Supporting data for "zUMIs - A fast and flexible pipeline to process RNA sequencing data with UMIs"

Dataset type: Software, Workflow, Transcriptomic
Data released on May 14, 2018

Parekh S; Ziegenhain C; Vieth B; Enard W; Hellmann I (2018): Supporting data for "zUMIs - A fast and flexible pipeline to process RNA sequencing data with UMIs" GigaScience Database. http://dx.doi.org/10.5524/100447

DOI10.5524/100447

Single cell RNA-seq (scRNA-seq) experiments typically analyze hundreds or thousands of cells after amplication of the cDNA. The high throughput is made possible by the early introduction of sample-specific barcodes (BCs) and the amplication bias is alleviated by unique molecular identiers (UMIs). Thus the ideal analysis pipeline for scRNA-seq data needs to efficiently tabulate reads according to both BC and UMI. zUMIs is such a pipeline, it can handle both known and random BCs and also efficiently collapses UMIs, either just for Exon mapping reads or for both Exon and Intron mapping reads. Another unique feature of zUMIs is the adaptive downsampling function, that facilitates dealing with hugely varying library sizes, but also allows to evaluate whether the library has been sequenced to saturation. zUMIs flexibility allows to accommodate data generated with any of the major scRNA-seq protocols that use BCs and UMIs. To illustrate the utility of zUMIs, we analysed a single-nucleus RNA-seq dataset and show that more than 35% of all reads map to Introns. We furthermore show that these intronic reads are informative about expression levels, significantly increasing the number of detected genes and improving the cluster resolution.

Additional details

Read the peer-reviewed publication(s):

Parekh, S., Ziegenhain, C., Vieth, B., Enard, W., & Hellmann, I. (2018). zUMIs - A fast and flexible pipeline to process RNA sequencing data with UMIs. GigaScience, 7(6). doi:10.1093/gigascience/giy059

Additional information:

https://github.com/sdparekh/zUMIs

https://scicrunch.org/resolver/RRID:SCR_016139





File NameSample IDData TypeFile FormatSizeRelease Date 
ReadmeTEXT1.71 KB2018-04-30
Mixed archiveTAR67 GB2018-05-15
mixed archivearchive198.31 MB2018-04-30
Displaying 1-3 of 3 File(s).
Funding body Awardee Award ID Comments
Deutsche Forschungsgemeinschaft I Hellmann SFB1243-A15
Deutsche Forschungsgemeinschaft W Enard SFB1243-A14
Date Action
May 14, 2018 Dataset publish
July 9, 2018 Manuscript Link added : 10.1093/gigascience/giy059