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Tsukasa Fukunaga

Researcher at University of Tokyo

Publications -  38
Citations -  2039

Tsukasa Fukunaga is an academic researcher from University of Tokyo. The author has contributed to research in topics: RNA & Computer science. The author has an hindex of 10, co-authored 31 publications receiving 1370 citations. Previous affiliations of Tsukasa Fukunaga include Japan Society for the Promotion of Science & Waseda University.

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MiFish, a set of universal PCR primers for metabarcoding environmental DNA from fishes: detection of more than 230 subtropical marine species.

TL;DR: The metabarcoding approach presented here is non-invasive, more efficient, more cost-effective and more sensitive than the traditional survey methods and has the potential to serve as an alternative tool for biodiversity monitoring that revolutionizes natural resource management and ecological studies of fish communities on larger spatial and temporal scales.
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MitoFish and MitoAnnotator: A Mitochondrial Genome Database of Fish with an Accurate and Automatic Annotation Pipeline

TL;DR: MitoFish contains re-annotations of previously sequenced fish mitogenomes, enabling researchers to refer to them when they find annotations that are likely to be erroneous or while conducting comparative mitogenomic analyses.
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Evolutionary origin of the Scombridae (tunas and mackerels): members of a paleogene adaptive radiation with 14 other pelagic fish families.

TL;DR: A clade of open-ocean fishes containing Scombridae is named “Pelagia” in reference to the common habitat preference that links the 15 families, suggesting that they represent a previously undetected adaptive radiation in the pelagic realm.
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MitoFish and MiFish Pipeline: A Mitochondrial Genome Database of Fish with an Analysis Pipeline for Environmental DNA Metabarcoding.

TL;DR: Recent functional updates of MitoFish and implementation of MiFish pipeline for metabarcoding analysis of fish mitochondrial environmental DNA, which is a fast-emerging and powerful technology in fish studies are described.
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CapR: revealing structural specificities of RNA-binding protein target recognition using CLIP-seq data

TL;DR: Using CapR, an efficient algorithm that calculates the probability that each RNA base position is located within each secondary structural context, it is demonstrated that several RBPs bind to their target RNA molecules under specific structural contexts.