Model-based deconvolution of cell cycle time-series data reveals gene expression details at high resolution.
In both prokaryotic and eukaryotic cells, gene expression is regulated across the cell cycle to ensure "just-in-time" assembly of select cellular structures and molecular machines. However, present in all time-series gene expression measurements is variability that arises from both systema...
Saved in:
Main Authors: | Dan Siegal-Gaskins, Joshua N Ash, Sean Crosson |
---|---|
Format: | article |
Language: | EN |
Published: |
Public Library of Science (PLoS)
2009
|
Subjects: | |
Online Access: | https://doaj.org/article/d0e8606b007b46e4be290813e7e4e31e |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Benchmarking of cell type deconvolution pipelines for transcriptomics data
by: Francisco Avila Cobos, et al.
Published: (2020) -
Reconstructing cell cycle pseudo time-series via single-cell transcriptome data
by: Zehua Liu, et al.
Published: (2017) -
Phase resetting reveals network dynamics underlying a bacterial cell cycle.
by: Yihan Lin, et al.
Published: (2012) -
Benchmarking pipelines for subclonal deconvolution of bulk tumour sequencing data
by: Georgette Tanner, et al.
Published: (2021) -
Deconvolution of single-cell multi-omics layers reveals regulatory heterogeneity
by: Longqi Liu, et al.
Published: (2019)