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RNA-seq

RNA-seq

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.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Ribosome Profiling

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.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...

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Related Experiment Video

Updated: Apr 13, 2026

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Published on: November 11, 2014

Mapping Cell Identity from scRNA-seq: A primer on computational methods.

Daniele Traversa¹, Matteo Chiara¹

¹Department of Biosciences, Università degli Studi di Milano, via Celoria 26, Milan 20133, Italy.

Computational and Structural Biotechnology Journal

|April 24, 2025

Summary

This summary is machine-generated.

Single-cell RNA sequencing (scRNA-seq) analyzes gene expression for cell type identification. This guide overviews computational tools for cell identity annotation, aiding new users.

Keywords:

Cell identity Cell type annotation RNAseq ScRNAseq Transcriptomics

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Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

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Last Updated: Apr 13, 2026

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Published on: November 11, 2014

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Published on: January 10, 2019

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

Area of Science:

Genomics
Computational Biology
Cell Biology

Background:

Single-cell technologies offer unprecedented resolution for studying cellular functions.
Large-scale initiatives aim to map human cell types for understanding development and disease.
Single-cell RNA sequencing (scRNA-seq) is a common application for profiling gene expression at the single-cell level.

Purpose of the Study:

To provide a concise overview of state-of-the-art computational methods for cell identity annotation in scRNA-seq.
To classify existing tools into five main categories for clarity.
To discuss the strengths, limitations, and application ranges of these computational methods for non-computational scientists.

Main Methods:

Review and classification of existing computational tools for scRNA-seq data analysis.
Categorization of methods based on algorithmic approaches and computational requirements.
Discussion of the implications of different methods for cell type inference.

Main Results:

scRNA-seq data analysis relies on computational methods to infer cell types from gene expression patterns.
A wide array of computational tools exists, each with specific strengths, limitations, and application ranges.
Understanding these computational aspects is crucial for accurate cell identity annotation.

Conclusions:

This overview aims to guide new users and non-computational scientists in selecting appropriate tools for cell identity annotation in scRNA-seq.
Classifying methods facilitates a better understanding of their applicability.
Informed tool selection is key to advancing cell-level resolution in biological research.