature vectors are finally fed into a simple yet powerful Fisher's discriminant algorithm for the prediction of protein structural classes. We tested the proposed method on three benchmark datasets in low homology and achieved the overall prediction accuracies of 82.9%, 83.1% and 81.3%, respectively. Comparisons with ten existing methods showed that our method consistently performs better for all the tested datasets and the overall accuracy improvements range from 2.3% to 27.5%. A web server that implements the proposed method is freely available at http://www1.spms.ntu.edu.sg/~chenxin/RKS_PPSC/.ConclusionThe high prediction accuracy achieved by our proposed method is attributed to the design of a comprehensive feature set on the predicted secondary structure sequences, which is capable of characterizing the sequence order information, local interactions of the secondary structural elements, and spacial arrangements of alpha helices and beta strands. Thus, it is a valuable method to predict protein structural classes particularly for low-homology amino acid sequences.

Dataset Information

A method of precise mRNA/DNA homology-based gene structure prediction.

Background

Results

Conclusion

Publications

A method of precise mRNA/DNA homology-based gene structure prediction.

Similar Datasets

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