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Karyotyping

Karyotyping

Genome Annotation and Assembly

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.

Subcellular Fractionation

Subcellular Fractionation

The homogenate obtained after cell lysis contains various membrane-bound organelles that can be further separated into pure fractions by subcellular fractionation. These isolates are used to study specific cellular components, analyze localized protein activity, and are even employed in diagnostics. Fractionation is typically achieved using centrifugation methods, the most common being density-gradient and differential centrifugation.
Differential Centrifugation
Differential centrifugation is...

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Related Experiment Video

Updated: May 7, 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

Building and analyzing metacells in single-cell genomics data.

Mariia Bilous^1,2,3,4, Léonard Hérault^1,2,3,4, Aurélie Ag Gabriel^1,2,3,4

¹Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland.

Molecular Systems Biology

|May 29, 2024

Summary

This summary is machine-generated.

Metacells simplify complex single-cell genomics data, preserving biological insights for better analysis. This review guides their effective use and provides tools for exploring single-cell RNA sequencing data.

Keywords:

Coarse-graining Metacells Single-cell Data Analysis Single-cell Genomics Tutorial

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Reusable Single Cell for Iterative Epigenomic Analyses

Reusable Single Cell for Iterative Epigenomic Analyses

Published on: February 11, 2022

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Last Updated: May 7, 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

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

Reusable Single Cell for Iterative Epigenomic Analyses

Reusable Single Cell for Iterative Epigenomic Analyses

Published on: February 11, 2022

Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

High-throughput single-cell genomics generates massive datasets, posing computational challenges.
Existing tools struggle to scale with increasing cell numbers and data complexity.
Metacells offer a solution to reduce data size and complexity while retaining biological information.

Purpose of the Study:

To review recent studies utilizing the metacell concept in single-cell genomics.
To provide guidance on the appropriate application and considerations for metacell analysis.
To offer practical resources for metacell construction and analysis.

Main Methods:

Review of literature on metacell applications in single-cell genomics.
Development of a tutorial for constructing and analyzing metacells from single-cell RNA-seq data.
Creation of an integrated toolkit for rapid metacell building, visualization, and evaluation.

Main Results:

Identification and synthesis of various metacell nomenclature and approaches.
Guidelines established for the effective and judicious use of metacells.
Accessible tutorial and toolkit provided for practical metacell data analysis.

Conclusions:

Metacells are a valuable strategy for managing and interpreting large-scale single-cell genomics data.
Understanding metacell principles and limitations is crucial for robust analysis.
The provided resources facilitate the adoption and application of metacell methods.