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Related Experiment Video

Updated: Oct 13, 2025

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

Besca, a single-cell transcriptomics analysis toolkit to accelerate translational research.

Sophia Clara Mädler¹, Alice Julien-Laferriere¹, Luis Wyss¹

¹Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel, Switzerland.

NAR Genomics and Bioinformatics

|November 11, 2021

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This summary is machine-generated.

Besca is a new software toolkit that standardizes single-cell RNA sequencing (scRNA-seq) analysis. It automates cell annotation and deconvolutes bulk RNA sequencing data, improving translational research.

Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Single-cell RNA sequencing (scRNA-seq) has transformed biological research.
Standardized and robust software is crucial for translational applications of scRNA-seq.
Current workflows can be complex and lack interoperability.

Purpose of the Study:

To present Besca, a software toolkit for streamlining scRNA-seq analysis.
To enable automated cell annotation and deconvolution of bulk RNA-seq data.
To enhance the reusability and interpretability of scRNA-seq data in translational research.

Main Methods:

Besca offers a standard workflow including quality control, filtering, and clustering.
Two modules utilize hierarchical cell signatures and machine learning for automated cell annotation.

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Last Updated: Oct 13, 2025

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

Transcriptome Analysis of Single Cells

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

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Published on: February 23, 2024

Gene expression profiles are used to estimate cell type proportions in bulk transcriptomics.

Main Results:

Besca successfully streamlines scRNA-seq analyses according to best practices.
Automated cell annotation and nomenclature harmonization were achieved.
The toolkit demonstrated acceleration, interoperability, and reusability across diverse datasets, including tumor tissues.

Conclusions:

Besca provides a standardized and robust solution for scRNA-seq data analysis.
The toolkit addresses critical needs in translational research by enhancing data accessibility and interpretability.
Besca facilitates the integration of scRNA-seq findings into broader biological and clinical contexts.