N
Nikolaus Rajewsky
Researcher at Max Delbrück Center for Molecular Medicine
Publications - 190
Citations - 59153
Nikolaus Rajewsky is an academic researcher from Max Delbrück Center for Molecular Medicine. The author has contributed to research in topics: Gene & Regulation of gene expression. The author has an hindex of 76, co-authored 164 publications receiving 50045 citations. Previous affiliations of Nikolaus Rajewsky include New York University & Rockefeller University.
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NovoSpaRc: flexible spatial reconstruction of single-cell gene expression with optimal transport.
TL;DR: NovoSpaRc as mentioned in this paper is a computational framework that assigns cells to tissue locations based on a structural correspondence hypothesis, that cells in physical proximity share similar gene expression profiles.
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Roles of Long Noncoding RNAs and Circular RNAs in Translation
TL;DR: Current understanding of the roles played by lncRNAs and circRNAs in protein synthesis is reviewed and challenges and open questions in the field are discussed.
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L(ou)sy miRNA targets
TL;DR: In this article, Didiano and Hobert examined known and predicted targets of the nematode lsy-6 miRNA, question the general validity of previously proposed rules about miRNA-target interactions and suggest that many functional miRNA targets might be context dependent.
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Human muscle-derived CLEC14A-positive cells regenerate muscle independent of PAX7
Andreas Marg,Helena Escobar,Nikos Karaiskos,Stefanie Grunwald,Eric Metzler,Janine Kieshauer,Sascha Sauer,Diana Pasemann,Edoardo Malfatti,Dominique Mompoint,Susanna Quijano-Roy,Anastasiya Boltengagen,Joanna Schneider,Markus Schülke,Séverine Kunz,Robert Carlier,Carmen Birchmeier,Helge Amthor,Andreas Spuler,Christine Kocks,Nikolaus Rajewsky,Simone Spuler +21 more
TL;DR: It is shown that despite not expressing pax7, these cells regenerate muscle and contribute to the muscle stem cell niche when transplanted into mice, and transplanted human cells do not depend on PAX7 for muscle regeneration.
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Binding site discovery from nucleic acid sequences by discriminative learning of hidden Markov models
Jonas Maaskola,Nikolaus Rajewsky +1 more
TL;DR: This work presents a discriminative learning method for pattern discovery of binding sites in nucleic acid sequences based on hidden Markov models that achieves the highest motif discovery performance, while being faster than most published methods.