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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...

Ribosome Profiling

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 11, 2026

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

scChat: A Large Language Model-Powered Co-Pilot for Contextualized Single-Cell RNA Sequencing Analysis.

Hsuan-Han Chiu¹, Ashley Varghese¹, Kunming Shao^1,2,3

¹Davidson School of Chemical Engineering, Purdue University, 480 W. Stadium Ave, West Lafayette, IN 47907, USA.

Aiche Journal. American Institute of Chemical Engineers

|April 10, 2026

Summary

This summary is machine-generated.

scChat, a large language model (LLM)-powered co-pilot, enhances single-cell RNA sequencing (scRNA-seq) analysis by integrating research context. It provides biologically grounded explanations and supports hypothesis generation for experimental planning.

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Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

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Last Updated: Apr 11, 2026

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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Published on: January 10, 2019

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

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Published on: March 12, 2021

Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

Published on: December 1, 2023

Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Single-cell RNA sequencing (scRNA-seq) provides transcriptomic data at single-cell resolution.
Current computational methods for scRNA-seq are data-driven, limiting contextual interpretation and hypothesis generation.

Purpose of the Study:

To introduce scChat, a novel large language model (LLM)-powered co-pilot for contextualized scRNA-seq analysis.
To develop an interactive, reasoning-based framework that integrates quantitative algorithms with retrieval-augmented generation and a multi-agent architecture.

Main Methods:

Development of scChat, an LLM-powered co-pilot.
Integration of quantitative algorithms, retrieval-augmented generation, and a multi-agent architecture.
Showcase and benchmarking studies to evaluate performance.

Main Results:

scChat achieves high accuracy in cell type annotation.
The system provides biologically grounded explanations and contextual insights.
scChat supports hypothesis validation, mechanistic interpretation, and experimental design.

Conclusions:

scChat offers a significant advancement over conventional scRNA-seq pipelines.
The LLM-powered co-pilot enhances the interpretability and impact of scRNA-seq data.
scChat facilitates hypothesis generation and experimental planning in biomedical research.