Prediction of Gene Structures from RNA-seq Data Using Dual Decomposition
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- Inatsuki Tatsumu
- Department of Biosciences and Informatics, Keio University
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- Sato Kengo
- Department of Biosciences and Informatics, Keio University
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- Sakakibara Yasubumi
- Department of Biosciences and Informatics, Keio University
Description
Numerous computational algorithms for predicting protein-coding genes from genomic sequences have been developed, and hidden Markov models (HMMs) have frequently been used to model gene structures. For eukaryotes, more complex gene structures such as introns make gene prediction much harder due to isoforms of transcripts by alternative splicing machinery. We develop a novel gene prediction method for eukaryote genomes that extends the traditional HMM-based gene prediction model by incorporating comprehensive evidence of transcripts by using RNA sequencing (RNA-seq) technology. We formulate gene prediction as an integer programming problem, and solve it by the dual decomposition technique. To confirm the utility of the proposed algorithm, computational experiments on benchmark datasets were conducted. The results show that our algorithm efficiently and effectively employs RNA-seq data in gene structure prediction.
Journal
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- IPSJ Transactions on Bioinformatics
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IPSJ Transactions on Bioinformatics 9 (0), 1-6, 2016
Information Processing Society of Japan
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Keywords
Details 詳細情報について
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- CRID
- 1390001205294913664
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- NII Article ID
- 130005139841
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- ISSN
- 18826679
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- Text Lang
- en
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- Data Source
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- JaLC
- Crossref
- CiNii Articles
- KAKEN
- OpenAIRE
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- Abstract License Flag
- Disallowed