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SPOT: a web-tool enabling swift profiling of transcriptomes

The increasing number of single cell and bulk RNAseq datasets describing complex gene expression profiles in different organisms, organs or cell types calls for an intuitive tool allowing rapid comparative analysis. Here, we present Swift Profiling Of Transcriptomes (SPOT) as a web tool that allows...

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Bibliographic Details
Main Authors: Farr, Elias (Author) , Sattler, Julia M. (Author) , Frischknecht, Friedrich (Author)
Format: Article (Journal)
Language:English
Published: 1 January 2022
In: Bioinformatics
Year: 2022, Volume: 38, Issue: 1, Pages: 284-285
ISSN:1367-4811
DOI:10.1093/bioinformatics/btab541
Online Access:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1093/bioinformatics/btab541
Verlag, kostenfrei, Volltext: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8406885
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Author Notes:Elias B. Farr, Julia M. Sattler, Friedrich Frischknecht

MARC

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520 |a The increasing number of single cell and bulk RNAseq datasets describing complex gene expression profiles in different organisms, organs or cell types calls for an intuitive tool allowing rapid comparative analysis. Here, we present Swift Profiling Of Transcriptomes (SPOT) as a web tool that allows not only differential expression analysis but also fast ranking of genes fitting transcription profiles of interest. Based on a heuristic approach the spot algorithm ranks the genes according to their proximity to the user-defined gene expression profile of interest. The best hits are visualized as a table, bar chart or dot plot and can be exported as an Excel file. While the tool is generally applicable, we tested it on RNAseq data from malaria parasites that undergo multiple stage transformations during their complex life cycle as well as on data from multiple human organs during development and cell lines infected by SARS-CoV-2. SPOT should enable non-bioinformaticians to easily analyse their own and any available dataset.SPOT is freely available for (academic) use at: https://frischknechtlab.shinyapps.io/SPOT/ and https://github.com/EliasFarr/SPOT.Supplementary data are available at Bioinformatics online. 
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