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

Kana Shimizu, Jun Adachi, Yoichi Muraoka

研究成果: Article

17 引用 (Scopus)

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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.

元の言語English
ページ(範囲)649-664
ページ数16
ジャーナルJournal of Bioinformatics and Computational Biology
4
発行部数3
DOI
出版物ステータスPublished - 2006 6 1

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ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Computer Science Applications

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