Dataset Information

Neural network detects errors in the assignment of mRNA splice sites.

ABSTRACT: The use of databanks in genetic research assumes reliability of the information they contain. Currently, error-detection in the manually or electronically entered data contained in the nucleotide sequence databanks at EMBL, Heidelberg and GenBank at Los Alamos is limited. We have used a subset of sequences from these databanks to train neural networks to recognize pre-mRNA splicing signals in human genes. During the training on 33 human genes from the EMBL databank seven genes appeared to disturb the learning process. Subsequent investigation revealed discrepancies from the original published papers, for three genes. In four genes, we found wrongly assigned splicing frames of introns. We believe this to be a reflection of the fact that splicing frames cannot always be unambiguously assigned on the basis of experimental data. Thus incorrect assignment appear both due to mere typographical misprints as well as erroneous interpretation of experiments. Training on 241 human sequences from GenBank revealed nine new errors. We propose that such errors could be detected by computer algorithms designed to check the consistency of data prior to their incorporation in databanks.

SUBMITTER: Brunak S

PROVIDER: S-EPMC331948 | biostudies-other | 1990 Aug

REPOSITORIES: biostudies-other

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Neural network detects errors in the assignment of mRNA splice sites.

Brunak S S Engelbrecht J J Knudsen S S

Nucleic acids research 19900801 16

The use of databanks in genetic research assumes reliability of the information they contain. Currently, error-detection in the manually or electronically entered data contained in the nucleotide sequence databanks at EMBL, Heidelberg and GenBank at Los Alamos is limited. We have used a subset of sequences from these databanks to train neural networks to recognize pre-mRNA splicing signals in human genes. During the training on 33 human genes from the EMBL databank seven genes appeared to distur ...[more]

PMID: 2395643

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Project description:BACKGROUND:Identifying splice sites is a necessary step to analyze the location and structure of genes. Two dinucleotides, GT and AG, are highly frequent on splice sites, and many other patterns are also on splice sites with important biological functions. Meanwhile, the dinucleotides occur frequently at the sequences without splice sites, which makes the prediction prone to generate false positives. Most existing tools select all the sequences with the two dimers and then focus on distinguishing the true splice sites from those pseudo ones. Such an approach will lead to a decrease in false positives; however, it will result in non-canonical splice sites missing. RESULT:We have designed SpliceFinder based on convolutional neural network (CNN) to predict splice sites. To achieve the ab initio prediction, we used human genomic data to train our neural network. An iterative approach is adopted to reconstruct the dataset, which tackles the data unbalance problem and forces the model to learn more features of splice sites. The proposed CNN obtains the classification accuracy of 90.25%, which is 10% higher than the existing algorithms. The method outperforms other existing methods in terms of area under receiver operating characteristics (AUC), recall, precision, and F1 score. Furthermore, SpliceFinder can find the exact position of splice sites on long genomic sequences with a sliding window. Compared with other state-of-the-art splice site prediction tools, SpliceFinder generates results in about half lower false positive while keeping recall higher than 0.8. Also, SpliceFinder captures the non-canonical splice sites. In addition, SpliceFinder performs well on the genomic sequences of Drosophila melanogaster, Mus musculus, Rattus, and Danio rerio without retraining. CONCLUSION:Based on CNN, we have proposed a new ab initio splice site prediction tool, SpliceFinder, which generates less false positives and can detect non-canonical splice sites. Additionally, SpliceFinder is transferable to other species without retraining. The source code and additional materials are available at https://gitlab.deepomics.org/wangruohan/SpliceFinder.

Dataset Information

Neural network detects errors in the assignment of mRNA splice sites.

Publications

Neural network detects errors in the assignment of mRNA splice sites.

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