Author

Mélissa Cardon

RIKEN Center for Integrative Medical SciencesORCID 0000-0001-9906-3236Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Genomics· 2 Bioinformatics· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Assay:bulk RNA-seq· 2 Research Focus:differential expression· 2 Organism:human· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Research Focus:drug response / drug discovery· 1 Assay:scRNA-seq· 1

Frequent Co-Authors

Akira HasegawaiD· 3 shared Erik ArneriD· 2 shared Imad AbugessaisaiD· 2 shared Shuhei Noguchi· 2 shared Takeya KasukawaiD· 2 shared Aditi KanhereiD· 1 shared Aki MinodaiD· 1 shared Albin SandeliniD· 1 shared

Activity by Year

2019-2021 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 40 mutual co-author links

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3 shared papersGenomics

Akira HasegawaORCID

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3 papers

Recent·Most cited

Automatic identification of small molecules that promote cell conversion and reprogramming

Francesco Napolitano, Trisevgeni Rapakoulia, Patrizia Annunziata, Akira Hasegawa, Mélissa Cardon, Sara Napolitano, Lorenzo Vaccaro, Antonella Iuliano, Luca G. Wanderlingh, Takeya Kasukawa, Diego L. Medina, Davide Cacchiarelli, Xin Gao, Diego di Bernardo, Erik Arner

DECCODE identifies small molecules that enhance cell reprogramming and conversion using transcriptional data and drug profiles.

Stem Cell Reports · Apr 22, 2021Peer-reviewedBioinformatics

Functional Annotation of Human Long Non-Coding RNAs via Molecular Phenotyping

Jordan A. Ramilowski, Chi Wai Yip, Saumya Agrawal, Jen-Chien Chang, Yari Ciani, Ivan V. Kulakovskiy, Mickaël Mendez, Jasmine Li Ching Ooi, John F. Ouyang, Nick Parkinson, Andreas Petri, Leonie Roos, Jessica Severin, Kayoko Yasuzawa, Imad Abugessaisa, , Chinatsu Yamamoto

The paper functionally annotates human long non-coding RNAs by systematically knocking down 285 lncRNAs and analyzing molecular phenotypes using CAGE sequencing.

Genome Research · Jul 1, 2020Peer-reviewedGenomics

Quality assessment of single-cell RNA sequencing data by coverage skewness analysis

Imad Abugessaisa, Shuhei Noguchi, Mélissa Cardon, Akira Hasegawa, Kazuhide Watanabe, Masataka Takahashi, Harukazu Suzuki, Shintaro Katayama, Juha Kere, Takeya Kasukawa

The paper introduces SkewC, a method to identify poor quality cells in scRNA-seq by analyzing gene coverage skewness, and validates it across 37,993 cells from 15 protocols.

bioRxiv · Dec 31, 2019 · PreprintGenomics