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Title :Rule-based Assembly for Short-read Datasets Obtained with Multiple Assemblers and k-mer Sizes
Authors :Ohshiro, Ayako
Afuso, Hitoshi
Okazaki, Takeo
Nakamura, Morikazu
Issue Date :2017
Abstract :Various de novo assembly methods based on the concept of k-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 k-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.
URL :https://doi.org/10.2197/ipsjtbio.10.9
Type Local :雑誌掲載論文
ISSN :1882-6679
Publisher :Information Processing Society of Japan (IPSJ)
URI :http://hdl.handle.net/20.500.12000/47510
Citation :IPSJ Transactions on Bioinformatics Vol.10 p.9 -15
Appears in Collections:Peer-reviewed Journal Articles (Faculty of Engineering)

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