2017年
Rule-based Assembly for Short-read Datasets Obtained with Multiple Assemblers and k-mer Sizes
IPSJ Transactions on Bioinformatics
- ,
- ,
- ,
- 巻
- 10
- 号
- 0
- 開始ページ
- 9
- 終了ページ
- 15
- 記述言語
- 英語
- 掲載種別
- 研究論文(学術雑誌)
- DOI
- 10.2197/ipsjtbio.10.9
- 出版者・発行元
- 一般社団法人 情報処理学会
<p>Various <i>de novo</i> assembly methods based on the concept of <i>k</i>-mer have been proposed. Despite the success of these methods, an alternative approach, referred to as the hybrid approach, has recently been proposed that combines different traditional methods to effectively exploit each of their properties in an integrated manner. However, the results obtained from the traditional methods used in the hybrid approach depend not only on the specific algorithm or heuristics but also on the selection of a user-specific <i>k</i>-mer size. Consequently, the results obtained with the hybrid approach also depend on these factors. Here, we designed a new assembly approach, referred to as the rule-based assembly. This approach follows a similar strategy to the hybrid approach, but employs specific rules learned from certain characteristics of draft contigs to remove any erroneous contigs and then merges them. To construct the most effective rules for this purpose, a learning method based on decision trees, i.e., a complex decision tree, is proposed. Comparative experiments were also conducted to validate the method. The results showed that proposed method could outperformed traditional methods in certain cases.</p>
- リンク情報
- ID情報
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- DOI : 10.2197/ipsjtbio.10.9
- eISSN : 1882-6679
- CiNii Articles ID : 130005509314
- identifiers.cinii_nr_id : 9000017545754