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SingleCAnalyzer: Interactive Analysis of Single Cell RNA-Seq Data on the Cloud

Single-cell RNA sequencing (scRNA-Seq) enables researchers to quantify the transcriptomes of individual cells. The capacity of researchers to perform this type of analysis has allowed researchers to undertake new scientific goals. The usefulness of scRNA-Seq has depended on the development of new co...

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Detalles Bibliográficos
Autores principales: Prieto, Carlos, Barrios, David, Villaverde, Angela
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9580930/
https://www.ncbi.nlm.nih.gov/pubmed/36304292
http://dx.doi.org/10.3389/fbinf.2022.793309
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author Prieto, Carlos
Barrios, David
Villaverde, Angela
author_facet Prieto, Carlos
Barrios, David
Villaverde, Angela
author_sort Prieto, Carlos
collection PubMed
description Single-cell RNA sequencing (scRNA-Seq) enables researchers to quantify the transcriptomes of individual cells. The capacity of researchers to perform this type of analysis has allowed researchers to undertake new scientific goals. The usefulness of scRNA-Seq has depended on the development of new computational biology methods, which have been designed to meeting challenges associated with scRNA-Seq analysis. However, the proper application of these computational methods requires extensive bioinformatics expertise. Otherwise, it is often difficult to obtain reliable and reproducible results. We have developed SingleCAnalyzer, a cloud platform that provides a means to perform full scRNA-Seq analysis from FASTQ within an easy-to-use and self-exploratory web interface. Its analysis pipeline includes the demultiplexing and alignment of FASTQ files, read trimming, sample quality control, feature selection, empty droplets detection, dimensional reduction, cellular type prediction, unsupervised clustering of cells, pseudotime/trajectory analysis, expression comparisons between groups, functional enrichment of differentially expressed genes and gene set expression analysis. Results are presented with interactive graphs, which provide exploratory and analytical features. SingleCAnalyzer is freely available at https://singleCAnalyzer.eu.
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spelling pubmed-95809302022-10-26 SingleCAnalyzer: Interactive Analysis of Single Cell RNA-Seq Data on the Cloud Prieto, Carlos Barrios, David Villaverde, Angela Front Bioinform Bioinformatics Single-cell RNA sequencing (scRNA-Seq) enables researchers to quantify the transcriptomes of individual cells. The capacity of researchers to perform this type of analysis has allowed researchers to undertake new scientific goals. The usefulness of scRNA-Seq has depended on the development of new computational biology methods, which have been designed to meeting challenges associated with scRNA-Seq analysis. However, the proper application of these computational methods requires extensive bioinformatics expertise. Otherwise, it is often difficult to obtain reliable and reproducible results. We have developed SingleCAnalyzer, a cloud platform that provides a means to perform full scRNA-Seq analysis from FASTQ within an easy-to-use and self-exploratory web interface. Its analysis pipeline includes the demultiplexing and alignment of FASTQ files, read trimming, sample quality control, feature selection, empty droplets detection, dimensional reduction, cellular type prediction, unsupervised clustering of cells, pseudotime/trajectory analysis, expression comparisons between groups, functional enrichment of differentially expressed genes and gene set expression analysis. Results are presented with interactive graphs, which provide exploratory and analytical features. SingleCAnalyzer is freely available at https://singleCAnalyzer.eu. Frontiers Media S.A. 2022-05-23 /pmc/articles/PMC9580930/ /pubmed/36304292 http://dx.doi.org/10.3389/fbinf.2022.793309 Text en Copyright © 2022 Prieto, Barrios and Villaverde. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Bioinformatics
Prieto, Carlos
Barrios, David
Villaverde, Angela
SingleCAnalyzer: Interactive Analysis of Single Cell RNA-Seq Data on the Cloud
title SingleCAnalyzer: Interactive Analysis of Single Cell RNA-Seq Data on the Cloud
title_full SingleCAnalyzer: Interactive Analysis of Single Cell RNA-Seq Data on the Cloud
title_fullStr SingleCAnalyzer: Interactive Analysis of Single Cell RNA-Seq Data on the Cloud
title_full_unstemmed SingleCAnalyzer: Interactive Analysis of Single Cell RNA-Seq Data on the Cloud
title_short SingleCAnalyzer: Interactive Analysis of Single Cell RNA-Seq Data on the Cloud
title_sort singlecanalyzer: interactive analysis of single cell rna-seq data on the cloud
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9580930/
https://www.ncbi.nlm.nih.gov/pubmed/36304292
http://dx.doi.org/10.3389/fbinf.2022.793309
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