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

By Swati Parekh, C. Ziegenhain, B. Vieth, Wolfgang Enard, Ines Hellmann. bioRxiv, 2017

352 citations recorded by the source corpus.

Single cell RNA-seq (scRNA-seq) experiments typically analyze hundreds or thousands of cells after amplification of the cDNA. The high throughput is made possible by the early introduction of sample-specific barcodes (BCs) and the amplification bias is alleviated by unique molecular identifiers (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. Availability https://github.com/sdparekh/zUMIs

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/c0a1156fdf36cd35576fab09cba24124ade4c4a2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

  1. Rickard Sandberg
  2. Daniel Ramsköld
  3. Sarah A. Teichmann
  4. John C. Marioni
  5. Aviv Regev
  6. Chen Xu
  7. Zhengchang Su
  8. Alexander van Oudenaarden

Citations in the corpus