Angle

A sequencing errors resistant program for predicting protein coding regions in unfinished cDNA

Kana Shimizu, Jun Adachi, Yoichi Muraoka

    Research output: Contribution to journalArticle

    13 Citations (Scopus)

    Abstract

    In the process of making full-length cDNA, predicting protein coding regions helps both in the preliminary analysis of genes and in any succeeding process. However, unfinished cDNA contains artifacts including many sequencing errors, which hinder the correct evaluation of coding sequences. Especially, predictions of short sequences are difficult because they provide little information for evaluating coding potential. In this paper, we describe ANGLE, a new program for predicting coding sequences in low quality cDNA. To achieve error-tolerant prediction, ANGLE uses a machine-learning approach, which makes better expression of coding sequence maximizing the use of limited information from input sequences. Our method utilizes not only codon usage, but also protein structure information which is difficult to be used for stochastic model-based algorithms, and optimizes limited information from a short segment when deciding coding potential, with the result that predictive accuracy does not depend on the length of an input sequence. The performance of ANGLE is compared with ESTSCAN on four dataset each of them having a different error rate (one frame-shift error or one substitution error per 200-500 nucleotides) and on one dataset which has no error. ANGLE outperforms ESTSCAN by 9.26% in average Matthews's correlation coefficient on short sequence dataset (<1000 bases). On long sequence dataset, ANGLE achieves comparable performance.

    Original languageEnglish
    Pages (from-to)649-664
    Number of pages16
    JournalJournal of Bioinformatics and Computational Biology
    Volume4
    Issue number3
    DOIs
    Publication statusPublished - 2006 Jun

    Fingerprint

    Open Reading Frames
    Complementary DNA
    Proteins
    Codon
    Artifacts
    Nucleotides
    Stochastic models
    Learning systems
    Datasets
    Substitution reactions
    Genes

    Keywords

    • AdaBoost
    • cDNA
    • Coding sequence
    • EST
    • Sequencing errors

    ASJC Scopus subject areas

    • Medicine(all)
    • Cell Biology

    Cite this

    Angle : A sequencing errors resistant program for predicting protein coding regions in unfinished cDNA. / Shimizu, Kana; Adachi, Jun; Muraoka, Yoichi.

    In: Journal of Bioinformatics and Computational Biology, Vol. 4, No. 3, 06.2006, p. 649-664.

    Research output: Contribution to journalArticle

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