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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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Updated: Apr 26, 2026

An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing

An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing

Published on: May 23, 2018

Modeling genome coverage in single-cell sequencing.

Timothy Daley¹, Andrew D Smith¹

¹Department of Mathematics and Department of Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA.

Bioinformatics (Oxford, England)

|August 10, 2014

Summary

This summary is machine-generated.

Predicting genome coverage in single-cell DNA sequencing is crucial. A new method uses shallow sequencing data to estimate deep sequencing coverage, aiding protocol optimization without extensive sequencing.

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Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

Area of Science:

Genomics
Molecular Biology

Background:

Single-cell DNA sequencing reveals genetic variation missed by bulk methods.
Protocol choice and library preparation introduce significant variability in genome representation.
Amplification biases and material loss cause extreme variation in single-cell sequencing library coverage.

Purpose of the Study:

To develop a method for predicting genome coverage in deep sequencing experiments.
To enable optimization and comparison of single-cell sequencing protocols.
To provide a way to screen libraries without deep sequencing.

Main Methods:

Utilizes information from an initial shallow sequencing experiment mapped to a reference genome.
Employs a non-parametric empirical Bayes Poisson model on observed coverage statistics.
Estimates the gain in coverage achievable with deeper sequencing.

Main Results:

Accurately predicts genome coverage for deep sequencing experiments.
Allows researchers to understand deep sequencing characteristics without performing them.
Facilitates informed decisions on single-cell sequencing protocol selection.

Conclusions:

The proposed method offers a valuable tool for single-cell DNA sequencing research.
Enables efficient optimization and comparison of sequencing protocols.
Reduces the need for costly and time-consuming deep sequencing for initial assessments.