Chaining Sequence/Structure Seeds for Computing RNA Similarity | Zendy

Laetitia Bourgeade | Zendy; Cédric Chauve | Zendy; Julien Allali | Zendy

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Chaining Sequence/Structure Seeds for Computing RNA Similarity

Author(s) -

Laetitia Bourgeade,

Cédric Chauve,

Julien Allali

Publication year - 2015

Publication title -

journal of computational biology

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.585

H-Index - 95

eISSN - 1557-8666

pISSN - 1066-5277

DOI - 10.1089/cmb.2014.0283

Subject(s) - chaining , search engine indexing , computer science , set (abstract data type) , pipeline (software) , computation , benchmark (surveying) , sequence (biology) , similarity (geometry) , algorithm , rna , computational biology , data mining , artificial intelligence , biology , genetics , psychology , geodesy , gene , image (mathematics) , psychotherapist , programming language , geography

We describe a new method to compare a query RNA with a static set of target RNAs. Our method is based on (i) a static indexing of the sequence/structure seeds of the target RNAs; (ii) searching the target RNAs by detecting seeds of the query present in the target, chaining these seeds in promising candidate homologs; and then (iii) completing the alignment using an anchor-based exact alignment algorithm. We apply our method on the benchmark Bralibase2.1 and compare its accuracy and efficiency with the exact method LocARNA and its recent seeds-based speed-up ExpLoc-P. Our pipeline RNA-unchained greatly improves computation time of LocARNA and is comparable to the one of ExpLoc-P, while improving the overall accuracy of the final alignments.

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