Uncovering transcriptional dark matter via gene annotation independent single-cell RNA sequencing analysis
Conventional single-cell RNA sequencing analysis rely on genome annotations that may be incomplete or inaccurate especially for understudied organisms. Here the authors present a bioinformatic tool that leverages single-cell data to uncover biologically relevant transcripts beyond the best available...
Enregistré dans:
Auteurs principaux: | Michael F. Z. Wang, Madhav Mantri, Shao-Pei Chou, Gaetano J. Scuderi, David W. McKellar, Jonathan T. Butcher, Charles G. Danko, Iwijn De Vlaminck |
---|---|
Format: | article |
Langue: | EN |
Publié: |
Nature Portfolio
2021
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/61a7bc13e77e42f98fc57182ea58774c |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
Spatiotemporal single-cell RNA sequencing of developing chicken hearts identifies interplay between cellular differentiation and morphogenesis
par: Madhav Mantri, et autres
Publié: (2021) -
Large-scale integration of single-cell transcriptomic data captures transitional progenitor states in mouse skeletal muscle regeneration
par: David W. McKellar, et autres
Publié: (2021) -
Most "dark matter" transcripts are associated with known genes.
par: Harm van Bakel, et autres
Publié: (2010) -
Supergravity with mimetic dark matter
par: Ali H. Chamseddine
Publié: (2021) -
Asymmetric accidental composite dark matter
par: Salvatore Bottaro, et autres
Publié: (2021)