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Related Concept Videos

RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
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An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing
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SINCERA: A Pipeline for Single-Cell RNA-Seq Profiling Analysis.

Minzhe Guo1,2, Hui Wang1, S Steven Potter3

  • 1The Perinatal Institute, Section of Neonatology, Perinatal and Pulmonary Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, United States of America.

Plos Computational Biology
|November 25, 2015
PubMed
Summary
This summary is machine-generated.

A new computational pipeline, SINCERA (SINgle CEll RNA-seq profiling Analysis), aids in analyzing single-cell RNA-seq data. This tool identifies cell types and gene signatures, advancing developmental biology research.

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Area of Science:

  • Developmental Biology
  • Computational Biology
  • Genomics

Background:

  • Understanding organogenesis requires analyzing cellular heterogeneity and differentiation processes.
  • Single-cell RNA sequencing (scRNA-seq) offers powerful insights but necessitates robust analytical tools for complex datasets.

Purpose of the Study:

  • To present SINCERA, a versatile computational pipeline for processing and analyzing scRNA-seq data.
  • To enable identification of cell types, gene signatures, and regulatory drivers in complex biological samples.

Main Methods:

  • Development of SINCERA, a computational pipeline implemented in R.
  • Application of the pipeline to scRNA-seq data from embryonic mouse lung at E16.5.

Main Results:

  • SINCERA successfully distinguished major cell types in the fetal mouse lung (epithelial, endothelial, smooth muscle, pericyte, fibroblast-like).
  • The pipeline identified cell-type-specific gene signatures, bioprocesses, and key regulators.
  • Demonstrated the pipeline's utility for analyzing whole organs or sorted cells.

Conclusions:

  • SINCERA provides a generally applicable solution for scRNA-seq data analysis.
  • The tool facilitates deeper understanding of cellular heterogeneity and developmental processes.
  • SINCERA is freely available, promoting accessibility in biological research.